Codestral vs o3-mini
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | Codestral | o3-mini |
|---|---|---|
| Provider | Mistral | OpenAI |
| Input Price (1M) | $0.30 | $1.10 |
| Output Price (1M) | $0.90 | $4.40 |
| Context Window | 256,000 | 200,000 |
Verdict
Codestral costs $0.30 per 1M input tokens and $0.90 per 1M output tokens. o3-mini costs $1.10 per 1M input tokens and $4.40 per 1M output tokens. Codestral is 73% cheaper on input tokens than o3-mini. For output tokens, Codestral is the more affordable option at $0.90/1M vs $4.40.
On context window, Codestral supports 256,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose Codestral
- ✓ You need the lowest input token cost ($ 0.30/1M)
- ✓ Your workload is output-heavy — Codestral generates text cheaper
- ✓ You need a larger context window (256,000 tokens)
- ✓ You are already integrated with Mistral
When to choose o3-mini
- ✓ You are already integrated with OpenAI
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 Codestral cheaper than o3-mini? ▼
Codestral is cheaper on input tokens at $0.30/1M vs $1.10/1M for o3-mini — a 73% saving.
What is the context window of Codestral vs o3-mini? ▼
Codestral has a 256,000-token context window. o3-mini has a 200,000-token context window. Codestral supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: Codestral or o3-mini? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Codestral is the cheaper option. For maximum context length, Codestral supports 256,000 tokens. Use the comparison table above to find the right fit for your workload.