Short answer: pick the model that passes your task most reliably at the lowest cost per success. DeepSeek is a strong default for reasoning and code, Qwen is versatile across languages and sizes, and Kimi is built for long-context reading.
Compare on four axes
| Axis | What to measure | Why it matters |
|---|---|---|
| Quality | Pass rate on 20 real prompts | A cheaper model that fails twice is expensive |
| Latency | Time to first token and total time | Interactive tools feel slow above a few seconds |
| Context | Tokens you actually need per request | Long documents shrink the candidate list |
| Capabilities | Tool calling, JSON mode, vision | Agent workflows depend on structured output |
Published benchmarks help shortlist models. Your workload decides the winner. Build a small evaluation set from real tickets, documents or code before committing.
Where each model tends to fit
DeepSeek is often the first choice for reasoning-heavy work, code generation and cost-sensitive batch jobs. Its API is OpenAI-compatible, so integration is straightforward.
Qwen covers a wide range of model sizes and handles multilingual content well. It is a practical option when you need one provider for several languages or a smaller model for high-volume classification.
Kimi focuses on long-context tasks: contract review, research synthesis and large codebase questions. If your prompts routinely exceed 100k tokens, test it early.
Build a cost-per-success metric
Track input tokens, output tokens, cached tokens and retries for each request. Then calculate:
cost per success = (total model cost + retry cost) / successful tasks
A model with a lower sticker price but a 20% retry rate can lose to a steadier model. Include the human review time for failed outputs if the workflow is not fully automated.
Test latency the way users feel it
Measure time to first token for chat and streaming interfaces, and total completion time for batch jobs. Test at the same time of day and from the same region. Provider load varies, so run each test three times and use the median.
Switching later should be cheap
Use an OpenAI-compatible endpoint and keep the model name in configuration. Then a failed model can be replaced without touching application code. Our guide to switching models without rewriting code shows the pattern, and the TOKEN plans cover access to multiple providers.
Frequently asked questions
Which model is cheapest?
Pricing changes often and depends on input, output and cache rates. Compare cost per successful task rather than price per million tokens.
Can I use the same SDK for DeepSeek, Qwen and Kimi?
Yes, through an OpenAI-compatible gateway. Change the base URL and model ID while keeping the same client code.
Which model is best for long documents?
Start with Kimi for very long contexts, then test Qwen and DeepSeek on your own documents. Quality at long context varies by task.
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