Forem: Evan-dong

DeepSeek V4 Flash vs Pro: How to Choose the Right Route for Your Coding Stack

Evan-dong — Sat, 25 Apr 2026 12:44:28 +0000

If your team is evaluating DeepSeek V4 right now, the most useful question is not "should we use it?" — it's "which tier, and for which workloads?"

As of April 24, 2026, DeepSeek's API now officially lists deepseek-v4-flash and deepseek-v4-pro with published pricing, 1M context, and 384K max output. Reuters separately confirmed the preview launch on the same date. The model is usable now, but preview status means you should still treat behavior as subject to change.

This guide is for engineering leads and platform teams who need to make a concrete routing decision — not a launch recap.

Who this is for

Platform teams migrating away from deepseek-chat and deepseek-reasoner before the July 24, 2026 deprecation
Engineering leads deciding where Flash fits vs. where Pro earns its cost
Teams trying to lower coding-model spend without replacing their premium fallback routes

Flash vs Pro: the one-paragraph decision

Flash (deepseek-v4-flash): $0.14 input / $0.28 output per 1M tokens. Use this as your default route for code generation, repo reading, summarization, and agent loops where throughput matters. The compatibility aliases (deepseek-chat, deepseek-reasoner) map to Flash behavior on deprecation, so it's also the lowest-risk migration target.

Pro (deepseek-v4-pro): $1.74 input / $3.48 output per 1M tokens. Use this as your escalation route for harder reasoning, multi-step analysis, and coding tasks where Flash doesn't clear your quality bar.

The mental model that works best in production: Flash = default, Pro = escalation. Don't flip everything to Pro by default.

Real cost shape by workload

These are rough estimates using official public pricing to show the cost difference at scale — not guaranteed production numbers.

Scenario 1: Repository analysis (250K input / 20K output)

Model	Estimated cost
DeepSeek V4 Flash	~$0.05
DeepSeek V4 Pro	~$0.51
GPT-5.4	~$0.93
Claude Opus 4.7	~$1.75

Flash is the obvious first test for codebase reading, dependency audits, and repo summarization.

Scenario 2: Multi-turn coding agent (120K input / 80K output)

Model	Estimated cost
DeepSeek V4 Flash	~$0.04
DeepSeek V4 Pro	~$0.49
GPT-5.4	~$1.50
Claude Opus 4.7	~$2.60

Output-heavy workloads punish expensive output pricing hard. This is where Flash's $0.28/M output rate matters most.

Scenario 3: Long document review (400K input / 25K output)

DeepSeek still holds a major cost advantage here. GPT-5.4 also documents a long-context premium rule (2x input / 1.5x output) for prompts above 272K tokens, which can change the economics significantly for large-context sessions.

Migration checklist: from deepseek-chat / deepseek-reasoner

DeepSeek's official docs confirm both legacy names are deprecated on July 24, 2026 and map to Flash compatibility behavior. Here's a practical migration path:

Inventory every current reference to deepseek-chat and deepseek-reasoner in your codebase
Test Flash first — because the compatibility aliases map to Flash, it's the lowest-risk first step
Promote only specific workloads to Pro — give Pro a narrow job (difficult coding, deeper analysis) before expanding its scope
Keep rollback routes active — preview means you should be able to revert quickly if quality, latency, or schema behavior changes

Where DeepSeek V4 has real limits

Preview status still matters. Reuters explicitly describes the release as a preview. Behavior can still change before finalization.

You still need your own eval set. No benchmark page tells you whether a model handles your specific codebase, your prompts, your failure patterns, and your latency budget — especially for agent loops, diff quality, and schema reliability.

Premium closed models still win on some tasks. Claude Opus 4.7 and GPT-5.4 are not going away for:

Highest-risk code changes
Hardest agentic tasks
Enterprise workflows where failure costs are high

When to keep Claude Opus 4.7 or GPT-5.4

Keep Claude Opus 4.7 if your team handles the hardest coding and review tasks and agent reliability matters more than token cost. Anthropic confirmed Opus 4.7 is generally available at $5/M input, $25/M output — same as Opus 4.6.

Keep GPT-5.4 if your team is already deeply invested in the OpenAI platform and your workflow depends on surrounding tooling as much as the model itself.

The stack that works for most teams

DeepSeek V4 Flash  →  default routing (code gen, repo reading, agent loops)
DeepSeek V4 Pro    →  escalation (harder reasoning, complex coding tasks)
Claude Opus 4.7    →  premium fallback (highest-stakes work)
GPT-5.4            →  premium fallback (OpenAI platform-dependent work)

This is usually better than trying to crown one universal winner.

Production rollout checklist

Define 20–50 real tasks from your own workload
Separate simple default-route tasks from premium-route tasks
Benchmark Flash and Pro independently
Compare output quality, not just benchmark headlines
Measure cost per successful task, not just cost per token
Keep rollback routes for GPT-5.4 or Claude Opus 4.7
Version prompts and evaluation harnesses
Log tool-call failures and schema failures separately
Watch latency and retry patterns during preview
Decide in advance what counts as "good enough to promote"

Sources: DeepSeek API Docs, DeepSeek Pricing, Anthropic Claude Opus 4.7, OpenAI GPT-5.4, Reuters

Tags: #deepseek #api #llm #aiengineering #codingtoolss

DeepSeek-V4 Runs on Huawei Ascend Chips at 85% Utilization — Here's What That Means for AI Infrastructure and Pricing

Evan-dong — Fri, 24 Apr 2026 08:38:42 +0000

DeepSeek released V4 on April 24, 2026. The headline numbers are striking on their own: 1 million token context window, Agent capabilities rivaling Claude Opus 4.6 on non-reasoning tasks, and API pricing 90% cheaper than GPT-4 Turbo. But the real story is what's underneath — DeepSeek-V4 runs on Huawei Ascend chips with 85%+ utilization, proving that China's domestic AI hardware stack can now compete with, and potentially undercut, Western alternatives built on Nvidia GPUs.

This isn't just a model release. It's a strategic signal about the future of AI infrastructure.

The Huawei Ascend Partnership: From "Usable" to "Competitive"

DeepSeek-V4 is the first Tier-1 large language model to achieve full inference compatibility with Huawei Ascend chips, with reported utilization rates exceeding 85%. For context, most domestic Chinese AI chips have struggled to hit 60% utilization on production inference workloads due to software stack immaturity and operator coverage gaps.

What changed to make 85% utilization possible:

1. Deep Hardware-Software Co-Optimization

DeepSeek worked directly with Huawei to optimize kernel implementations for Ascend 910B and Ascend 950 chips, focusing specifically on the operations that define V4's architecture:

MoE (Mixture of Experts) routing: The sparse activation pattern that lets V4 use only a fraction of its 1.6 trillion parameters per inference call
Sparse attention computation: The DSA mechanism that compresses attention at the token dimension
Memory-intensive operations: The Engram architecture's retrieval module that bridges CPU and GPU memory

2. Custom Operator Fusion for CANN Framework

Traditional Transformer operations were re-engineered to align with Huawei's CANN (Compute Architecture for Neural Networks) framework. Standard deep learning operators designed for CUDA had to be decomposed and reassembled to match Ascend's compute graph execution model. This eliminated memory bandwidth bottlenecks that previously capped utilization at ~60%.

