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.
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