Model comparison
Claude Opus 4 vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.8 to 43.1 on the Noometry Index.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. Claude Opus 4 scores higher in 1 category and GPT-6 Astra in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 27.3.
- The biggest single-benchmark swing is ARC-AGI-2: 8.6% for Claude Opus 4 and 95% for GPT-6 Astra.
- GPT-6 Astra is cheaper at $10 / $50 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- GPT-6 Astra accepts more context: 1.05M tokens versus 200K.
Side by side
| Claude Opus 4 | GPT-6 Astra | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 43.1 | 70.8 |
| Released | 2025-05-22 | 2026-09-03 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 32K | 128K |
| Input $ / M tokens | $15 | $10 |
| Output $ / M tokens | $75 | $50 |
| Results tracked | 56 | 56 |
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Category by category
Coding GPT-6 Astra leads
Claude Opus 4: 47.2 (#62), GPT-6 Astra: 73.7 (#2)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| GSO | 6.9% | 79.4% |
| WeirdML | 43.7% | 93.6% |
| LMArena Coding | 1442 | 1487 |
| SWE-bench Verified | 70.7% | — |
| DeepSWE | — | 74.1% |
| FrontierCode | — | 53.3% |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| LMArena WebDev | — | 1786 |
| FrontierSWE | — | 65.5% |
| SciCode | — | 56.5% |
| MirrorCode | — | 46.7% |
| ALE-Bench | — | 2,951 |
| AlgoTune | 1.33 | — |
Agentic & Tool Use GPT-6 Astra leads
Claude Opus 4: 34.8 (#42), GPT-6 Astra: 52.9 (#3)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| APEX-Agents | — | 64.7% |
| Remote Labor Index | — | 20.8% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| BALROG | — | 68.3% |
| GDP.pdf | — | 34.2% |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
| Vending-Bench 2 | — | 15,515 |
Reasoning GPT-6 Astra leads
Claude Opus 4: 27.3 (#121), GPT-6 Astra: 85.1 (#1)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| ARC-AGI-2 | 8.6% | 95% |
| ARC-AGI-1 | 35.7% | 98.5% |
| CritPt | 0.3% | 31.7% |
| LMArena Hard Prompts | 1399 | 1462 |
| DTBench | 81.6% | 97.3% |
| LMCA | 37.4% | 64.4% |
| Epoch Capabilities Index | 142.67 | 166.45 |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 74.3% | — |
| NYT Connections (extended) | — | 98.1% |
| Chess Puzzles | — | 72% |
| EnigmaEval | 5.6% | — |
| EBR-Bench | — | 76.2% |
| Mystery Game Puzzles | — | 84% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 61.1 | — |
Math GPT-6 Astra leads
Claude Opus 4: 42.0 (#86), GPT-6 Astra: 93.5 (#2)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | 100% |
| LMArena Math | 1390 | 1465 |
| FrontierMath (Tiers 1-3) | — | 93.7% |
| FrontierMath Tier 4 | — | 97.6% |
| ProofBench | — | 99% |
| Omni-MATH | 61.6% | — |
| MATH Level 5 | 85% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Erdős | — | 2.9% |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GPT-6 Astra leads
Claude Opus 4: 44.0 (#88), GPT-6 Astra: 75.3 (#1)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| GPQA Diamond | 76.3% | 95.8% |
| Humanity's Last Exam | 10.7% | 54.8% |
| Vectara Hallucination Rate | 12% | 8.7% |
| LMArena Expert | 1386 | 1483 |
| SimpleQA Verified | — | 75.6% |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| GPQA (HELM) | 70.8% | — |
Multimodal GPT-6 Astra leads
Claude Opus 4: 31.5 (#106), GPT-6 Astra: 55.0 (#3)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| LMArena Vision | 1192 | 1281 |
| GeoBench | 49% | — |
| VPCT | 38% | — |
| Blueprint-Bench 2 | — | 49.7% |
| Furniture Assembly | — | 80% |
| LMArena Document | — | 1468 |
Multilingual GPT-6 Astra leads
Claude Opus 4: 48.8 (#138), GPT-6 Astra: 53.7 (#61)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1362 | 1430 |
| LMArena Chinese | 1386 | 1484 |
| LMArena French | 1372 | 1456 |
| LMArena German | 1391 | 1440 |
| LMArena Japanese | 1331 | 1379 |
| LMArena Korean | 1321 | 1426 |
| LMArena Russian | 1392 | 1436 |
| LMArena Spanish | 1389 | 1407 |
Instruction Following Too close to call
Claude Opus 4: 77.1 (#28), GPT-6 Astra: 76.3 (#44)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1406 | 1450 |
| IFEval | 91.8% | — |
Long Context GPT-6 Astra leads
Claude Opus 4: 39.6 (#172), GPT-6 Astra: 44.5 (#62)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1422 | 1456 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference GPT-6 Astra leads
Claude Opus 4: 61.2 (#89), GPT-6 Astra: 75.3 (#7)
| Benchmark | Claude Opus 4 | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1377 | 1441 |
| LMArena Creative Writing | 1387 | 1418 |
| EQ-Bench Creative Writing | 1580 | 2173 |
| LMArena Multi-Turn | 1396 | 1448 |
| Short-Story Creative Writing | 83.6% | — |
| WildBench | 85.2% | — |
Frequently asked questions
Is Claude Opus 4 better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 43.1 on the Noometry Index.
Which is cheaper, Claude Opus 4 or GPT-6 Astra?
GPT-6 Astra is cheaper. It lists at $10 per million input tokens and $50 per million output tokens; Claude Opus 4 lists at $15 and $75.
Is Claude Opus 4 or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 47.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 200K.
How many benchmarks do Claude Opus 4 and GPT-6 Astra share?
31 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and GPT-6 Astra has 56.