3. Production-Scale Validation

DeepSeek's internal engineering teams have been running V4 on Ascend infrastructure for weeks before the public release. Their reported findings:

Inference quality matches Nvidia A100 deployments across standard benchmarks
Hardware costs reduced by approximately 40% compared to equivalent A100 clusters
Throughput scales linearly up to the cluster sizes tested

Why this matters for the broader AI industry:

Since the U.S. imposed high-end GPU export restrictions on China in October 2022, Chinese AI labs have been forced to choose between three options:

Stockpile pre-ban Nvidia chips — finite supply, increasingly expensive on secondary markets
Use older or smuggled GPUs — legal risk, limited performance ceiling
Wait for domestic chip alternatives to mature — capability gap, uncertain timeline

DeepSeek-V4 proves that option 3 is now viable at production scale. If a model can match Claude Opus 4.6 on non-reasoning tasks while running entirely on domestic Chinese hardware, the "you need Nvidia to compete in AI" narrative starts to crack.

The Pricing Bomb: V4-Flash at $0.014 Per Million Input Tokens

DeepSeek-V4 introduces tiered pricing across two model sizes, both with the full 1 million token context window:

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context Window
DeepSeek V4-Pro	$0.55	$2.19	1M tokens
DeepSeek V4-Flash	$0.014	$0.28	1M tokens

For comparison, here's what you'd pay with competing Western models:

Model	Input (per 1M tokens)	Output (per 1M tokens)	Context Window
GPT-4 Turbo (OpenAI)	$10.00	$30.00	128K tokens
Claude Opus 4.6 (Anthropic)	$15.00	$75.00	200K tokens
Gemini 3.1 Pro (Google)	$1.25	$5.00	2M tokens
DeepSeek V4-Flash	$0.014	$0.28	1M tokens

V4-Flash is 700x cheaper than GPT-4 Turbo on input tokens, and 100x cheaper on output tokens.

Even V4-Pro — the flagship model with Agent capabilities approaching Claude Opus 4.6 — costs $2.19 per million output tokens compared to Opus's $75. That's a 34x price difference for comparable non-reasoning performance.

What You Can Actually Build at These Prices

Scenario 1: Long-context document analysis

Process a 500-page legal contract (~200K tokens input, ~10K tokens output):

GPT-4 Turbo: $2.00 (input) + $0.30 (output) = $2.30 per document
DeepSeek V4-Pro: $0.11 (input) + $0.02 (output) = $0.13 per document
DeepSeek V4-Flash: $0.003 (input) + $0.003 (output) = $0.006 per document

At V4-Flash prices, you could analyze 383 legal contracts for the cost of analyzing one on GPT-4 Turbo.

Scenario 2: Agent-based coding assistant

Generate 50K tokens of code per day for a development team (1.5M output tokens/month):

Claude Opus 4.6: $112.50/month
DeepSeek V4-Pro: $3.29/month
DeepSeek V4-Flash: $0.42/month

Scenario 3: High-volume customer support chatbot

Serve 1 million user queries per month (average 1K input tokens + 500 output tokens per query):

GPT-4 Turbo: $10,000 (input) + $15,000 (output) = $25,000/month
Claude Opus 4.6: $15,000 (input) + $37,500 (output) = $52,500/month
DeepSeek V4-Flash: $14 (input) + $140 (output) = $154/month

At these price points, entire categories of AI applications — enterprise document processing, automated customer support, code generation pipelines, research summarization — become economically viable for small teams and individual developers who previously couldn't afford production-scale LLM deployments.

Technical Foundations: The Three Architectural Innovations Behind V4's Cost Structure

DeepSeek didn't just slash prices by running on cheaper hardware. V4 introduces three architectural innovations that fundamentally reduce the cost of inference at every level of the stack.

Innovation 1: Engram Architecture — Separating Memory from Computation

Traditional Transformer models store all learned knowledge in GPU memory through their parameter weights. This creates a direct coupling: longer context windows and larger knowledge bases require proportionally more expensive GPU memory.

V4's Engram architecture breaks this coupling by splitting the model into two distinct modules:

Static knowledge retrieval module: Stores factual knowledge, world knowledge, and learned patterns in cheap CPU RAM using a hash-based lookup mechanism. This module handles the "what does the model know" question.
Dynamic reasoning module: Runs on GPU and handles the "how should the model think about this specific query" question. It decides which memories to retrieve from the static module and integrates them into the inference chain.

The practical result: V4 can handle 1 million token context windows without proportional GPU memory growth. This is why DeepSeek can offer 1M context as the default for all API tiers — the marginal cost of extending context from 128K to 1M is minimal because the expensive GPU memory isn't what scales.

This is a fundamentally different approach from OpenAI's and Anthropic's architectures, which still couple knowledge storage and reasoning computation in the same GPU memory space.

Innovation 2: mHC (Manifold-Constrained Hyper-Connections) — Stable Deep Network Training

Training a 1.6 trillion parameter Mixture of Experts model is notoriously unstable. Gradients explode, training runs collapse, and teams waste weeks of compute on failed experiments. This instability is one of the hidden costs that inflates the price of frontier models.

V4 uses mHC (Manifold-Constrained Hyper-Connections) technology to solve this:

Layer connections are projected onto a bi-stochastic matrix manifold using the Sinkhorn-Knopp algorithm
This enforces a mathematical invariant: signal conservation — the sum of inputs equals the sum of outputs at every node in the network
The constraint prevents the "signal explosion" phenomenon that normally kills deep network training runs

The practical result: DeepSeek can train deeper, more parameter-efficient models without the trial-and-error waste that inflates training costs at other labs. Fewer failed training runs = lower amortized cost per inference = lower API prices.

Innovation 3: DSA (DeepSeek Sparse Attention) — Token-Level Compression

Standard attention mechanisms compute pairwise relationships between all tokens in the context window, creating O(n²) computational complexity. This is why long-context inference is expensive — doubling the context length quadruples the attention computation.

V4's DSA (DeepSeek Sparse Attention) compresses attention computation at the token dimension, not just the head dimension (which is what most prior sparse attention methods target). Combined with learned sparse attention patterns, this achieves:

Compute reduction from O(n²) to near-linear scaling
60-70% reduction in memory bandwidth requirements
1M token context inference on consumer-grade hardware (for the Flash tier)

The practical result: Lower inference compute per token → lower electricity and hardware costs per API call → lower API prices passed to developers.

