Model comparison
Claude Sonnet 4.6 vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.8 to 50.3 on the Noometry Index. Claude Sonnet 4.6 costs 3.3× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 44 shared benchmarks.
Summary
- They share 44 benchmarks with published results for both. Claude Sonnet 4.6 scores higher in 3 categories and GPT-6 Astra in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Astra leads 93.5 to 52.9.
- The biggest single-benchmark swing is Mystery Game Puzzles: 16% for Claude Sonnet 4.6 and 84% for GPT-6 Astra.
- Claude Sonnet 4.6 is cheaper at $3 / $15 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 1M.
Side by side
| Claude Sonnet 4.6 | GPT-6 Astra | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 50.3 | 70.8 |
| Released | 2026-02-17 | 2026-09-03 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $3 | $10 |
| Output $ / M tokens | $15 | $50 |
| Results tracked | 57 | 56 |
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Category by category
Coding GPT-6 Astra leads
Claude Sonnet 4.6: 46.3 (#67), GPT-6 Astra: 73.7 (#2)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| DeepSWE | 29.9% | 74.1% |
| FrontierCode | 24.3% | 53.3% |
| LMArena WebDev | 1522 | 1786 |
| SciCode | 46.8% | 56.5% |
| WeirdML | 66.1% | 93.6% |
| LMArena Coding | 1504 | 1487 |
| ALE-Bench | 1,327 | 2,951 |
| SWE-bench Verified | 75.2% | — |
| FrontierSWE | — | 65.5% |
| GSO | — | 79.4% |
| MirrorCode | — | 46.7% |
Agentic & Tool Use GPT-6 Astra leads
Claude Sonnet 4.6: 39.1 (#28), GPT-6 Astra: 52.9 (#3)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| APEX-Agents | 43% | 64.7% |
| GDP.pdf | 18% | 34.2% |
| Vending-Bench 2 | 7,204 | 15,515 |
| Terminal-Bench | 53.4% | — |
| OSWorld 2.0 | 9.3% | — |
| Remote Labor Index | — | 20.8% |
| DeepResearch Bench | 54.9% | — |
| OSWorld | 72.1% | — |
| BALROG | — | 68.3% |
| ExploitBench | 23.6% | — |
| GBAEval | 48.8% | — |
| LMArena Search | 1221 | — |
Reasoning GPT-6 Astra leads
Claude Sonnet 4.6: 46.1 (#45), GPT-6 Astra: 85.1 (#1)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| ARC-AGI-2 | 60.4% | 95% |
| NYT Connections (extended) | 80.9% | 98.1% |
| ARC-AGI-1 | 86.5% | 98.5% |
| CritPt | 3.1% | 31.7% |
| Chess Puzzles | 13% | 72% |
| LMArena Hard Prompts | 1484 | 1462 |
| Mystery Game Puzzles | 16% | 84% |
| DTBench | 89.9% | 97.3% |
| LMCA | 46.5% | 64.4% |
| Epoch Capabilities Index | 152.24 | 166.45 |
| Thematic Generalization | 76.3% | — |
| EBR-Bench | — | 76.2% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 62 | — |
Math GPT-6 Astra leads
Claude Sonnet 4.6: 52.9 (#49), GPT-6 Astra: 93.5 (#2)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 85.8% | 100% |
| ProofBench | 45% | 99% |
| LMArena Math | 1462 | 1465 |
| FrontierMath (Tiers 1-3) | — | 93.7% |
| FrontierMath Tier 4 | — | 97.6% |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Erdős | — | 2.9% |
| FrontierMath Tier 4 (v1) | 8.3% | — |
Knowledge GPT-6 Astra leads
Claude Sonnet 4.6: 51.7 (#65), GPT-6 Astra: 75.3 (#1)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| GPQA Diamond | 87.4% | 95.8% |
| SimpleQA Verified | 35.5% | 75.6% |
| Vectara Hallucination Rate | 10.6% | 8.7% |
| LMArena Expert | 1500 | 1483 |
| Humanity's Last Exam | — | 54.8% |
Multimodal GPT-6 Astra leads
Claude Sonnet 4.6: 38.0 (#68), GPT-6 Astra: 55.0 (#3)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| LMArena Vision | 1283 | 1281 |
| Blueprint-Bench 2 | 6.7% | 49.7% |
| LMArena Document | 1482 | 1468 |
| Furniture Assembly | — | 80% |
Multilingual Too close to call
Claude Sonnet 4.6: 54.4 (#41), GPT-6 Astra: 53.7 (#61)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1440 | 1430 |
| LMArena Chinese | 1491 | 1484 |
| LMArena French | 1465 | 1456 |
| LMArena German | 1428 | 1440 |
| LMArena Japanese | 1420 | 1379 |
| LMArena Korean | 1411 | 1426 |
| LMArena Russian | 1440 | 1436 |
| LMArena Spanish | 1464 | 1407 |
Instruction Following Claude Sonnet 4.6 leads
Claude Sonnet 4.6: 77.4 (#25), GPT-6 Astra: 76.3 (#44)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1475 | 1450 |
Long Context Too close to call
Claude Sonnet 4.6: 45.3 (#44), GPT-6 Astra: 44.5 (#62)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1479 | 1456 |
Writing & Preference GPT-6 Astra leads
Claude Sonnet 4.6: 70.2 (#22), GPT-6 Astra: 75.3 (#7)
| Benchmark | Claude Sonnet 4.6 | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1458 | 1441 |
| LMArena Creative Writing | 1435 | 1418 |
| EQ-Bench Creative Writing | 1810 | 2173 |
| LMArena Multi-Turn | 1464 | 1448 |
| EQ-Bench 4 | 1207 | — |
Frequently asked questions
Is Claude Sonnet 4.6 better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 50.3 on the Noometry Index. Claude Sonnet 4.6 costs 3.3× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, Claude Sonnet 4.6 or GPT-6 Astra?
Claude Sonnet 4.6 is cheaper. It lists at $3 per million input tokens and $15 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is Claude Sonnet 4.6 or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 46.3 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 1M.
How many benchmarks do Claude Sonnet 4.6 and GPT-6 Astra share?
44 benchmarks have published results for both models. Claude Sonnet 4.6 has 57 scored results on Noometry and GPT-6 Astra has 56.