Forem: Chinallmapi

GPT-5.4 vs DeepSeek V4 vs GLM-4.7: How to choose the right model without testing each one

Chinallmapi — Sat, 02 May 2026 15:08:25 +0000

GPT-5.4 vs DeepSeek V4 vs GLM-4.7: How to choose the right model without testing each one

If you are building with AI models right now, you are facing too many choices.

OpenAI has GPT-5.4 and GPT-5.5. DeepSeek offers V4 Flash and V4 Pro. GLM has 4.7, 5, and 5.1. Kimi has K2.5. MiniMax has M2.5. Qwen has 3.5 Plus.

Each provider claims their model is the best. But benchmarks do not tell you which model is right for your specific use case.

I spent weeks testing these models across real workloads: code generation, technical writing, creative tasks, structured output, Chinese-language processing, and multi-step reasoning.

Here is what I found, and how I decided which model to use for which task.

The models I tested

All tests were run through a single gateway (ChinaLLM) using the same OpenAI-compatible SDK. Same prompts, same temperature, same max tokens. The only variable was the model name.

Models tested:

Model	Provider	Input per 1M	Output per 1M
gpt-5.4	OpenAI	$2.50 official / $0.325 via ChinaLLM	$15.00 official / $1.95 via ChinaLLM
gpt-5.5	OpenAI	$5.00 official / $0.65 via ChinaLLM	$30.00 official / $5.20 via ChinaLLM
deepseek-v4-flash	DeepSeek	$0.147	$0.294
deepseek-v4-pro	DeepSeek	$0.924	$1.848
glm-4.7	Alibaba	$0.660	$2.585
glm-5	Alibaba	$0.990	$3.553
GLM-5.1	ZAI	$1.197	$4.200
kimi-k2.5	Moonshot	$0.660	$3.410
MiniMax-M2.5	MiniMax	$0.352	$1.375
qwen3.5-plus	Alibaba	$1.320	$3.850

Pricing sourced from OpenAI official pricing and ChinaLLM public pricing.

Test 1: Code generation

Prompt: Write a Python function that implements a thread-safe LRU cache with a maximum size parameter and expiration timeout.

Results:

gpt-5.4: Excellent. Correct implementation using OrderedDict, threading.Lock, and time-based expiration. Included docstring, type hints, and a usage example.
deepseek-v4-pro: Very good. Correct implementation, slightly less polished docstring but functionally identical to GPT-5.4.
deepseek-v4-flash: Good. Basic LRU cache with threading, but missed the expiration timeout. Had to add it manually.
glm-4.7: Good. Working implementation, but the code style was less Pythonic. Used a manual dict instead of OrderedDict.
kimi-k2.5: Good. Correct logic, but included unnecessary complexity for a simple task.
MiniMax-M2.5: Adequate. Basic cache worked but had a subtle thread-safety bug in the eviction logic.

Verdict: For code generation, deepseek-v4-flash is good enough for simple tasks, deepseek-v4-pro is near-GPT quality for most code, and gpt-5.4 is best for complex or production-critical code.

Test 2: Technical explanation

Prompt: Explain how the transformer attention mechanism works to someone who understands neural networks but has not studied NLP.

Results:

gpt-5.4: Excellent. Clear analogy, step-by-step explanation, covered query, key, value with concrete examples.
deepseek-v4-pro: Very good. Similar structure to GPT-5.4, slightly less intuitive analogy but equally accurate.
deepseek-v4-flash: Fair. Explained the basics correctly but missed the scaled dot-product detail.
glm-4.7: Good. Strong explanation with a nice matrix visualization. Slightly more academic tone.
kimi-k2.5: Good. Solid explanation with a practical example from translation tasks.
MiniMax-M2.5: Fair. Covered the basics but had a minor inaccuracy about how attention scores are normalized.

Verdict: For technical writing and explanations, deepseek-v4-pro is the best value. It delivers near-GPT quality at a fraction of the cost.

Test 3: Chinese-language tasks

Prompt: Analyze the sentiment and extract key entities from a Chinese product review text.

Results:

GLM-5.1: Excellent. Correct sentiment analysis (mixed positive/negative), accurate entity extraction, nuanced analysis.
glm-4.7: Very good. Similar to GLM-5.1, slightly less detailed analysis.
qwen3.5-plus: Very good. Strong performance on entity extraction, good sentiment breakdown.
gpt-5.4: Good. Correct overall sentiment but missed the nuance in the mixed feedback.
deepseek-v4-pro: Good. Accurate but less detailed than Chinese-native models.
kimi-k2.5: Good. Good analysis with practical suggestions.
deepseek-v4-flash: Fair. Got the basic sentiment right but missed several entities.

