Model comparison
GPT-5.1 vs Mixtral 8x7B
GPT-5.1 is the stronger model overall, scoring 49.0 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's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Mistral AISummary
- They share 26 benchmarks with published results for both. GPT-5.1 scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.1 leads 50.6 to 11.0.
- The biggest single-benchmark swing is GPQA Diamond: 87.6% for GPT-5.1 and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 49.0 | 27.1 |
| Released | 2025-11-13 | 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 | 63 | 38 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1454 | 1126 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| WeirdML | 60.8% | — |
| LiveBench Coding | 72.5% | — |
| ALE-Bench | 1,192 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5.1: 32.7 (#60), Mixtral 8x7B: —
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| DeepResearch Bench | 42.8% | — |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1457 | 1115 |
| DTBench | 90.1% | 49.6% |
| Epoch Capabilities Index | 149.64 | 118.47 |
| ForecastBench | 58.1 | 56.3 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| Chess Puzzles | 32% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | 72.1% | — |
| LMCA | 43.9% | — |
| Adversarial NLI | — | 55.2% |
| HellaSwag | — | 86.7% |
| LiveBench | 78.8% | — |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | 46.4% | 10.5% |
| LMArena Math | 1447 | 1147 |
| OTIS Mock AIME 2024-2025 | 88.6% | — |
| LiveBench Math | 94.5% | — |
| MATH Level 5 | — | 10% |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
| GSM8K | — | 74.4% |
Knowledge GPT-5.1 leads
GPT-5.1: 50.6 (#71), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 87.6% | 30.6% |
| MMLU-Pro | 57.9% | 33.5% |
| GPQA (HELM) | 44.2% | 29.6% |
| LMArena Expert | 1470 | 1088 |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| Vectara Hallucination Rate | 10.9% | — |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Mixtral 8x7B: —
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 53.8 (#56), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1431 | 1077 |
| LMArena Chinese | 1495 | 1055 |
| LMArena French | 1450 | 1166 |
| LMArena German | 1438 | 1114 |
| LMArena Japanese | 1453 | 931 |
| LMArena Korean | 1401 | 968 |
| LMArena Russian | 1435 | 1090 |
| LMArena Spanish | 1433 | 1111 |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| IFEval | 93.5% | 57.5% |
| LMArena Instruction Following | 1443 | 1109 |
| LiveBench Instruction Following | 93.3% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1447 | 1103 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-5.1 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1443 | 1132 |
| LMArena Creative Writing | 1427 | 1109 |
| WildBench | 86.3% | 67.3% |
| LMArena Multi-Turn | 1450 | 1115 |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Mixtral 8x7B?
GPT-5.1 is the stronger model overall, scoring 49.0 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's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 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 lists at $1.25 and $10.
Is GPT-5.1 or Mixtral 8x7B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 32K.
How many benchmarks do GPT-5.1 and Mixtral 8x7B share?
26 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Mixtral 8x7B has 38.