The Geopolitical Subtext: A Deliberate Mirror Image

On April 23, 2026 — one day before V4's public release — Reuters reported that DeepSeek refused to grant early API access to U.S. chip manufacturers, including Nvidia. This mirrors the U.S. government's October 2022 ban on exporting high-end AI GPUs (A100, H100) to China.

The strategic sequence:

U.S. restricts chip exports to China → Chinese AI labs lose access to H100/A100 GPUs
DeepSeek builds V4 on Huawei Ascend → proves domestic Chinese chips can run Tier-1 models at production scale
DeepSeek restricts U.S. access to V4 API → signals technological parity and strategic independence

This isn't just about one model or one company. It's about ecosystem decoupling:

If Chinese labs can train and deploy competitive models on domestic hardware...
And Chinese cloud providers (Alibaba Cloud, Tencent Cloud, Huawei Cloud) offer these models at 1/100th the price of Western alternatives...
Then the global AI supply chain splits into two parallel technology stacks: one built on Nvidia/CUDA/AWS/OpenAI, one built on Ascend/CANN/Huawei Cloud/DeepSeek.

For developers and enterprises, this creates a new dimension of technology strategy that didn't exist 12 months ago.

What DeepSeek-V4 Means for Developers Outside China

Short-Term Impact (2026-2027)

Price pressure on Western AI providers: If DeepSeek can offer GPT-4-class models at $0.28/M output tokens, OpenAI and Anthropic will face margin compression. Expect aggressive price cuts or new "economy" model tiers from Western providers within 6 months.
Multi-model routing becomes standard architecture: Developers will route simple classification, extraction, and summarization tasks to V4-Flash ($0.28/M) while reserving complex reasoning, safety-critical, and creative tasks for Claude Opus 4.6 ($75/M) or GPT-4 Turbo ($30/M). The cost difference makes single-model architectures economically irrational.
Geopolitical compliance becomes a development concern: U.S. developers may face restrictions on using Chinese AI APIs, similar to TikTok-related concerns. Enterprise compliance teams will need to audit model provenance and data routing.

Long-Term Impact (2028+)

Two parallel AI ecosystems: Western stack (Nvidia + OpenAI/Anthropic/Google) vs. Chinese stack (Ascend + DeepSeek/Alibaba/Baidu). Developers building for global markets may need to maintain dual implementations.
Commoditization of intelligence: If 1M-context models cost $0.28/M tokens, AI becomes infrastructure — like cloud storage, CDN bandwidth, or database queries. The competitive moat shifts from "access to intelligence" to "what you build with intelligence."
Open-source ecosystem fragmentation: DeepSeek releases model weights, but they're optimized for Ascend chips. Western researchers may struggle to replicate results on Nvidia hardware without significant re-optimization, fragmenting the open-source AI community along hardware lines.

How to Access DeepSeek-V4: API Reference and Quick Start

REST API

curl https://api.deepseek.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum entanglement in simple terms"}
    ],
    "max_tokens": 1000,
    "temperature": 0.7
  }'

Model Options

deepseek-v4-pro — Flagship model, optimized for Agent workflows and complex multi-step tasks
deepseek-v4-flash — Faster inference, lower cost, retains 98% of Pro's reasoning ability

Reasoning Mode for Complex Agent Tasks

{
  "model": "deepseek-v4-pro",
  "reasoning_mode": true,
  "reasoning_effort": "max",
  "messages": [
    {"role": "user", "content": "Design a microservices architecture for a real-time bidding system"}
  ]
}

Reasoning mode activates chain-of-thought inference similar to Claude Opus 4.6's extended thinking mode. Use reasoning_effort: "max" for complex architectural decisions, code generation, and multi-step problem solving.

Open-Source Model Weights

Hugging Face: huggingface.co/deepseek-ai/DeepSeek-V4-Pro
ModelScope (China): modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro

Quick Start

Try DeepSeek-V4 directly: DeepSeek Chat on EvoLink

The Bigger Picture: Post-Scaling Law AI

DeepSeek-V4 represents a paradigm shift from brute-force scaling to architectural efficiency:

Old paradigm: More parameters + more training data + more compute = better models. This is the approach that drove GPT-3 → GPT-4 improvements.
New paradigm: Smarter architectures (Engram) + memory-compute separation + sparse attention (DSA) + training stability (mHC) = cheaper, more capable models on diverse hardware.

This matters because:

Scaling returns are diminishing: The improvement from GPT-4 to GPT-5 is marginal compared to GPT-3 to GPT-4. The low-hanging fruit of pure scale is gone.
Efficiency becomes the competitive moat: If you can deliver GPT-4-class intelligence at 1/100th the cost, you don't need to be 10x smarter — you just need to be 10x cheaper. DeepSeek is betting on this strategy.
Hardware diversity wins: When models are optimized for architectural efficiency rather than raw compute, they can run on diverse hardware platforms — Huawei Ascend, AMD Instinct, Intel Gaudi, even mobile chips. Nvidia's GPU monopoly weakens as the industry moves from "more FLOPS" to "smarter FLOPS."

DeepSeek-V4 is the first major model to prove this thesis at production scale.

Final Thoughts

The question DeepSeek-V4 poses isn't "is it better than Claude or GPT-4 on benchmark X?" The question is: what happens to the AI industry when intelligence costs $0.28 per million tokens?

We're about to find out.

Resources:

Disclosure: This analysis is based on publicly available information and technical documentation. The author has no financial relationship with DeepSeek, Huawei, or competing AI providers.

GPT Image 2 + Seedance 2.0: A Practical Workflow from Static Visuals to Publishable Shorts

Evan-dong — Thu, 23 Apr 2026 08:17:13 +0000

If you've been working with AI visuals lately, you've probably felt a clear shift: image generation and video generation are no longer two disconnected steps. They're becoming a reusable production pipeline.

The core idea is simple: use GPT Image 2 to design the visuals correctly first, then use Seedance 2.0 to turn those visuals into motion, rhythm, atmosphere, and sound.

Why this division of labor works

A lot of people start by throwing a single text-to-video prompt at a model and hoping the result will feel cinematic. Sometimes the video moves, but the storytelling collapses. Sometimes the cuts are interesting, but the character design drifts.

The more reliable approach is to divide the work properly:

GPT Image 2 handles pre-production visual design: character sheets, storyboard grids, comic pages, posters, title cards, key art
Seedance 2.0 handles motion and audiovisual execution: camera movement, shot progression, sound atmosphere, final video feel

When you first lock the character, framing, and visual order with GPT Image 2, then pass the result into Seedance 2.0, you're breaking one difficult task into two more manageable ones.

Workflow 1: Storyboard grid → 15-second trailer

Generate a 3×3 storyboard grid with GPT Image 2 where each panel represents a shot, then use that image as the starting frame for Seedance 2.0 and guide the sequence with a shot-by-shot motion prompt.