Verdict: For Chinese-language tasks, GLM-5.1 and qwen3.5-plus outperform general-purpose models. Use a Chinese-native model when your workload is primarily in Chinese.

Test 4: Structured output (JSON)

Prompt: Return a JSON object with the schema: summary string, key_points array, sentiment enum, action_items array of objects.

Results:

gpt-5.4: Perfect JSON. All fields present, correctly typed, sensible content.
deepseek-v4-pro: Perfect JSON. Identical quality to GPT-5.4.
gpt-5.5: Perfect JSON. No noticeable difference from GPT-5.4 for this task.
glm-4.7: Good JSON. One minor issue: a key_points entry was an object instead of a string.
kimi-k2.5: Good JSON. All fields correct but content was slightly generic.
MiniMax-M2.5: Fair. JSON was valid but missing one optional field.
deepseek-v4-flash: Fair. JSON was mostly correct but had a type mismatch.

Verdict: For structured output, deepseek-v4-pro and gpt-5.4 are the most reliable. Flash models occasionally produce type mismatches.

Test 5: Multi-step reasoning

Prompt: A company has three departments. Engineering has twice as many people as Marketing. Sales has 5 more people than Engineering. If the total is 45 people, how many are in each department?

Results:

gpt-5.4: Correct. Set up equation M + 2M + (2M + 5) = 45, solved M = 8, Engineering = 16, Sales = 21.
deepseek-v4-pro: Correct. Same approach, same answer, clear steps.
gpt-5.5: Correct. Same as GPT-5.4.
glm-4.7: Correct. Different presentation but same math.
kimi-k2.5: Correct. Clear explanation.
deepseek-v4-flash: Incorrect. Set up the equation wrong, got wrong total.
MiniMax-M2.5: Incorrect. Similar equation error.
qwen3.5-plus: Correct. Clean solution.

Verdict: For multi-step reasoning, stick with deepseek-v4-pro or gpt-5.4. Flash models can make reasoning errors on problems with multiple constraints.

The decision matrix

After all the tests, here is how I map tasks to models:

Task type	Recommended model	Cost per 1M output	Why
Code generation simple	deepseek-v4-flash	$0.294	Fast, accurate enough for syntax
Code generation complex	deepseek-v4-pro	$1.848	Near-GPT quality, production-ready
Technical writing	deepseek-v4-pro	$1.848	Clear explanations, good structure
Creative writing	gpt-5.4	$1.95	Best nuance and style
Structured output	deepseek-v4-pro	$1.848	Reliable JSON, correct types
Multi-step reasoning	gpt-5.4 or deepseek-v4-pro	$1.95 / $1.848	Both reliable, pro is cheaper
Chinese-language tasks	GLM-5.1 or glm-4.7	$4.200 / $2.585	Outperform general models on Chinese
Simple Q&A	deepseek-v4-flash	$0.294	Good enough, very cheap
Image generation	gpt-image-2	$0.039 per image	Best quality through gateway

What surprised me

deepseek-v4-flash is better than I expected. For 80% of my daily tasks, it was good enough. The 20% where it fell short were edge cases: multi-constraint reasoning, structured output with strict schemas, and domain-specific knowledge.

Chinese-native models punch above their weight on Chinese tasks. GLM-5.1 and qwen3.5-plus consistently outperformed GPT-5.4 on sentiment analysis, entity extraction, and nuanced Chinese text generation.

GPT-5.5 is not worth the premium for most tasks. At 2x the price of GPT-5.4, I did not see a meaningful quality difference on the workloads I tested.

The gateway approach makes model selection trivial. Because all models are accessible through the same OpenAI-compatible SDK, switching is just changing a model string.

How to apply this to your workload

Categorize your tasks. Split your AI usage into buckets: code, writing, reasoning, Chinese, structured output.
Test one prompt per bucket. Run each through 3-4 models. Note the quality difference.
Assign models to buckets. Use the cheapest model that meets your quality bar.
Route through a gateway. Set up a single OpenAI-compatible client and route each task type to its model.
Re-test periodically. Model quality changes over time.

Final takeaway

You do not need to pick one model and stick with it. Use different models for different tasks, all through a single OpenAI-compatible interface.

deepseek-v4-flash for high-volume, low-risk tasks
deepseek-v4-pro for medium-complexity work
gpt-5.4 for edge cases requiring maximum quality
GLM-5.1 or glm-4.7 for Chinese-language tasks
gpt-image-2 for image generation

All pricing data sourced from OpenAI pricing and ChinaLLM pricing, accessed May 2026.

Complete code examples for multi-model routing: GitHub repo.

This is a practical model selection guide based on real testing, not a benchmark comparison.

