Mistral Small 4 vs Kimi K2.5
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Mistral Small 4 | Kimi K2.5 |
|---|---|---|
| Provider | Mistral | Moonshot AI |
| Input Price (1M) | $0.15 | $0.60 |
| Output Price (1M) | $0.60 | $3.00 |
| Context Window | 131,000 | 262,144 |
Verdict
Mistral Small 4 costs $0.15 per 1M input tokens and $0.60 per 1M output tokens. Kimi K2.5 costs $0.60 per 1M input tokens and $3.00 per 1M output tokens. Mistral Small 4 is 75% cheaper on input tokens than Kimi K2.5. For output tokens, Mistral Small 4 is the more affordable option at $0.60/1M vs $3.00.
On context window, Kimi K2.5 supports 262,144 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Mistral Small 4
- ✓ You need the lowest input token cost ($ 0.15/1M)
- ✓ Your workload is output-heavy — Mistral Small 4 generates text cheaper
- ✓ You are already integrated with Mistral
When to choose Kimi K2.5
- ✓ You need a larger context window (262,144 tokens)
- ✓ You are already integrated with Moonshot AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is Mistral Small 4 cheaper than Kimi K2.5? ▼
Mistral Small 4 is cheaper on input tokens at $0.15/1M vs $0.60/1M for Kimi K2.5 — a 75% saving.
What is the context window of Mistral Small 4 vs Kimi K2.5? ▼
Mistral Small 4 has a 131,000-token context window. Kimi K2.5 has a 262,144-token context window. Kimi K2.5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Mistral Small 4 or Kimi K2.5? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Mistral Small 4 is the cheaper option. For maximum context length, Kimi K2.5 supports 262,144 tokens. Use the comparison table above to find the right fit for your workload.