Model comparison
Claude Opus 4.8 vs Mistral Nemo
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 26.4 on the Noometry Index. Mistral Nemo costs 67× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Mistral AISummary
- They share 4 benchmarks with published results for both. Claude Opus 4.8 scores higher in 5 categories and Mistral Nemo in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.4 to 25.5.
- The biggest single-benchmark swing is GPQA Diamond: 91% for Claude Opus 4.8 and 29.9% for Mistral Nemo.
- Mistral Nemo is cheaper at $0.15 / $0.15 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 128K.
- Mistral Nemo has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.8 | Mistral Nemo | |
|---|---|---|
| Provider | Anthropic | Mistral AI |
| Noometry Index | 60.7 | 26.4 |
| Released | 2026-05-28 | 2024-07-01 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 128K | 128K |
| Input $ / M tokens | $5 | $0.15 |
| Output $ / M tokens | $25 | $0.15 |
| Results tracked | 65 | 10 |
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Category by category
Coding Not comparable
Claude Opus 4.8: 59.9 (#12), Mistral Nemo: —
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| LMArena Coding | 1490 | — |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#11), Mistral Nemo: 23.5 (#125)
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| APEX-Agents | 48.9% | — |
| Berkeley Function Calling Leaderboard | — | 27.6% |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| BALROG | — | 17.6% |
| 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 Nemo: 20.7 (#232)
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| DTBench | 94.9% | 48.6% |
| Epoch Capabilities Index | 158.21 | 118.68 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| LMArena Hard Prompts | 1482 | — |
| Mystery Game Puzzles | 36% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 59.9 | — |
| PIQA | — | 83.5% |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), Mistral Nemo: 25.5 (#268)
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| 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% | — |
| LMArena Math | 1487 | — |
| MATH Level 5 | — | 10.8% |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
| GSM8K | — | 84.2% |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), Mistral Nemo: 12.3 (#298)
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| GPQA Diamond | 91% | 29.9% |
| SimpleQA Verified | 53% | — |
| LMArena Expert | 1502 | — |
| BoolQ | — | 82.5% |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), Mistral Nemo: —
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Not comparable
Claude Opus 4.8: 55.2 (#33), Mistral Nemo: —
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| LMArena Non-English | 1450 | — |
| LMArena Chinese | 1507 | — |
| LMArena French | 1481 | — |
| LMArena German | 1472 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |
| LMArena Russian | 1474 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Not comparable
Claude Opus 4.8: 77.4 (#24), Mistral Nemo: —
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| LMArena Instruction Following | 1476 | — |
Long Context Not comparable
Claude Opus 4.8: 45.4 (#35), Mistral Nemo: —
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| LMArena Longer Query | 1483 | — |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), Mistral Nemo: 28.5 (#296)
| Benchmark | Claude Opus 4.8 | Mistral Nemo |
|---|---|---|
| EQ-Bench Creative Writing | 1840 | 881 |
| LMArena Text | 1461 | — |
| LMArena Creative Writing | 1454 | — |
| EQ-Bench 4 | 1281 | — |
| LMArena Multi-Turn | 1476 | — |
Frequently asked questions
Is Claude Opus 4.8 better than Mistral Nemo?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 26.4 on the Noometry Index. Mistral Nemo costs 67× 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 Nemo?
Mistral Nemo is cheaper. It lists at $0.15 per million input tokens and $0.15 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 128K.
How many benchmarks do Claude Opus 4.8 and Mistral Nemo share?
4 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and Mistral Nemo has 10.