Kimi K3 vs Codestral
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Kimi K3 | Codestral |
|---|---|---|
| Provider | Moonshot AI | Mistral |
| Input Price (1M) | $3.00 | $0.30 |
| Output Price (1M) | $15.00 | $0.90 |
| Context Window | 1,000,000 | 256,000 |
Verdict
Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. Codestral costs $0.30 per 1M input tokens and $0.90 per 1M output tokens. Codestral is 90% cheaper on input tokens than Kimi K3. For output tokens, Codestral is the more affordable option at $0.90/1M vs $15.00.
On context window, Kimi K3 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Kimi K3
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Moonshot AI
When to choose Codestral
- ✓ You need the lowest input token cost ($ 0.30/1M)
- ✓ Your workload is output-heavy — Codestral generates text cheaper
- ✓ You are already integrated with Mistral
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 Kimi K3 cheaper than Codestral? ▼
Codestral is cheaper on input tokens at $0.30/1M vs $3.00/1M for Kimi K3 — a 90% saving.
What is the context window of Kimi K3 vs Codestral? ▼
Kimi K3 has a 1,000,000-token context window. Codestral has a 256,000-token context window. Kimi K3 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Kimi K3 or Codestral? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Codestral is the cheaper option. For maximum context length, Kimi K3 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.