This works because:

Pacing is naturally controlled — each panel already corresponds to a defined beat
Character and style consistency are stronger — all nine shots are generated inside one unified image
Seedance 2.0 is far more likely to interpret the input as a multi-shot sequence

Workflow 2: Comic page or character sheet → animated short

Treat GPT Image 2 outputs — comic pages, character sheets, narrative design boards — as visual scripts, then use Seedance 2.0 to animate them.

The condition is simple: the input image must not only be beautiful; it must be usable as shot design.

The practical sequence

Step 1: Write shot intent before you write prompts

Before generating anything, write a short shot list. Even for a 15-second piece, define the opening beat, middle beat, escalation, and ending hold.

Step 2: Generate the storyboard or character sheet with GPT Image 2

Use a structured prompt that specifies panel count, shot types, and visual style. The goal is not a pretty image — it's a usable production asset.

Step 3: Pass the image into Seedance 2.0 with a motion prompt

Reference specific panels in your motion prompt. Describe camera movement, pacing, and transitions explicitly.

Step 4: Iterate on the motion prompt, not the image

If the video doesn't feel right, adjust the motion prompt first. Only regenerate the source image if the visual design itself is the problem.

Prompt resources

For ready-to-use GPT Image 2 prompts covering storyboard grids, character sheets, comic pages, and more:

EvoLinkAI/awesome-gpt-image-2-prompts

The repo includes prompts organized by use case, with notes on what works well for downstream video generation.

The most reliable path for AI trailers, animated teasers, and story-driven shorts: design the image first, then generate the video.

Google Deep Research Is No Longer a Chatbot Feature — It's a Research Platform

Evan-dong — Wed, 22 Apr 2026 11:58:59 +0000

Google's latest Deep Research upgrade is worth paying attention to, and not just because it's faster or smarter.

What changed is the product's positioning. Google is no longer presenting Deep Research as a chatbot feature that helps you look things up. With the Gemini 3.1 Pro upgrade, Deep Research Max, MCP support, multimodal grounding, and enterprise data integration, it's being positioned as a research workflow platform.

That's a meaningful distinction.

What Actually Changed

Collaborative planning: Before execution, users can now review and edit the system's research plan. This is significant — it shifts the model from "AI produces output" to "human directs workflow, AI executes."

Multi-tool support in one run: Google Search, remote MCP servers, URL Context, Code Execution, and File Search can all operate within the same research workflow.

Private data grounding: Web access can be turned off entirely, enabling research runs grounded only in internal documents. This is the enterprise unlock.

Multimodal inputs: PDFs, CSVs, images, audio, and video alongside text. Real-world research doesn't live in clean prose — product teams have slide decks, investors have filings and transcripts, operations teams have dashboards and exports.

Native visualizations: Charts and infographics generated inline. A report with structured visualizations is a business artifact that circulates internally and presents to stakeholders. That changes the product's role.

The Programmatic Layer

For developers, the interesting detail: Deep Research and Deep Research Max are available in public preview through paid tiers in the Gemini API. That opens the door for teams to build custom research products — not use Deep Research as a fixed UI, but embed its agentic capabilities into domain-specific workflows.

Specialized research applications for healthcare, legal analysis, competitive intelligence, and technical discovery become buildable primitives.

The Strategic Signal

Google's subscription positioning is telling: Deep Research sits alongside large file uploads and workflows for turning source material into blog posts, web pages, and content. The message is "productivity stack for turning information into output," not "better search."

For organizations, AI stops being an assistant and starts becoming a force multiplier for analysts, researchers, and strategy teams — when it can scan hundreds of sources, compare competing claims, synthesize against internal documents, and package the result into a usable report.

The Caveats

More capable research tooling doesn't eliminate the need for judgment. A system that produces polished, stakeholder-ready reports makes human review more important, not less. The competitive advantage won't come from using the tool. It'll come from building the review processes, source standards, and editorial discipline around it.

For unified API access to Google, OpenAI, Anthropic and 30+ models: EvoLink

Claude Design: This Is Not Another AI Image Generator

Evan-dong — Mon, 20 Apr 2026 12:15:49 +0000

Anthropic just launched Claude Design, and the reaction was immediate — both from the community and from financial markets, where shares of Adobe and Figma came under pressure within hours of the announcement.

That market reaction may be premature. But it points to something real.

What Claude Design actually is

Claude Design is not an image generator. It is not a Midjourney competitor. It is an attempt to rethink what design software becomes when the primary interface is natural language instead of a toolbar.

According to Anthropic's positioning, the product can generate:

Editable design drafts
Interactive prototypes
Presentation decks
Single-page documents

The critical distinction: it doesn't produce static outputs you admire and export. It produces design artifacts that can participate in a workflow — things teams can iterate on, comment on, and eventually ship.

Currently in research preview, rolling out to Claude Pro, Max, Team, and Enterprise users.

The shift from GUI to LUI

The most important idea behind Claude Design is the move from GUI to LUI — language user interface.

Instead of building from panels, layers, and precision tools, you describe what you want. Claude generates a first version. You refine through follow-up prompts, leave comments on specific elements, edit text directly, and adjust spacing and layout through generated controls.

Traditional design software assumes expertise is expressed through tool mastery — shortcuts, component libraries, spacing logic, handoff conventions. Claude Design suggests a different premise: for a large class of tasks, the bottleneck is no longer software fluency. It's the ability to articulate intent clearly.

The brand adaptation feature is the strategic core

One of the strongest ideas in the product is how it handles design systems.

During setup, Claude can reportedly read a team's codebase and design files, then infer and construct a design system covering colors, fonts, and component rules — reusable across future projects.

AI-generated design is far more valuable when it's brand-aware and structurally aligned with how teams already build. Generic outputs get ignored. Opinionated outputs get used.

Who this actually disrupts

Claude Design's real wedge may not be professional designers at all.

It's the product manager who needs a UI mockup but doesn't know Figma. The founder who needs a fundraising deck but doesn't want to hire an agency. The marketer who needs creative output without waiting in a design queue.

That user base is much larger than the traditional design industry. The threat isn't "stealing Figma's power users." It's redrawing the boundary of who can produce acceptable design work at all.

Export and integration

Finished work exports to Canva, PDF, PPTX, or standalone HTML, and can be packaged into Claude Code for implementation. More integrations reportedly coming.

For enterprise users: the feature is disabled by default and must be enabled by an admin — a signal that Anthropic is already thinking about governance.

For more context on the Claude ecosystem and unified API access:
EvoLink

GPT Image 2: Text That Actually Works, and Why It Changes Everything for Builders

Evan-dong — Sun, 19 Apr 2026 12:21:03 +0000

For years, AI image generation had one obvious tell: the text inside images was almost always wrong. Misspelled labels, broken characters, nonsensical typography. You could generate a beautiful composition and still get a sign that said "COFEFE" when you asked for "COFFEE."

That limitation quietly kept AI image generation out of a huge class of real workflows. If you couldn't trust the text, you couldn't use the output for social graphics, product packaging concepts, UI mockups, or anything where the words actually matter.

