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GPT-4o mini vs Codestral

Detailed pricing comparison and cost analysis.

Updated September 2026

Cost Simulator

GPT-4o mini Cost
$0.27
Codestral Cost
$0.48
GPT-4o mini is 44% cheaper
FeatureGPT-4o miniCodestral
ProviderOpenAIMistral
Input Price (1M)$0.15$0.30
Output Price (1M)$0.60$0.90
Context Window128,000256,000

Verdict

GPT-4o mini costs $0.15 per 1M input tokens and $0.60 per 1M output tokens. Codestral costs $0.30 per 1M input tokens and $0.90 per 1M output tokens. GPT-4o mini is 50% cheaper on input tokens than Codestral. For output tokens, GPT-4o mini is the more affordable option at $0.60/1M vs $0.90.

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 GPT-4o mini

  • ✓ You need the lowest input token cost ($ 0.15/1M)
  • ✓ Your workload is output-heavy — GPT-4o mini generates text cheaper
  • ✓ You are already integrated with OpenAI

When to choose Codestral

  • ✓ You need a larger context window (256,000 tokens)
  • ✓ 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 GPT-4o mini cheaper than Codestral?

GPT-4o mini is cheaper on input tokens at $0.15/1M vs $0.30/1M for Codestral — a 50% saving.

What is the context window of GPT-4o mini vs Codestral?

GPT-4o mini has a 128,000-token context window. Codestral has a 256,000-token context window. Codestral supports the larger context, suitable for longer documents and agentic workflows.

Which model is better: GPT-4o mini or Codestral?

The best choice depends on your use case. For cost efficiency on input tokens, GPT-4o mini 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.