Model comparison
GPT-5.6 Sol vs Mixtral 8x7B
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 27.1 on the Noometry Index. Mixtral 8x7B costs 11× less per token, which makes it the better buy when GPT-5.6 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-5.6 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-5.6 Sol leads 85.6 to 18.8.
- The biggest single-benchmark swing is GPQA Diamond: 93.5% for GPT-5.6 Sol and 30.6% for Mixtral 8x7B.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 32K.
- Mixtral 8x7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 65.0 | 27.1 |
| Released | 2026-07-09 | 2023-12-11 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 32K |
| Max output | 128K | 32K |
| Input $ / M tokens | $4 | $0.70 |
| Output $ / M tokens | $20 | $0.70 |
| Results tracked | 65 | 38 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Mixtral 8x7B: 32.8 (#269)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1498 | 1126 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| GSO | 76.5% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.3 (#7), Mixtral 8x7B: —
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), Mixtral 8x7B: 18.2 (#285)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1484 | 1115 |
| DTBench | 96% | 49.6% |
| Epoch Capabilities Index | 161.66 | 118.47 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Adversarial NLI | — | 55.2% |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Mixtral 8x7B: 18.8 (#289)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1474 | 1147 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 74.4% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Mixtral 8x7B: 11.0 (#301)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 93.5% | 30.6% |
| LMArena Expert | 1516 | 1088 |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 12.4% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), Mixtral 8x7B: —
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), Mixtral 8x7B: 29.6 (#266)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1452 | 1077 |
| LMArena Chinese | 1527 | 1055 |
| LMArena French | 1477 | 1166 |
| LMArena German | 1476 | 1114 |
| LMArena Japanese | 1471 | 931 |
| LMArena Korean | 1442 | 968 |
| LMArena Russian | 1468 | 1090 |
| LMArena Spanish | 1441 | 1111 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Mixtral 8x7B: 51.0 (#297)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1482 | 1109 |
| IFEval | — | 57.5% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), Mixtral 8x7B: 33.4 (#260)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1480 | 1103 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Mixtral 8x7B: 34.2 (#270)
| Benchmark | GPT-5.6 Sol | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1457 | 1132 |
| LMArena Creative Writing | 1448 | 1109 |
| LMArena Multi-Turn | 1460 | 1115 |
| EQ-Bench Creative Writing | 1972 | — |
| WildBench | — | 67.3% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Mixtral 8x7B?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 27.1 on the Noometry Index. Mixtral 8x7B costs 11× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 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-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Mixtral 8x7B better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 32.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 32K.
How many benchmarks do GPT-5.6 Sol and Mixtral 8x7B share?
20 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Mixtral 8x7B has 38.