GPT Image 2 appears to be changing this. Based on community testing, A/B comparisons in ChatGPT, and developer reports from API metadata — though not yet officially announced by OpenAI — the next-generation model shows a dramatic improvement in text rendering accuracy.

What's Actually Different

Text rendering that holds up

Community testing shows multi-word labels, interface copy, signage, and packaging text rendering accurately. This isn't just "slightly better" — it's the difference between an output you can use and one you have to manually fix.

UI and interface generation

Leaked outputs show browser windows, mobile app screens, dashboards, and product pages that are coherent enough to communicate a product concept or UX direction. Not pixel-perfect recreations, but genuinely usable for pitches, prototypes, and documentation.

Photorealism in the small details

Better faces and hands, fewer visual artifacts, cleaner textures. The improvements aren't purely benchmark-level — they show up in everyday outputs.

What This Unlocks for Builders

Once text in images becomes reliable, whole categories of work open up:

Marketing graphics with accurate in-image copy, no manual cleanup
Product mockups with readable labels and packaging text
UI previews for ideation and internal review before engineering builds anything
Illustrated documentation where diagrams actually say the right things
Automated content pipelines where text inside the image is part of the payload

A solo founder can now communicate product ideas visually. A newsletter writer can create custom graphics without hiring a designer. A product team can iterate on visual directions earlier and more often.

The Darker Side

Better text rendering also means more convincing fake screenshots. Realistic banking interfaces, fake SaaS pricing pages, fabricated product screens — these become easier to produce. The informal trust we've placed in screenshots as evidence needs to be retired.

Any environment that casually treats screenshots as proof — journalism, compliance, customer support investigations — will need to raise its standards.

Status

"GPT Image 2" is currently a community label inferred from testing, not an official OpenAI product announcement. The pattern is credible — OpenAI has a long history of A/B testing capabilities in ChatGPT before broader rollout. If it follows the usual pattern, wider availability comes first in ChatGPT, then API access.

For high-quality prompts, examples, and use cases, the community has been collecting them here:
awesome-gpt-image-2-prompts

Claude Opus 4.7 vs 4.6: What Actually Changed and What Breaks on Migration

Evan-dong — Sat, 18 Apr 2026 08:48:03 +0000

Anthropic just released Claude Opus 4.7 and positioned it as the direct upgrade to Opus 4.6. Same headline pricing, same context window. But "same price" doesn't mean "drop-in replacement" — and the migration guide confirms several breaking changes that will catch teams off guard.

Here's what actually changed and what you need to fix before switching.

Quick Comparison

Area	Opus 4.6	Opus 4.7
Model ID	`claude-opus-4-6`	`claude-opus-4-7`
Pricing	$5/$25 per MTok	$5/$25 per MTok
Context	1M tokens	1M tokens
Thinking	Adaptive + legacy extended	Adaptive only
Sampling	temperature/top_p/top_k work	Non-default values return `400`
Thinking display	Visible by default	Omitted unless opted in
Tokenizer	Prior	Updated (1.0x–1.35x more tokens)

The Breaking Changes

1. Extended thinking payloads break

Old budget_tokens-style reasoning payloads return a 400 error on Opus 4.7. Migrate to:

thinking={"type": "adaptive", "effort": "high"}

2. Custom sampling parameters are gone

If your prompts use temperature=0, top_p, or top_k, those now return 400. Remove them and use prompt-based alternatives for deterministic behavior.

3. Thinking text is hidden by default

Opus 4.7 still reasons, but the visible chain-of-thought is omitted unless you explicitly request it:

thinking={"type": "adaptive", "effort": "high", "display": "summarized"}

If your app streams visible reasoning to users, this is a UX regression you need to opt back into.

4. Token costs can still rise

Same list price, but the updated tokenizer maps the same input to roughly 1.0x–1.35x more tokens depending on content type. Measure token deltas on your actual workload before assuming the bill stays flat.

What Anthropic Actually Improved

Opus 4.7 is positioned as a coding and agent model first:

Stronger advanced software engineering
Better handling of complex, long-running tasks
More precise instruction following
Better self-verification before reporting results
Substantially better vision and image understanding

The customer quotes Anthropic highlighted are almost all about coding reliability, tool use, and agent workflows — not general chat quality.

Migration Strategy

Migrate first if your workload is:

Multi-step coding
Code review
Tool-using agents
Long-running debugging loops

Wait if your app depends on:

Old reasoning payloads
Visible thinking traces
Strict token ceilings
Custom sampling values

Safest rollout:

Swap a small % of coding traffic to claude-opus-4-7
Re-run your eval set on bug fixing and long-horizon tasks
Measure token deltas, not just win rate
Retune effort, max_tokens, and compaction thresholds
Promote only after checking both quality and cost per task

Production Routing

If you're managing multiple Claude versions (or want to keep Opus 4.6 as fallback while testing 4.7), a unified API gateway like EvoLink lets you route between models with one parameter change — no code rewrites per provider.

Last verified: April 16, 2026. Sources: Anthropic announcement, Claude API migration guide, official pricing page.

Opus 4.6 Hallucination Rate Hit 33% — Here's What Changed and How to Fix It

Evan-dong — Tue, 14 Apr 2026 09:27:50 +0000

If your Claude Code sessions have been producing more errors, skipping files, or fabricating APIs that don't exist — you're not imagining it.

Over the past two weeks, developers across GitHub, X, and YouTube have reported a measurable decline in Opus 4.6's coding quality. Independent benchmarks now confirm it: the model's hallucination rate has nearly doubled.

This post covers the evidence, the root cause, and the exact settings to fix it.

The Data

BridgeBench Hallucination Benchmark

BridgeBench measures how often AI models fabricate false claims when analyzing code — 30 tasks, 175 questions, verified against ground truth.

Opus 4.6's trajectory:

Previous: #2 with 83.3% accuracy (~17% fabrication)
Current: #10 with 68.3% accuracy (33% fabrication)

One in three responses now contains fabricated information.

Current leaderboard (April 14, 2026):

Model	Accuracy	Fabrication Rate	Rank
Grok 4.20 Reasoning	91.8%	10.0%	#1
GPT-5.4	86.1%	16.7%	#2
Claude Opus 4.5	72.3%	27.9%	#6
Claude Sonnet 4.6	72.4%	28.9%	#7
Claude Opus 4.6	68.3%	33.0%	#10

Notable: Sonnet 4.6 (smaller, cheaper) outperforms Opus 4.6 on accuracy.

Developer Testing

@om_patel5 ran the same prompt on Opus 4.6 and 4.5:

4.6: failed 5 consecutive windows
4.5: passed every time

His tweet got 682K views and 1,118 bookmarks. He now runs this as a "quantization canary" before every session.

6,852-Session Analysis

An AMD executive analyzed 6,852 Claude Code sessions and measured a 67% drop in reasoning depth compared to pre-February behavior.

