Model comparison
GPT-6 Sol vs Mistral Large 4
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.1 on the Noometry Index. Mistral Large 4 costs 3.9× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Mistral AISummary
- They share 15 benchmarks with published results for both. GPT-6 Sol scores higher in 5 categories and Mistral Large 4 in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 74.0 to 22.5.
- The biggest single-benchmark swing is NYT Connections (extended): 90.1% for GPT-6 Sol and 27.4% for Mistral Large 4.
- Mistral Large 4 is cheaper at $0.68 / $2.09 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-6 Sol | Mistral Large 4 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 61.8 | 43.1 |
| Released | 2026-09-22 | 2026-10-06 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 262K |
| Input $ / M tokens | $2 | $0.68 |
| Output $ / M tokens | $10 | $2.09 |
| Results tracked | 45 | 15 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Mistral Large 4: 48.6 (#57)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena WebDev | 1688 | 1541 |
| LMArena Coding | 1447 | 1475 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| SciCode | 57.6% | — |
| ALE-Bench | 2,462 | — |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Mistral Large 4: —
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Mistral Large 4: 22.5 (#192)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| NYT Connections (extended) | 90.1% | 27.4% |
| LMArena Hard Prompts | 1418 | 1444 |
| ARC-AGI-2 | 89.6% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| DTBench | 97.3% | — |
| LMCA | 59.1% | — |
| Epoch Capabilities Index | 162.72 | — |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Mistral Large 4: 40.4 (#91)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Math | 1402 | 1488 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Mistral Large 4: 36.6 (#166)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| SimpleQA Verified | 60.7% | 20% |
| LMArena Expert | 1439 | 1447 |
| GPQA Diamond | 94.3% | — |
| Vectara Hallucination Rate | 6.5% | — |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Mistral Large 4: —
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual Mistral Large 4 leads
GPT-6 Sol: 50.5 (#118), Mistral Large 4: 52.6 (#82)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Non-English | 1385 | 1415 |
| LMArena Chinese | 1405 | 1491 |
| LMArena Russian | 1401 | 1414 |
| LMArena French | 1410 | — |
| LMArena German | 1390 | — |
| LMArena Japanese | 1385 | — |
| LMArena Korean | 1341 | — |
| LMArena Spanish | 1384 | — |
Instruction Following Too close to call
GPT-6 Sol: 74.5 (#94), Mistral Large 4: 75.0 (#76)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Instruction Following | 1412 | 1424 |
Long Context Too close to call
GPT-6 Sol: 43.1 (#108), Mistral Large 4: 43.6 (#89)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Longer Query | 1411 | 1429 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Mistral Large 4: 60.4 (#97)
| Benchmark | GPT-6 Sol | Mistral Large 4 |
|---|---|---|
| LMArena Text | 1395 | 1427 |
| LMArena Creative Writing | 1378 | 1361 |
| LMArena Multi-Turn | 1412 | 1424 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Mistral Large 4?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 43.1 on the Noometry Index. Mistral Large 4 costs 3.9× 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 Mistral Large 4?
Mistral Large 4 is cheaper. It lists at $0.68 per million input tokens and $2.09 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Mistral Large 4 better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 48.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-6 Sol and Mistral Large 4 share?
15 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Mistral Large 4 has 15.