Model comparison
GPT-5-Codex vs Mistral Small
GPT-5-Codex is the stronger model overall, scoring 37.9 to 33.4 on the Noometry Index. Mistral Small costs 13× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GPT-5-Codex scores higher in 3 categories and Mistral Small in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5-Codex leads 30.9 to 19.8.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 70.3% for GPT-5-Codex and 37.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 262K.
- Mistral Small has downloadable open weights; the other is API-only.
Side by side
| GPT-5-Codex | Mistral Small | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 37.9 | 33.4 |
| Released | 2025-09-15 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 256K |
| Input $ / M tokens | $1.25 | $0.15 |
| Output $ / M tokens | $10 | $0.60 |
| Results tracked | 3 | 39 |
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Category by category
Coding GPT-5-Codex leads
GPT-5-Codex: 42.4 (#103), Mistral Small: 34.0 (#247)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| SciCode | — | 26.5% |
| WeirdML | 54.5% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| LMArena Coding | — | 1362 |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |
Agentic & Tool Use GPT-5-Codex leads
GPT-5-Codex: 31.0 (#72), Mistral Small: 28.1 (#93)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| Terminal-Bench | 44.3% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
Reasoning GPT-5-Codex leads
GPT-5-Codex: 30.9 (#83), Mistral Small: 19.8 (#250)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | 37.8% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 44.8% |
| LMArena Hard Prompts | — | 1335 |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| LiveBench | — | 44% |
Math Not comparable
GPT-5-Codex: —, Mistral Small: 16.4 (#293)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| LMArena Math | — | 1341 |
| MATH Level 5 | — | 46.8% |
Knowledge Not comparable
GPT-5-Codex: —, Mistral Small: 31.0 (#222)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| GPQA Diamond | — | 47.5% |
| Vectara Hallucination Rate | — | 5.1% |
| LMArena Expert | — | 1291 |
| MMLU | — | 68.7% |
Multimodal Not comparable
GPT-5-Codex: —, Mistral Small: 33.5 (#96)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual Not comparable
GPT-5-Codex: —, Mistral Small: 45.5 (#169)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| LMArena Non-English | — | 1315 |
| LMArena Chinese | — | 1340 |
| LMArena French | — | 1337 |
| LMArena German | — | 1340 |
| LMArena Japanese | — | 1275 |
| LMArena Korean | — | 1259 |
| LMArena Russian | — | 1324 |
| LMArena Spanish | — | 1346 |
Instruction Following Not comparable
GPT-5-Codex: —, Mistral Small: 66.4 (#209)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| LiveBench Instruction Following | — | 63.7% |
| LMArena Instruction Following | — | 1310 |
Long Context Not comparable
GPT-5-Codex: —, Mistral Small: 40.4 (#156)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| LMArena Longer Query | — | 1327 |
Writing & Preference Not comparable
GPT-5-Codex: —, Mistral Small: 52.5 (#171)
| Benchmark | GPT-5-Codex | Mistral Small |
|---|---|---|
| LMArena Text | — | 1338 |
| LMArena Creative Writing | — | 1305 |
| LMArena Multi-Turn | — | 1344 |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GPT-5-Codex better than Mistral Small?
GPT-5-Codex is the stronger model overall, scoring 37.9 to 33.4 on the Noometry Index. Mistral Small costs 13× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5-Codex or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5-Codex lists at $1.25 and $10.
Is GPT-5-Codex or Mistral Small better for coding?
GPT-5-Codex scores higher on coding benchmarks: 42.4 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5-Codex does, with 400K tokens against 262K.
How many benchmarks do GPT-5-Codex and Mistral Small share?
1 benchmark has published results for both models. GPT-5-Codex has 3 scored results on Noometry and Mistral Small has 39.