How I cut my OpenAI API costs by 87% using a single gateway

Chinallmapi — Wed, 29 Apr 2026 13:48:21 +0000

I was paying around $200 per month for OpenAI API calls. Not a massive bill, but enough to notice every time the invoice came through.

The real pain point was not the absolute cost, it was the friction. Every time I wanted to try a cheaper model, the process looked like this:

Research alternatives. DeepSeek, GLM, Qwen, MiniMax, Kimi. Each has different capabilities and pricing.
Sign up for a new provider and generate an API key.
Install a new SDK or figure out their HTTP API format.
Rewrite my integration code to work with a different request and response structure.
Test that everything still works. Handle new error codes. Update retry logic.

That friction meant I stayed on OpenAI far longer than I should have. The switching cost was higher than the savings themselves.

Then I found a different approach. Instead of switching providers, I added a layer between my code and the models. One gateway that speaks the OpenAI protocol, but routes to multiple backends behind the scenes.

This is not a product review. It is a cost engineering story. And it changed how I think about building with AI models.

The problem: OpenAI pricing at scale

My app used OpenAI for three distinct capabilities.

Chat completions for general reasoning and code generation. This was the biggest cost driver. At OpenAI's official pricing, GPT-5.4 costs $2.50 per million input tokens and $15.00 per million output tokens.

Embeddings for semantic search across a knowledge base. Smaller cost, but it adds up.

Image generation for blog post thumbnails. At OpenAI's official pricing, GPT-image-2 costs $8.00 per million tokens for inputs, $2.00 for cached inputs, $30.00 for outputs.

Total: around $200 per month in API usage, including subscriptions.

The obvious fix was to switch to cheaper models for at least some of these workloads. DeepSeek offered similar quality at a fraction of the price. GLM, Qwen, and Kimi had strengths for specific tasks. But each switch meant a new integration, a new authentication flow, a new set of quirks.

Three integrations. Three auth flows. Three potential failure points.

I procrastinated for months.

The alternative: a unified gateway

A gateway sits between your application code and multiple model providers. Your app calls it the same way it calls OpenAI. Same base URL pattern, same Bearer token authentication, same request and response format.

Behind the scenes, the gateway routes your request to whichever model you specify in the model parameter.

The key insight: you specify the model name, not the provider. The gateway handles the routing.

Here is what the setup looks like with the standard OpenAI Python SDK:

import openai

client = openai.OpenAI(
    api_key="your-gateway-key",
    base_url="https://chinallmapi.com/v1"
)

# Same SDK, different backends:
client.chat.completions.create(model="gpt-5.4", messages=[...])
client.chat.completions.create(model="deepseek-v4-flash", messages=[...])
client.chat.completions.create(model="glm-4.7", messages=[...])
client.chat.completions.create(model="kimi-k2.5", messages=[...])

Same SDK. Same method signatures. Different backend. Zero code changes beyond the base URL and the model name.

This is what ChinaLLM does. It is an OpenAI-compatible gateway that routes to both OpenAI models and China-native providers.

The cost comparison with real numbers

I pulled the actual pricing from both OpenAI's official pricing page (openai.com/api/pricing/) and ChinaLLM's public pricing page (chinallmapi.com/pricing). Here is what I found.

GPT-5.4 per 1 million tokens:

	OpenAI official	ChinaLLM	Savings
Input	$2.50	$0.325	87%
Output	$15.00	$1.95	87%
Cached input	$0.25	$0.033	87%

GPT-5.5 per 1 million tokens:

	OpenAI official	ChinaLLM	Savings
Input	$5.00	$0.65	87%
Output	$30.00	$5.20	87%
Cached input	$0.50	$0.065	87%

The 1.3x OpenAI group multiplier on ChinaLLM is already reflected in these prices. Even with the markup, you are paying roughly 13% of OpenAI's official rate for the exact same model.

China-native models available through the same gateway:

Model	Input (per 1M)	Output (per 1M)	Group
deepseek-v4-flash	$0.147	$0.294	DeepSeek 1.05x
deepseek-v4-pro	$0.924	$1.848	DeepSeek 1.05x
glm-4.7	$0.660	$2.585	CodingPlan 1.1x
glm-5	$0.990	$3.553	CodingPlan 1.1x
GLM-5.1	$1.197	$4.200	ZAI 1x
kimi-k2.5	$0.660	$3.410	CodingPlan 1.1x
MiniMax-M2.5	$0.352	$1.375	CodingPlan 1.1x
qwen3.5-plus	$1.320	$3.850	CodingPlan 1.1x

For my use case -- coding assistance and general reasoning -- I tested deepseek-v4-flash, deepseek-v4-pro, and glm-4.7:

deepseek-v4-flash ($0.147 input / $0.294 output) handled about 80% of my prompts acceptably. Code generation, simple Q&A, drafting emails and summaries all worked fine.
deepseek-v4-pro ($0.924 input / $1.848 output) handled about 95% at near-GPT quality. Technical explanations, debugging assistance, and documentation generation all worked well.
I only needed gpt-5.4 for complex multi-step reasoning and creative writing where nuance really matters.

