Codestral vs GPT-5.3-Codex
Comprehensive comparison between Mistral AI's Codestral and OpenAI's GPT-5.3-Codex. Compare pricing, performance, features, and user reviews.
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Codestral
Mistral AIMistral's dedicated coding model. SOTA for Fill-in-the-Middle (FIM) use cases. 2x faster code generation than original. 256K context for large codebases. Excellent for autocomplete and code completion.
$0.3/$0.9per M tokens
Details GPT-5.3-Codex
OpenAIOpenAI's coding-optimized model, surpassing Claude on SWE-bench. HN users praise its coding value and much more generous quotas than Claude. Ideal for intensive coding work.
$1.75/$14per M tokens
Details Specs Comparison
| Specification | Codestral | GPT-5.3-Codex |
|---|---|---|
| Context Window | 256K | 256K |
| Max Output | 33K | 64K |
| Input (per 1M tokens) | $0.30 | $1.75 |
| Output (per 1M tokens) | $0.90 | $14.00 |
| Reasoning | ||
| Open Source |
Scenario Score Comparison
Coding
—
vs
93
Translation
—
vs
65
Data Analysis
—
vs
78
Conversation
—
vs
60
Image Gen
—
vs
50
Codestral
Pros
- + SOTA for FIM/autocomplete
- + 2x faster than original Codestral
- + 256K context for large codebases
- + Very affordable ($0.3/$0.9)
- + Optimized for IDE integration
Cons
- − Code-only, not general purpose
- − No multimodal support
- − Not open source (unlike Mistral Large)
- − Less capable for complex reasoning
GPT-5.3-Codex
Pros
- + Coding-optimized
- + Great value
- + Generous quotas
- + SWE-bench leader
Cons
- − Text-only
- − Weak at creative tasks
- − Requires Codex-specific API
Recommendation
Choose Codestral if you:
- • Need sota for fim/autocomplete
- • Need 2x faster than original codestral
- • Need 256k context for large codebases
Choose GPT-5.3-Codex if you:
- • Need coding-optimized
- • Need great value
- • Need generous quotas
Based on scores across 5 scenarios, GPT-5.3-Codex performs better overall.
Get Started with Codestral
1Visit the provider's website
2Create an account
3Start using the model
Get Started with GPT-5.3-Codex
1Visit the provider's website
2Create an account
3Start using the model
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