Root Cause: Two Default Changes

Anthropic made two changes in early 2026:

1. Effort level default: high → medium (March 3, 2026)

The model now "conserves thinking" by default. Complex problems that need deep reasoning get classified as "simple enough" and receive shallow analysis.

2. Adaptive thinking introduced (February 9, 2026)

The model dynamically allocates reasoning tokens per turn. Under medium effort, some turns receive zero reasoning tokens — the model answers without thinking at all.

These two changes compound: the model skips thinking precisely when you need it most.

The Fix

Quick fix (per session)

/effort max

Permanent fix (environment variables)

export CLAUDE_CODE_EFFORT_LEVEL=max
export CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1

Add these to your .bashrc or .zshrc.

Nuclear option: switch to Opus 4.5

Set model to claude-opus-4-5-20251101. Slower and more expensive, but consistently reliable.

Quick Reference

Problem	Fix	Command
Session feels dumb	Max effort	`/effort max`
Resets every session	Env var	`CLAUDE_CODE_EFFORT_LEVEL=max`
Zero-reasoning turns	Disable adaptive	`CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING=1`
Still unreliable	Use Opus 4.5	Model: `claude-opus-4-5-20251101`

Model Switching in Production

If you're calling Claude via API in production, switching models means changing endpoints, auth, and billing for each provider. A unified API gateway like EvoLink lets you swap between 30+ models by changing one parameter. The Smart Router (evolink/auto) can automatically route deep-reasoning tasks to more reliable models.

Sources:

BridgeBench Hallucination Benchmark — bridgebench.ai/hallucination
@om_patel5 on X (Apr 10, 2026, 682K+ views)
GitHub Issue #42796 — github.com/anthropics/claude-code/issues/42796
Digit.in — AMD executive's 6,852-session analysis
pasqualepillitteri.it — effort/adaptive thinking configuration guide

Midjourney V7 in 2026: What Actually Changed for Builders?

Evan-dong — Mon, 13 Apr 2026 12:18:11 +0000

I spent time revisiting Midjourney V7 from a builder's point of view, and the conclusion is more specific than "the images look good."

They do look good. That is not the interesting part.

The more useful question is whether V7 changes the way a product team, creative tooling team, or AI workflow builder should think about Midjourney in 2026. My short answer: yes, but only if you understand what V7 is good at and where it still does not behave like a deterministic design API.

The short version

Midjourney V7 is still worth using when the job is taste-driven image generation:

campaign concept exploration
hero visuals
moodboards
stylized product shots
editorial or cinematic visual directions
brand-adjacent creative systems

It is less ideal when the job is exact typography, rigid design-system layout, or tiny deterministic edits where one label must change and nothing else can move.

That distinction matters because many teams evaluate image models with one vague question: "Which model is best?" For Midjourney V7, a better question is:

Do I need visual taste, or do I need pixel-level obedience?

V7 is strongest in the first case.

What changed from V6 to V7?

Midjourney says V7 was released on April 3, 2025 and became the default model on June 17, 2025. The important practical changes are:

better text and image prompt precision
richer textures and more coherent detail
Draft Mode for fast exploration
Omni Reference for stronger reference-guided generation
a more useful personalization and style workflow

For teams building around an image model, those are not cosmetic upgrades. They affect how many prompts you run, how you explore visual directions, and how much manual review you need before selecting a final image.

V7 vs V6: not just "better images"

The biggest difference is workflow shape.

V6 could already produce excellent images. V7 makes it easier to treat Midjourney as a repeatable creative system rather than a one-off image generator.

Area	V6	V7
Prompt handling	Strong, often parameter-heavy	Cleaner prompt-to-result behavior
Draft exploration	Not the headline feature	Core part of the workflow
References	Useful style workflows	Stronger Omni Reference and personalization
Team workflow	More manual iteration	Easier to standardize around repeatable directions
Editing	Legacy edit behavior remains important	Some edit surfaces still require careful auditing

That last row is important. V7 is a better default, but it does not magically turn Midjourney into a fully deterministic design editor.

Draft Mode is the operational upgrade

Draft Mode is the feature I would pay the most attention to. Official Midjourney documentation describes it as roughly 10x faster and about half the GPU cost of standard generation.

That changes the economics of ideation:

Generate many rough directions cheaply.
Keep only the promising compositions.
Promote winners to higher-quality output.
Spend expensive generation only where quality matters.

For creative teams, that mirrors how visual work already happens. Most of the work is exploration. Only a few outputs become final assets.

If you are building an app or internal workflow around image generation, Draft Mode suggests a useful product pattern:

use Draft for option generation
let users shortlist
run final-quality generation only after selection
store task IDs and references for follow-up edits

That is a better experience than making every prompt expensive by default.

A practical V7 pipeline for builders

If I were adding Midjourney V7 to a product today, I would not expose it as a single "generate image" button and call it done.

I would design the flow around the fact that Midjourney is best at creative search:

Collect intent

Ask the user for the goal, not only the prompt. A hero image, a product moodboard, and a cinematic concept frame should not use the same defaults.

Generate draft directions

Run several Draft Mode generations with different framing, aspect ratio, and style assumptions. This is where V7's speed/cost profile matters.

Show candidates as directions

Present early outputs as options, not final assets. The UI copy matters here. Users should feel they are choosing a direction, not judging a finished render.

Promote only the winners

When one direction is close, enhance or regenerate at higher quality. This keeps full-quality generation tied to user selection.

Persist references

Store prompt text, selected outputs, task IDs, reference images, style parameters, and rejected candidates. The rejected candidates are useful too because they tell your system what not to repeat.

Route follow-up edits deliberately

If the edit is visual and loose, keep it in the Midjourney-style workflow. If the edit is exact text, layout, or object-level preservation, route it to a different image-editing path.

This is the main mental shift. V7 should not be treated as a single endpoint. It is better as a stage in a creative decision loop.

Minimal backend shape

The backend does not need to be complicated, but it should be explicit.

At minimum, I would track something like:

{
  "job_id": "img_123",
  "model": "midjourney-v7",
  "mode": "draft",
  "prompt": "editorial product photo, soft studio light...",
  "status": "running",
  "reference_assets": ["ref_01.png"],
  "selected_candidate": null,
  "created_at": "2026-04-13T00:00:00Z"
}

Then move it through states:

queued
running
needs_review
selected
enhancing
completed
failed
moderated

This sounds boring, but this is where image products become reliable. The model can be creative. The system around it should be predictable.

Where V7 still needs caution

Midjourney V7 is not the right default for every production image task.

Exact text

If your output needs precise packaging copy, exact UI text, or reliable typography, be careful. V7 can create strong compositions, but composition quality is not the same as text fidelity.

Micro-edits

If your requirement is "change only this one object and preserve everything else exactly," you should test carefully before standardizing on V7. Some editing workflows are useful, but they are not the same as deterministic image editing.

Async production flow

Midjourney workflows are naturally async. That means your app needs to handle:

task creation
polling or callbacks
persistence
retries
moderation or failed outputs

This is not a blocker. It just belongs in the architecture from day one.