My switching strategy: incremental migration

I did not switch everything at once. I migrated in phases, testing quality at each step.

Phase 1: High-volume, low-risk calls to deepseek-v4-flash

Changed my chat completions to use deepseek-v4-flash for:

Code generation (syntax is deterministic, quality is fine)
Simple Q&A (factual questions that don't need nuanced reasoning)
Drafting emails and summaries (good enough for a first pass)

Saved: about $120 per month on these workloads

Phase 2: Medium-risk calls to deepseek-v4-pro

Used deepseek-v4-pro for:

Technical explanations (needed more depth than flash provided)
Debugging assistance (needed to follow logic chains)
Documentation generation (needed structure and completeness)

Saved: about $40 per month while maintaining near-GPT quality.

Phase 3: Keep premium for edge cases with gpt-5.4 through the gateway

Kept GPT-5.4 for complex reasoning chains and creative writing, but routed it through the gateway instead of calling OpenAI directly. At $0.325 input / $1.95 output through the gateway versus $2.50 input / $15.00 output from OpenAI directly, I saved 87% even on the same model.

Volume dropped to less than 10% of total usage, but the per-call savings were massive.

Phase 4: Embeddings and images

Migrated embeddings to DeepSeek through the gateway. Kept images on gpt-image-2 ($0.039 per image through the gateway).

The total savings

Metric	Before	After	Difference
Monthly API cost	~$200	~$50	~$150 saved
Percentage	100%	25%	75% reduction
GPT-5.4 usage	100% of calls via OpenAI direct	<10% of calls via gateway	87% saved per call

The key insight: I did not rewrite any integration code. I changed model strings in configuration files and let the gateway handle the routing.

The code change was minimal

Before:

# config.py
MODEL = "gpt-5.4"

# app.py
response = client.chat.completions.create(model=MODEL, messages=prompts)

After:

# config.py
MODEL_SIMPLE = "deepseek-v4-flash"    # coding, simple tasks
MODEL_ADVANCED = "deepseek-v4-pro"    # technical explanations
MODEL_PREMIUM = "gpt-5.4"             # complex reasoning

# app.py
def get_response(prompt_type, messages):
    model = {
        "code": MODEL_SIMPLE,
        "explain": MODEL_ADVANCED,
        "complex": MODEL_PREMIUM
    }[prompt_type]
    return client.chat.completions.create(model=model, messages=messages)

That is the entire change. Same client. Same method. Same response handling.

When this approach makes sense

This is not for everyone. You need:

Volume enough to care about cost. If you are spending less than $50 per month on API usage, the absolute savings may not justify the migration effort.

Flexibility in quality requirements. If every single call needs GPT-5.5 level quality, you are locked into premium pricing. The savings come from routing different workloads to different quality tiers.

Multiple model use cases. If you only use chat completions for one type of task, a simpler direct integration might be cleaner. The gateway approach shines when you have multiple capabilities -- chat, embeddings, images -- and want to optimize each independently.

Gateway trust. You are adding a middle layer. ChinaLLM has public documentation and transparent pricing, which helped with my evaluation.

The trade-offs

What I gained:

75% cost reduction on my API bill
Zero integration code changes -- the SDK stayed the same
The ability to test new models instantly by just changing the model string
87% savings on the same OpenAI models when routed through the gateway

What I accepted:

A gateway layer between me and the providers
Slightly higher latency from the routing overhead
Different quality profiles for different models that required testing and tuning

The net result was clearly positive.

Final takeaway

If your API costs are noticeable:

Do not integrate each provider separately. The switching cost is too high.
Use a unified gateway. One SDK, multiple backends, model-level routing.
Migrate incrementally. Start with high-volume, low-risk calls. Test the quality. Expand gradually.

The math: my ~$200/month bill became ~$50/month. The code change was updating configuration values.

For complete code examples in Python, Node.js, and curl, see the GitHub repo.

All pricing data in this article was sourced from OpenAI's official pricing page and ChinaLLM's public pricing page, accessed May 2026.

This is a cost engineering story, not a product endorsement. The approach -- gateway-based model routing -- is what matters. ChinaLLM is one implementation of that pattern, publicly documented with transparent pricing.