Model comparison
GPT-6.1 Sol vs Mixtral 8x7B
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.7× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
Mistral AISummary
- They share 14 benchmarks with published results for both. GPT-6.1 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-6.1 Sol leads 93.7 to 18.8.
- The biggest single-benchmark swing is GPQA Diamond: 95.4% for GPT-6.1 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-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 65.6 | 27.1 |
| Released | 2026-09-29 | 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 | 34 | 38 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1487 | 1126 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-6.1 Sol: 39.6 (#26), Mixtral 8x7B: —
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 60% | — |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1466 | 1115 |
| Epoch Capabilities Index | 166.09 | 118.47 |
| ARC-AGI-2 | 94.2% | — |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| Mystery Game Puzzles | 80% | — |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1464 | 1147 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 95.4% | 30.6% |
| LMArena Expert | 1502 | 1088 |
| SimpleQA Verified | 73.9% | — |
| MMLU-Pro | — | 33.5% |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Mixtral 8x7B: —
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1438 | 1077 |
| LMArena Chinese | 1477 | 1055 |
| LMArena Russian | 1455 | 1090 |
| LMArena French | — | 1166 |
| LMArena German | — | 1114 |
| LMArena Japanese | — | 931 |
| LMArena Korean | — | 968 |
| LMArena Spanish | — | 1111 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1468 | 1109 |
| IFEval | — | 57.5% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1465 | 1103 |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-6.1 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1447 | 1132 |
| LMArena Creative Writing | 1432 | 1109 |
| LMArena Multi-Turn | 1449 | 1115 |
| WildBench | — | 67.3% |
Frequently asked questions
Is GPT-6.1 Sol better than Mixtral 8x7B?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 27.1 on the Noometry Index. Mixtral 8x7B costs 5.7× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6.1 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-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Mixtral 8x7B better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 32K.
How many benchmarks do GPT-6.1 Sol and Mixtral 8x7B share?
14 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Mixtral 8x7B has 38.