Decision checklist

Before making V7 your default image route, I would ask:

Does the workflow benefit from generating many options?
Can users tolerate selecting and refining candidates?
Is exact text optional or handled elsewhere?
Do we have a place to store task state and generated assets?
Can moderation or failed outputs be represented clearly in the UI?
Do we need style consistency across multiple generations?

If most answers are yes, V7 is probably a good fit.

If the core requirement is "produce the exact final asset in one synchronous request," I would be more cautious.

Who should use V7?

Use Midjourney V7 when your product or team cares about:

taste-first image generation
concept exploration
visual range
reusable style direction
high-quality creative outputs

Compare alternatives first when you need:

exact layout preservation
reliable text rendering
deterministic small edits
strict production templates

Final take

Midjourney V7 is not interesting because it is "new." It is interesting because it makes Midjourney easier to use as a creative workflow engine.

V7 is the better default than V6 for most new work, especially when Draft Mode and reference workflows matter. Just do not evaluate it like a traditional deterministic API. It is strongest when your system is designed around exploration, selection, and refinement.

I wrote the deeper review here: https://evolink.ai/blog/midjourney-v7-review-2026?utm_source=devto&utm_medium=community&utm_campaign=midjourney_v7_review&utm_content=devto

Hermes Agent Crossed 47K GitHub Stars in Two Months — What's Actually Going On?

Evan-dong — Sat, 11 Apr 2026 13:25:10 +0000

If you've been watching GitHub trending lately, you've probably noticed Hermes Agent. It crossed 22,000 stars within its first month after open-sourcing in late February, then added more than 6,400 stars in a single day after the v0.8.0 release on April 8. In under two months, it passed 47,000 stars and spent multiple days at the top of global trending charts.

That kind of growth usually signals one of two things: a project has hit a real developer nerve, or it's become a vehicle for a narrative bigger than the product itself. Hermes might be both — and that's worth unpacking for anyone building with AI agents.

What Hermes Agent actually does

Hermes is an open-source AI agent framework from Nous Research, MIT licensed. But it's not just another tool-use orchestration layer.

The core idea: the agent should grow with the user over time.

Hermes stores historical conversations in a local database, organizes them through retrieval and summarization, and tries to build a working model of how you operate — how you code, which tools you prefer, how you respond to errors. It's not just a searchable log. It's meant to be a persistent layer that accumulates knowledge across sessions.

On top of that, Hermes tries to turn completed tasks into reusable skills. After finishing a complex workflow, it can abstract the process into something like a playbook: steps, decision points, common failure modes, validation logic. When a similar task comes up later, it leans on that prior experience.

There's also an early self-training angle. Hermes can export tool-use traces from runtime, which can then be used as fine-tuning data. That pushes it beyond the "AI assistant" category and into something closer to a research system that treats usage itself as part of a model improvement loop.

Why developers are paying attention

One thing that keeps coming up in community testing: Hermes seems to reduce the amount of prompt babysitting required for complex work. Relatively vague instructions can still lead to surprisingly complete workflows. A request like "write a script that scrapes data and generates a visualization" doesn't always need heavily scaffolded prompting — Hermes can break the task down, generate code, inspect errors, adjust its path, and move toward a working solution.

That's not the same as solving autonomous software engineering. But it points to something developers care about more than flashy one-shot demos: whether an agent can keep moving forward under ambiguity.

Many agents look capable when the task is clean and the prompt is precise. Hermes is gaining traction because it gives people a glimpse of a different mode — an agent that can operate under incomplete instructions, recover from failed attempts, and compound experience over time.

The design bet: growth over control

Most agent frameworks still optimize for explicit control. You write the prompt, define the tools, hardcode the behavior. That's reliable and debuggable. But it also means the agent's capability ceiling is bounded by what you predefine.

Hermes bets on a different path. It assumes a useful long-term agent should accumulate capability through use. Memory isn't just a searchable log. Skills aren't only manually authored. Behavior shouldn't stay static if the system has enough evidence to improve.

That's more ambitious — and introduces more uncertainty. Systems that learn over time can become more powerful, but also noisier, less predictable, harder to evaluate.

Recent updates make this ambition clearer. Hermes now supports multi-instance configurations (multiple isolated agents in the same environment, each with its own memory and skills) and MCP integration, letting conversations and memory surface directly inside tools like Claude Desktop, Cursor, or VS Code. It's starting to blur the line between a background agent and the development environment itself.

Hermes vs. OpenClaw: same destination, different philosophy

As Hermes took off, comparisons with OpenClaw became inevitable. Both respond to the same frustration with hosted AI: too little privacy, too little control, too much dependency on centralized platforms.

But they diverge sharply underneath that shared vision.

OpenClaw is closer to a deterministic control plane. Its skill system is mainly human-authored. Developers define actions, prompts, and boundaries up front. That makes it well suited to scenarios where security, permissioning, and operational clarity matter more than open-ended adaptation.

Hermes takes the opposite bet. Skills are meant to emerge from experience. Memory isn't just about storing facts — it's about building a working model of the user. The value is less about precise control and more about cumulative capability.

They're probably not competing. They represent two complementary directions: one focused on execution, the other on cognition and growth.

The controversy worth knowing about

Hermes isn't just a technology story. It's also a trust story.

Several core members of Nous Research reportedly come from Web3, and the company's funding history reflects that ecosystem. As of April 2026, Nous Research had raised roughly $70M across two public rounds, with backing from major crypto-native investors. Its broader mission includes decentralized AI infrastructure — including Psyche, a distributed training network.

Worth noting: Nous Research had not officially launched a token or published any formal token distribution plan at the time of writing. But in crypto-adjacent communities, speculation around future airdrops had already started, and unofficial "NOUS" assets had emerged on-chain without direct project endorsement.

For developers: judge Hermes on its technical merit first. For everyone else: anything tied to unofficial NOUS token narratives deserves caution.

What this means for the agent ecosystem

Hermes matters because it's trying to build something the current AI stack still lacks: an agent that improves through use and keeps that improvement under user control.

If the model works, the way we evaluate agents may shift from "what can it do right now?" to "what does it become after months of shared work?" That would move the conversation away from static capability snapshots and toward compounding system value.

The project is still early. Long-term memory systems can become noisy. Auto-generated skills can be brittle. Self-improvement loops are notoriously hard to stabilize. Deployment isn't yet seamless enough for mainstream users.

But even at this stage, it's made one future feel more technically tangible: agents that become more valuable because they exist continuously in time, not because they win a benchmark on day one.

Tags: ai-agents open-source machine-learning developer-tools llm

Happy Horse 1.0: What We Know About the AI Video Model Topping Benchmarks

Evan-dong — Fri, 10 Apr 2026 12:51:44 +0000

If you've been following AI video generation lately, you've probably seen "Happy Horse" appear in benchmark discussions, Reddit threads, and X posts. It's a new video model that seemingly came out of nowhere and started ranking above established names like Seedance 2.0 and Kling 3.0 on public leaderboards. Here's what we know so far, what the benchmarks actually show, and why the AI video community is paying close attention.

