Model comparison
GPT-5.3 Codex vs Mistral Medium
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 36.3 on the Noometry Index. Mistral Medium costs 1.6× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Mistral AISummary
- They share 2 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Mistral Medium in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 28.3.
- The biggest single-benchmark swing is WeirdML: 79.3% for GPT-5.3 Codex and 43.7% for Mistral Medium.
- Mistral Medium is cheaper at $1.50 / $7.50 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 262K.
- Mistral Medium has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Mistral Medium | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 45.8 | 36.3 |
| Released | 2026-02-05 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $1.75 | $1.50 |
| Output $ / M tokens | $14 | $7.50 |
| Results tracked | 8 | 36 |
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Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Mistral Medium: 34.2 (#243)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| WeirdML | 79.3% | 43.7% |
| ALE-Bench | 1,655 | 763.98 |
| SWE-bench Verified | 74.8% | — |
| FrontierCode | — | 8% |
| LMArena WebDev | 1409 | — |
| SciCode | — | 40.2% |
| LMArena Coding | — | 1434 |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 48.0 (#9), Mistral Medium: 28.3 (#90)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| Berkeley Function Calling Leaderboard | — | 37.7% |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Mistral Medium: 24.0 (#167)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| Kagi LLM Benchmark | — | 50% |
| CritPt | — | 0% |
| LMArena Hard Prompts | — | 1426 |
| DTBench | — | 75.5% |
| LMCA | — | 26.1% |
| Surface Evolver Bench | — | 26.9% |
| Epoch Capabilities Index | 156.77 | — |
Math Not comparable
GPT-5.3 Codex: —, Mistral Medium: 28.1 (#245)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 32.2% |
| ProofBench | — | 9% |
| LMArena Math | — | 1408 |
| MATH Level 5 | — | 81.6% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Not comparable
GPT-5.3 Codex: —, Mistral Medium: 25.0 (#265)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| GPQA Diamond | — | 59.5% |
| Humanity's Last Exam | — | 4.5% |
| Vectara Hallucination Rate | — | 22.7% |
| LMArena Expert | — | 1408 |
Multimodal Not comparable
GPT-5.3 Codex: —, Mistral Medium: 35.3 (#88)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| LMArena Vision | — | 1172 |
Multilingual Not comparable
GPT-5.3 Codex: —, Mistral Medium: 52.1 (#91)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| LMArena Non-English | — | 1408 |
| LMArena Chinese | — | 1447 |
| LMArena French | — | 1459 |
| LMArena German | — | 1432 |
| LMArena Japanese | — | 1378 |
| LMArena Korean | — | 1380 |
| LMArena Russian | — | 1411 |
| LMArena Spanish | — | 1433 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Mistral Medium: 73.7 (#116)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | — | 1398 |
Long Context Not comparable
GPT-5.3 Codex: —, Mistral Medium: 42.9 (#114)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| LMArena Longer Query | — | 1406 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Mistral Medium: 60.0 (#103)
| Benchmark | GPT-5.3 Codex | Mistral Medium |
|---|---|---|
| LMArena Text | — | 1424 |
| LMArena Creative Writing | — | 1391 |
| Short-Story Creative Writing | — | 77.3% |
| LMArena Multi-Turn | — | 1418 |
Frequently asked questions
Is GPT-5.3 Codex better than Mistral Medium?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 36.3 on the Noometry Index. Mistral Medium costs 1.6× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Codex or Mistral Medium?
Mistral Medium is cheaper. It lists at $1.50 per million input tokens and $7.50 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Mistral Medium better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 262K.
How many benchmarks do GPT-5.3 Codex and Mistral Medium share?
2 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Mistral Medium has 36.