Model comparison
Claude Opus 4.8 vs GPT-4.1 nano
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 27.9 on the Noometry Index. GPT-4.1 nano costs 57× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. Claude Opus 4.8 scores higher in 10 categories and GPT-4.1 nano in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Opus 4.8 leads 64.7 to 8.5.
- The biggest single-benchmark swing is ARC-AGI-1: 92.5% for Claude Opus 4.8 and 0% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Opus 4.8 | GPT-4.1 nano | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 60.7 | 27.9 |
| Released | 2026-05-28 | 2025-04-14 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 128K | 33K |
| Input $ / M tokens | $5 | $0.10 |
| Output $ / M tokens | $25 | $0.40 |
| Results tracked | 65 | 38 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), GPT-4.1 nano: 24.1 (#330)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| SciCode | 53.5% | 25.9% |
| WeirdML | 82.9% | 19% |
| LMArena Coding | 1490 | 1306 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1556 | — |
| GSO | 47.1% | — |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Claude Opus 4.8 leads
Claude Opus 4.8: 47.6 (#11), GPT-4.1 nano: 26.5 (#104)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| APEX-Agents | 48.9% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| 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), GPT-4.1 nano: 8.5 (#349)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 72.1% | 0% |
| Kagi LLM Benchmark | 88.8% | 33.3% |
| ARC-AGI-1 | 92.5% | 0% |
| CritPt | 20.9% | 0% |
| LMArena Hard Prompts | 1482 | 1286 |
| DTBench | 94.9% | 52.5% |
| LMCA | 57.5% | 5.5% |
| Epoch Capabilities Index | 158.21 | 129.62 |
| SimpleBench | 64.8% | — |
| NYT Connections (extended) | 91.1% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), GPT-4.1 nano: 26.9 (#252)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 98.3% | 28.9% |
| LMArena Math | 1487 | 1274 |
| FrontierMath (Feb 2025 set) | 47.2% | 1% |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| ProofBench | 69% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), GPT-4.1 nano: 21.8 (#273)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 91% | 48.9% |
| SimpleQA Verified | 53% | 6% |
| LMArena Expert | 1502 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
Multimodal Claude Opus 4.8 leads
Claude Opus 4.8: 42.9 (#26), GPT-4.1 nano: 29.2 (#113)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | 1294 | 1063 |
| 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), GPT-4.1 nano: 41.6 (#205)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1450 | 1260 |
| LMArena Chinese | 1507 | 1270 |
| LMArena German | 1472 | 1288 |
| LMArena Japanese | 1440 | 1198 |
| LMArena Russian | 1474 | 1261 |
| LMArena French | 1481 | — |
| LMArena Korean | 1432 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.4 (#24), GPT-4.1 nano: 67.8 (#193)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1476 | 1267 |
| IFEval | — | 84.3% |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), GPT-4.1 nano: 23.7 (#296)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1483 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), GPT-4.1 nano: 40.5 (#243)
| Benchmark | Claude Opus 4.8 | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1461 | 1285 |
| LMArena Creative Writing | 1454 | 1260 |
| EQ-Bench Creative Writing | 1840 | 946 |
| LMArena Multi-Turn | 1476 | 1277 |
| WildBench | — | 81.2% |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GPT-4.1 nano?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 27.9 on the Noometry Index. GPT-4.1 nano costs 57× 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 GPT-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or GPT-4.1 nano better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 1M.
How many benchmarks do Claude Opus 4.8 and GPT-4.1 nano share?
29 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GPT-4.1 nano has 38.