How Happy Horse Appeared

Unlike most high-profile AI models, Happy Horse 1.0 didn't launch with a press event or a technical paper. It showed up on AI video benchmark leaderboards -- specifically Artificial Analysis's AI Video Arena -- and immediately started generating discussion because of where it ranked.

The model appeared near the top in multiple categories:

Text-to-video (without audio)
Image-to-video (without audio)
Text-to-video with audio (leading, but by a smaller margin)
Image-to-video with audio (roughly tied with Seedance 2.0)

That breadth is what caught people's attention. Most new models are strong in one mode. Happy Horse looked competitive across several.

The Seedance 2.0 Comparison

The most common comparison has been with Seedance 2.0, which has been one of the strongest video models in recent discussions.

Arguments for Happy Horse:

Strong multi-shot generation capability
Better prompt-following in detailed/cinematic instructions
Competitive enough to potentially shift the landscape if it becomes accessible

Arguments for caution:

Seedance 2.0 may still produce more natural motion in some side-by-side comparisons
Benchmark Elo rankings don't always translate directly to production value
No public API yet, so real-world testing is limited

The honest take: being "close to Seedance 2.0" is already significant for a new entrant. If Happy Horse turns out to be cheaper, faster, or more accessible, that changes the equation regardless of marginal quality differences.

Who Built It?

This has been the biggest mystery. Early speculation ranged widely, but a Chinese tech report from SMZDM has now attributed the model to Alibaba, claiming it was developed internally and will be formally released soon.

This is the strongest attribution so far, though it should still be treated as a reported development rather than a confirmed official announcement from Alibaba.

If confirmed, it would mean another major Chinese tech company entering the frontier video generation space alongside ByteDance (Seedance) and Kuaishou (Kling).

What the Benchmarks Actually Show

Based on the Artificial Analysis AI Video Arena data discussed across platforms:

Category	Happy Horse vs Competition
Text-to-video (no audio)	Ranked above Seedance 2.0 and Kling 3.0
Image-to-video (no audio)	Ranked above Seedance 2.0 and Kling 3.0
Text-to-video (with audio)	Leading, smaller margin
Image-to-video (with audio)	Roughly tied with Seedance 2.0

Important caveat: benchmark success does not equal production readiness. API availability, inference speed, cost, and consistency all matter for real deployment.

Why It Matters

The deeper significance isn't just about one model scoring well. It's about what happens next:

If Alibaba formally releases it, it adds another serious competitor to the video generation market
It could pressure existing providers on pricing and access
The community is watching whether it will be open source, support local workflows, or offer developer-friendly API access
A model doesn't need to be universally "the best" -- it just needs to be strong enough, affordable enough, and accessible enough to change user behavior

Current Status

As of now, Happy Horse 1.0 has no public API. The market is evaluating it through benchmark signals and community-shared examples. If the Alibaba attribution holds and a formal release follows, expect this to become one of the most consequential launches in AI video this year.

References

EvoLink is planning to support Happy Horse API access once it officially launches: https://evolink.ai/happyhorse-coming-soon?utm_source=dev&utm_medium=community&utm_campaign=happyhorse

tags: ai, video-generation, happy-horse, benchmark, seedance

Kling AI Video Generation Pricing: Complete Cost Breakdown for Developers (2026)

Evan-dong — Thu, 09 Apr 2026 06:20:07 +0000

If you're integrating Kling's video generation API into a project, one of the first questions you'll hit is: how much is this actually going to cost at scale? This guide breaks down every pricing tier for Kling 3.0, Kling O3, Kling O1, and Motion Control so you can budget accurately before you start building.

Tags: ai, video, api, machinelearning

How Kling Billing Works

Kling bills per second of output video, rounded to the nearest integer. The final cost depends on four variables:

Model (Kling 3.0, Kling O3, Kling O1)
Mode (Text-to-Video, Image-to-Video, Motion Control)
Resolution (720p or 1080p)
Audio (with or without)

Kling 3.0 Text-to-Video

Duration range: 3–15 seconds

Resolution	Without Audio	With Audio
720p	$0.075/sec	$0.113/sec
1080p	$0.100/sec	$0.150/sec

Quick cost checks:

5-sec 720p no audio: $0.38
10-sec 1080p no audio: $1.00
15-sec 1080p with audio: $2.25

Kling O3 Text-to-Video

Duration range: 3–15 seconds

Resolution	Without Audio	With Audio
720p	$0.075/sec	$0.100/sec
1080p	$0.100/sec	$0.125/sec

O3 costs less than 3.0 when audio is included — worth noting if you're generating at volume.

Quick cost checks:

8-sec 720p with audio: $0.80
15-sec 1080p with audio: $1.88 (vs $2.25 for 3.0)

Kling O1 Image-to-Video

Fixed duration options: 5 seconds or 10 seconds

Duration	Price	Per-second rate
5 seconds	$0.556	$0.111/sec
10 seconds	$1.111	$0.111/sec

Flat pricing, no audio options. Good for product image animation.

Kling 3.0 Motion Control

For precise animation control with motion paths and keyframes.

Duration depends on reference type:

Image reference: up to 10 seconds
Video reference: up to 30 seconds

Resolution	Rate
720p	$0.113/sec
1080p	$0.151/sec

Max cost scenario: 30-sec 1080p = $4.53

Model Selection Guide

Use case	Recommended	Cost
Budget / drafts	Kling O3 720p no audio	$0.075/sec
Social content with audio	Kling O3 720p with audio	$0.100/sec
Marketing / presentation	Kling O3 1080p with audio	$0.125/sec
Premium production	Kling 3.0 1080p with audio	$0.150/sec
Image animation	Kling O1	$0.111/sec flat
Complex animation	Motion Control 1080p	$0.151/sec

Audio Pricing Premium

Adding audio increases cost by:

Kling 3.0: +$0.038–$0.050/sec (+50%)
Kling O3: +$0.025/sec (+25–33%)

For high-volume pipelines without audio requirements, skipping audio saves significantly.

Real-World Scenarios

Social media campaign — 10 videos × 5 sec, 720p, with audio:

Kling 3.0: $5.65
Kling O3: $5.00 (save $0.65)

Product demo series — 5 videos × 12 sec, 1080p, with audio:

Kling 3.0: $9.00
Kling O3: $7.50 (save $1.50)

Image gallery animation — 20 images × 10 sec:

Kling O1: $22.22 total

Cost Optimization Tips

Prototype at 720p before committing to 1080p production runs
Skip audio during iteration — add only to final outputs
Use O3 for volume — cheaper than 3.0 with nearly equivalent quality
Reserve Motion Control for shots that actually need precise path control
Automatic fallback is built in — if a model is unavailable, Kling routes to the next cheapest option automatically