Model comparison
Claude Opus 4.8 vs Mistral Large 4
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 43.1 on the Noometry Index. Mistral Large 4 costs 9.7× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Mistral AISummary
- They share 15 benchmarks with published results for both. Claude Opus 4.8 scores higher in 8 categories and Mistral Large 4 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.8 leads 64.7 to 22.5.
- The biggest single-benchmark swing is NYT Connections (extended): 91.1% for Claude Opus 4.8 and 27.4% for Mistral Large 4.
- Mistral Large 4 is cheaper at $0.68 / $2.09 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Mistral Large 4 accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Opus 4.8 | Mistral Large 4 | |
|---|---|---|
| Provider | Anthropic | Mistral AI |
| Noometry Index | 60.7 | 43.1 |
| Released | 2026-05-28 | 2026-10-06 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 128K | 262K |
| Input $ / M tokens | $5 | $0.68 |
| Output $ / M tokens | $25 | $2.09 |
| Results tracked | 65 | 15 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), Mistral Large 4: 48.6 (#57)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena WebDev | 1556 | 1541 |
| LMArena Coding | 1490 | 1475 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.8: 47.6 (#11), Mistral Large 4: —
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| APEX-Agents | 48.9% | — |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), Mistral Large 4: 22.5 (#192)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| NYT Connections (extended) | 91.1% | 27.4% |
| LMArena Hard Prompts | 1482 | 1444 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 158.21 | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), Mistral Large 4: 40.4 (#91)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Math | 1487 | 1488 |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 69% | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), Mistral Large 4: 36.6 (#166)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| SimpleQA Verified | 53% | 20% |
| LMArena Expert | 1502 | 1447 |
| GPQA Diamond | 91% | — |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), Mistral Large 4: —
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Claude Opus 4.8 leads
Claude Opus 4.8: 55.2 (#33), Mistral Large 4: 52.6 (#82)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Non-English | 1450 | 1415 |
| LMArena Chinese | 1507 | 1491 |
| LMArena Russian | 1474 | 1414 |
| LMArena French | 1481 | — |
| LMArena German | 1472 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.4 (#24), Mistral Large 4: 75.0 (#76)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Instruction Following | 1476 | 1424 |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), Mistral Large 4: 43.6 (#89)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Longer Query | 1483 | 1429 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), Mistral Large 4: 60.4 (#97)
| Benchmark | Claude Opus 4.8 | Mistral Large 4 |
|---|---|---|
| LMArena Text | 1461 | 1427 |
| LMArena Creative Writing | 1454 | 1361 |
| LMArena Multi-Turn | 1476 | 1424 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than Mistral Large 4?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 43.1 on the Noometry Index. Mistral Large 4 costs 9.7× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 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; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or Mistral Large 4 better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 48.6 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 4 does, with 1.05M tokens against 1M.
How many benchmarks do Claude Opus 4.8 and Mistral Large 4 share?
15 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Mistral Large 4 has 15.