Model comparison
GPT-6 Sol vs Mixtral 8x7B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.7× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. GPT-6 Sol scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 18.8.
- The biggest single-benchmark swing is GPQA Diamond: 94.3% for GPT-6 Sol and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 61.8 | 27.1 |
| Released | 2026-09-22 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $2 | $0.70 |
| Output $ / M tokens | $10 | $0.70 |
| Results tracked | 45 | 38 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1447 | 1126 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| ALE-Bench | 2,462 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Mixtral 8x7B: —
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1115 |
| DTBench | 97.3% | 49.6% |
| Epoch Capabilities Index | 162.72 | 118.47 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| LMCA | 59.1% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1402 | 1147 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 94.3% | 30.6% |
| LMArena Expert | 1439 | 1088 |
| SimpleQA Verified | 60.7% | — |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 6.5% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Mixtral 8x7B: —
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1385 | 1077 |
| LMArena Chinese | 1405 | 1055 |
| LMArena French | 1410 | 1166 |
| LMArena German | 1390 | 1114 |
| LMArena Japanese | 1385 | 931 |
| LMArena Korean | 1341 | 968 |
| LMArena Russian | 1401 | 1090 |
| LMArena Spanish | 1384 | 1111 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1109 |
| IFEval | — | 57.5% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1411 | 1103 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1395 | 1132 |
| LMArena Creative Writing | 1378 | 1109 |
| LMArena Multi-Turn | 1412 | 1115 |
| EQ-Bench Creative Writing | 2125 | — |
| WildBench | — | 67.3% |
Frequently asked questions
Is GPT-6 Sol better than Mixtral 8x7B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.7× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol or Mixtral 8x7B?
Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Mixtral 8x7B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 32K.
How many benchmarks do GPT-6 Sol and Mixtral 8x7B share?
20 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Mixtral 8x7B has 38.