I tested 4 free 70B-class LLM endpoints for real production work — here's what each is actually good at

Pranav Chandra — Sat, 02 May 2026 09:35:16 +0000

The question

Most "production-grade" AI tools ship on paid endpoints — OpenAI, Anthropic, Gemini Pro. That's the safe choice. It's also the expensive one.

I wanted to know: in mid-2026, can free 70B-class open-source endpoints actually carry a real product workload? Not a toy chatbot — a tool that generates working HTML/CSS/JS for arbitrary user prompts.

So I built one. The result is Sitecraft (wiz-craft.vercel.app) — a free, open-source AI website builder. It runs across four different free endpoints and lets users switch between them mid-conversation.

This post is what I learned about each one. Not a benchmark — a working engineer's notes.

The 4 endpoints I shipped

Provider	Model	Why it's on the list
Cerebras	Qwen 3 235B	Reasoning depth — strongest at "think before generating"
Groq	Llama 4 Scout	Throughput king — fastest token rate I've measured
OpenRouter	Ling-2.6 Flash	Generalist; best fallback when the others rate-limit
Cloudflare	GPT-OSS 120B	Edge inference; lowest latency from a Workers backend

All four have free tiers that are actually usable for shipping (not just "free for 100 tokens then $50/M"). Different free-tier shapes, but real free.

What each one is actually good at

Cerebras · Qwen 3 235B — the reasoner

When the prompt requires planning (e.g. "build a 3-page site with consistent design language across all pages, plus a working contact form"), Qwen on Cerebras consistently produces the most coherent output. It thinks about the whole problem before emitting code.

Trade-off: it's slower per token than Groq, and the free tier rate-limits aggressively when you go beyond a few requests per minute.

Use it when: The prompt is open-ended and the model needs to invent structure.

Groq · Llama 4 Scout — the speed demon

Groq's LPUs are the fastest inference I've ever benchmarked. Llama 4 Scout hits ~500+ tokens/second sustained, which means a full single-page site (~3000-5000 tokens) lands in under 10 seconds — feels instant in the UI.

Trade-off: Llama 4 Scout is smaller and less "thoughtful" than Qwen 3 235B. For complex prompts it sometimes generates plausible but incorrect code (wrong CSS selectors, hallucinated APIs).

Use it when: Iteration speed matters more than first-shot correctness.

OpenRouter · Ling-2.6 Flash — the generalist fallback

OpenRouter's free Ling-2.6 Flash isn't the best at any single thing, but it's consistently okay across prompt types and almost never rate-limits. That makes it the perfect fallback target when one of the other three is throttled.

Trade-off: code output quality is noticeably lower than Qwen — more boilerplate, less elegant HTML structure.

Use it when: You need a fallback that won't fail. Paired with quality-checks downstream, it's the safety net.

Cloudflare · GPT-OSS 120B — the edge play

If your backend already runs on Cloudflare Workers, calling Cloudflare AI from the same edge node is hands-down the lowest-latency setup. No cross-region hop, no cold-start penalty.

Trade-off: GPT-OSS 120B is older architecture than the others. Output quality sits between Llama 4 Scout and Ling — fine for short outputs, weaker for long-form generation.

Use it when: Your stack is already on Cloudflare and you want to keep the inference call inside the edge.

The orchestration strategy

You don't pick one. You orchestrate.

For Sitecraft I shipped a simple router:

Default: Groq Llama 4 (fastest perceived response)
If user explicitly toggles "high-quality": Cerebras Qwen 3 235B
If primary is rate-limited (429): fall back to OpenRouter Ling
Cloudflare GPT-OSS: offered as a manual switch for users on slow connections (edge-routed)

The user can also switch mid-conversation — useful when, say, Qwen produced a great structural draft but you want Groq to iterate fast on the styling.

The whole router is ~80 lines of plain JS. No LangChain, no framework. Just an if/else over which endpoint to hit, plus a small wrapper that normalises response formats (each provider returns slightly different envelope JSON).

The honest trade-offs

If you're considering this approach, here's what I wish someone had told me upfront:

Response format quirks. Every provider returns slightly different JSON. Cerebras gives choices[0].message.content, Cloudflare gives result.response, OpenRouter mostly mirrors OpenAI but with quirks on streaming. Build a normaliser early or you'll regret it.
Free tiers are real but bounded. "Free" usually means rate-limited per minute and per day. For a side project with low traffic, you'll never notice. For a viral hit, you'll need a paid tier or a queue.
Quality variance is real. Even within the same prompt, Qwen and Llama 4 will produce noticeably different code. If your UX expects consistency, normalise via post-processing or pick one provider per request type.
No streaming compatibility guarantees. SSE format varies subtly. If you stream tokens to a UI, expect to write provider-specific stream parsers.
Latency varies wildly by region. Groq is fast from US-East. From APAC, the round-trip dominates. Cloudflare's edge play wins here.

