Model comparison
Claude Opus 4.6 vs Pixtral Large
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 32.2 on the Noometry Index. Pixtral Large costs 3.3× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Mistral AISummary
- They share 3 benchmarks with published results for both. Claude Opus 4.6 scores higher in 3 categories and Pixtral Large in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Claude Opus 4.6 leads 73.5 to 32.9.
- The biggest single-benchmark swing is EnigmaEval: 7.6% for Claude Opus 4.6 and 0.8% for Pixtral Large.
- Pixtral Large is cheaper at $2 / $6 per million input/output tokens, against $5 / $25 for Claude Opus 4.6.
- Claude Opus 4.6 accepts more context: 1M tokens versus 128K.
- Pixtral Large has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.6 | Pixtral Large | |
|---|---|---|
| Provider | Anthropic | Mistral AI |
| Noometry Index | 58.2 | 32.2 |
| Released | 2026-02-04 | 2024-11-01 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 128K | 128K |
| Input $ / M tokens | $5 | $2 |
| Output $ / M tokens | $25 | $6 |
| Results tracked | 68 | 3 |
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Category by category
Coding Not comparable
Claude Opus 4.6: 57.2 (#20), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| FrontierCode | 26.6% | — |
| SWE-bench Verified (bash only) | 75.6% | — |
| LMArena WebDev | 1547 | — |
| SWE-bench Multilingual | 72% | — |
| GSO | 41.2% | — |
| WeirdML | 78% | — |
| LMArena Coding | 1536 | — |
| ALE-Bench | 996.5 | — |
| AlgoTune | 1.47 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.6: 51.1 (#4), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| Terminal-Bench | 79.8% | — |
| APEX-Agents | 46.3% | — |
| Remote Labor Index | 4.2% | — |
| τ²-bench Banking | 27.3% | — |
| Cybench | 93% | — |
| DeepResearch Bench | 55.3% | — |
| GBAEval | 44.1% | — |
| LMArena Search | 1253 | — |
| METR Time Horizons | 78.9% | — |
| Vending-Bench 2 | 8,018 | — |
Reasoning Claude Opus 4.6 leads
Claude Opus 4.6: 57.8 (#23), Pixtral Large: 21.7 (#218)
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| EnigmaEval | 7.6% | 0.8% |
| ARC-AGI-2 | 69.2% | — |
| SimpleBench | 67.6% | — |
| Kagi LLM Benchmark | 83.6% | — |
| NYT Connections (extended) | 92.1% | — |
| ARC-AGI-1 | 94% | — |
| Chess Puzzles | 17% | — |
| Thematic Generalization | 80.6% | — |
| EBR-Bench | 12.7% | — |
| LMArena Hard Prompts | 1527 | — |
| Mystery Game Puzzles | 25% | — |
| DTBench | 91.2% | — |
| LMCA | 55.8% | — |
| Epoch Capabilities Index | 155.24 | — |
| ForecastBench | 60 | — |
Math Not comparable
Claude Opus 4.6: 63.0 (#31), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 66% | — |
| FrontierMath Tier 4 | 26.8% | — |
| MathArena Final-Answer Competitions | 78.5% | — |
| OTIS Mock AIME 2024-2025 | 94.4% | — |
| ProofBench | 50% | — |
| LMArena Math | 1519 | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Not comparable
Claude Opus 4.6: 61.9 (#26), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| GPQA Diamond | 90.5% | — |
| Humanity's Last Exam | 34.4% | — |
| SimpleQA Verified | 47% | — |
| Vectara Hallucination Rate | 12.2% | — |
| LMArena Expert | 1546 | — |
Multimodal Claude Opus 4.6 leads
Claude Opus 4.6: 37.3 (#74), Pixtral Large: 30.6 (#111)
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| LMArena Vision | 1316 | 1089 |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1507 | — |
Multilingual Not comparable
Claude Opus 4.6: 57.9 (#6), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1489 | — |
| LMArena Chinese | 1551 | — |
| LMArena French | 1513 | — |
| LMArena German | 1502 | — |
| LMArena Japanese | 1484 | — |
| LMArena Korean | 1464 | — |
| LMArena Russian | 1497 | — |
| LMArena Spanish | 1510 | — |
Instruction Following Not comparable
Claude Opus 4.6: 79.5 (#4), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1523 | — |
Long Context Not comparable
Claude Opus 4.6: 48.1 (#13), Pixtral Large: —
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| CL-bench | 20.7% | — |
| CL-bench Life | 17% | — |
| LMArena Longer Query | 1520 | — |
Writing & Preference Claude Opus 4.6 leads
Claude Opus 4.6: 73.5 (#10), Pixtral Large: 32.9 (#278)
| Benchmark | Claude Opus 4.6 | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1809 | 988 |
| LMArena Text | 1503 | — |
| LMArena Creative Writing | 1505 | — |
| EQ-Bench 4 | 1223 | — |
| LMArena Multi-Turn | 1513 | — |
Frequently asked questions
Is Claude Opus 4.6 better than Pixtral Large?
Claude Opus 4.6 is the stronger model overall, scoring 58.2 to 32.2 on the Noometry Index. Pixtral Large costs 3.3× less per token, which makes it the better buy when Claude Opus 4.6's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.6 or Pixtral Large?
Pixtral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; Claude Opus 4.6 lists at $5 and $25.
Which has the bigger context window?
Claude Opus 4.6 does, with 1M tokens against 128K.
How many benchmarks do Claude Opus 4.6 and Pixtral Large share?
3 benchmarks have published results for both models. Claude Opus 4.6 has 68 scored results on Noometry and Pixtral Large has 3.