Model comparison
GPT-5.1-Codex vs Mixtral 8x7B
GPT-5.1-Codex is the stronger model overall, scoring 38.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.9× less per token, which makes it the better buy when GPT-5.1-Codex's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Mistral AISummary
- The widest gap is in math, where GPT-5.1-Codex leads 30.3 to 18.8.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.25 / $10 for GPT-5.1-Codex.
- GPT-5.1-Codex accepts more context: 400K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1-Codex | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 38.6 | 27.1 |
| Released | 2025-11-12 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 400K | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $1.25 | $0.70 |
| Output $ / M tokens | $10 | $0.70 |
| Results tracked | 6 | 38 |
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Category by category
Coding GPT-5.1-Codex leads
GPT-5.1-Codex: 41.9 (#116), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1337 | — |
| LMArena Coding | — | 1126 |
| ALE-Bench | 1,245 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5.1-Codex: 38.0 (#33), Mixtral 8x7B: —
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 60.4% | — |
| METR Time Horizons | 70.8% | — |
Reasoning Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | — | 1115 |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| Epoch Capabilities Index | — | 118.47 |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5.1-Codex leads
GPT-5.1-Codex: 30.3 (#235), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| ProofBench | 9% | — |
| Omni-MATH | — | 10.5% |
| LMArena Math | — | 1147 |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | — | 30.6% |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| LMArena Expert | — | 1088 |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | — | 1077 |
| LMArena Chinese | — | 1055 |
| LMArena French | — | 1166 |
| LMArena German | — | 1114 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Russian | — | 1090 |
| LMArena Spanish | — | 1111 |
Instruction Following Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| IFEval | — | 57.5% |
| LMArena Instruction Following | — | 1109 |
Long Context Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | — | 1103 |
Writing & Preference Not comparable
GPT-5.1-Codex: —, Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-5.1-Codex | Mixtral 8x7B |
|---|---|---|
| LMArena Text | — | 1132 |
| LMArena Creative Writing | — | 1109 |
| WildBench | — | 67.3% |
| LMArena Multi-Turn | — | 1115 |
Frequently asked questions
Is GPT-5.1-Codex better than Mixtral 8x7B?
GPT-5.1-Codex is the stronger model overall, scoring 38.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 4.9× less per token, which makes it the better buy when GPT-5.1-Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1-Codex or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-5.1-Codex lists at $1.25 and $10.
Is GPT-5.1-Codex or Mixtral 8x7B better for coding?
GPT-5.1-Codex scores higher on coding benchmarks: 41.9 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1-Codex does, with 400K tokens against 32K.
How many benchmarks do GPT-5.1-Codex and Mixtral 8x7B share?
0 benchmarks have published results for both models. GPT-5.1-Codex has 6 scored results on Noometry and Mixtral 8x7B has 38.