When this approach is right (and when it isn't)

Use free open-source endpoints if:

You're shipping a side project / proof-of-concept and don't want a credit-card dependency
Your users are okay with "good enough" code (and you can iterate)
You want flexibility to swap models without lock-in
You're learning about LLM orchestration and want hands-on experience with the trade-offs

Stick with paid (Claude / GPT-5 / Gemini Pro) if:

You need single-shot correctness (no iteration loop)
You're building agentic workflows with deep reasoning (multi-tool, long context)
You have a real budget and uptime SLA matters
Your prompts genuinely need 200K+ context

For Sitecraft, the 4-endpoint orchestration was the right call — generating a website is iterative anyway, the user is in the loop, and "free forever" is part of the product pitch.

For my other side project, App Architect (a 5-phase design workflow that turns app ideas into TDD prompts), I went the opposite direction and built it on Claude Artifacts — because that tool needs deep reasoning over a long structured conversation, and the paid frontier model is worth it.

Right tool for the right job.

Try it

→ wiz-craft.vercel.app — switch between the 4 endpoints mid-conversation and feel the difference for yourself.

Source: github.com/pcpranav/sitecraft

If you've shipped on free open-source endpoints, I'd love to hear which providers you settled on and why. Drop a comment.

I built a 5-phase design system that turns "I have an app idea" into a TDD prompt

Pranav Chandra — Fri, 01 May 2026 21:14:27 +0000

App Architect walks you from a one-line idea to a production-ready prompt — user flow, page map, system design, spec, and tests. Here's why I built it and how it works.

The problem with "build me an app" prompts

Every developer has done this at least once: you have a vague idea, you open Claude or ChatGPT, you type "build me a habit tracker with social features", and forty seconds later you're staring at 600 lines of code that compiles but doesn't actually solve the problem you had in your head.

The model isn't wrong. Your prompt is.

There's a missing layer between "I have an idea" and "here is the code" — the layer where a real engineering team would normally spend a week: user flows, page maps, schemas, edge cases, the boring stuff that decides whether the thing actually ships.

I kept skipping that layer. So I built a tool that refuses to let me skip it.

What App Architect does

App Architect is a Claude artifact that takes you through five structured phases. You describe your idea in one sentence; it asks the right follow-up questions; at the end you get a single prompt you can paste into Claude Code (or any capable LLM) to actually build the thing.

The five phases:

App Flow — user journey, tech stack, integrations, edge cases
Page Map — routes, wireframes, layout zones, key interactions
System Design — DB schema, API routes, architecture diagram
Executive Summary — full spec: features, stack, risks, open questions
TDD Prompt — a production-ready prompt with unit tests, E2E tests, and CI config baked in

The output of phase 5 is the artifact. Everything before it exists to make that final prompt non-garbage.

Why TDD as the final output

Because the failure mode of AI-generated code isn't "it doesn't compile." It's "it compiles, looks reasonable, and silently does the wrong thing in three places."

If the final prompt asks the LLM to write tests first, then implementation, you get:

A clear, executable definition of "done"
A regression net the moment something else changes
A way to spot hallucinated APIs early (the test fails before you ship)

You don't need to be a TDD purist to want that.

How to use it

Open the App Architect landing page
Click Launch — it opens as a Claude artifact in your own Claude account (free tier works fine)
Describe your idea in one or two sentences
Answer the phase prompts honestly — "I don't know yet" is a valid answer and the tool handles it
Copy the final TDD prompt and paste it into Claude Code, Cursor, or any agentic dev tool

There's no sign-up on my side. No data collection. Usage runs on your Claude account, billed to you. I don't see any of it.

What I'd do differently

A few things I'd change if I rebuilt it tomorrow:

Persist drafts. Right now if you close the tab mid-flow you start over. Local storage would fix it cheaply.
Branching flows. Some apps don't have a "page map" (CLIs, MCP servers, libraries). The current flow nudges everything toward web apps. A branch at phase 1 would help.
Stack templates. A "Next.js + Postgres + Drizzle" preset would skip three rounds of follow-up questions for the 60% of users who already know their stack.

I'll get there. For now, the v1 covers the painful part: stopping you from prompting before you've thought.

Try it

→ promptcraft-io.vercel.app

If you build something with it, I'd genuinely love to see what comes out the other side. Drop a comment.

Forem: Pranav Chandra

I tested 4 free 70B-class LLM endpoints for real production work — here's what each is actually good at

The question

The 4 endpoints I shipped

What each one is actually good at

Cerebras · Qwen 3 235B — the reasoner

Groq · Llama 4 Scout — the speed demon

OpenRouter · Ling-2.6 Flash — the generalist fallback

Cloudflare · GPT-OSS 120B — the edge play

The orchestration strategy

The honest trade-offs

When this approach is right (and when it isn't)

Try it

I built a 5-phase design system that turns "I have an app idea" into a TDD prompt

The problem with "build me an app" prompts

What App Architect does

Why TDD as the final output

How to use it

What I'd do differently

Try it