Model comparison
Claude Opus 4.7 vs GPT-5 Pro
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 46.4 on the Noometry Index.
Last verified . 10 shared benchmarks.
Summary
- They share 10 benchmarks with published results for both. Claude Opus 4.7 scores higher in 4 categories and GPT-5 Pro in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.7 leads 66.7 to 48.5.
- The biggest single-benchmark swing is ARC-AGI-2: 75.8% for Claude Opus 4.7 and 18.3% for GPT-5 Pro.
- Claude Opus 4.7 is cheaper at $5 / $25 per million input/output tokens, against $15 / $120 for GPT-5 Pro.
- Claude Opus 4.7 accepts more context: 1M tokens versus 400K.
Side by side
| Claude Opus 4.7 | GPT-5 Pro | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 58.3 | 46.4 |
| Released | 2026-04-14 | 2025-10-06 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 400K |
| Max output | 128K | 272K |
| Input $ / M tokens | $5 | $15 |
| Output $ / M tokens | $25 | $120 |
| Results tracked | 66 | 12 |
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Category by category
Coding Claude Opus 4.7 leads
Claude Opus 4.7: 59.6 (#13), GPT-5 Pro: 44.0 (#80)
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| WeirdML | 76.4% | 60.4% |
| SWE-bench Verified | 83.5% | — |
| FrontierCode | 38.5% | — |
| LMArena WebDev | 1558 | — |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| LMArena Coding | 1518 | — |
| MirrorCode | 31.1% | — |
| ALE-Bench | 1,323 | — |
| AlgoTune | — | 1.31 |
Agentic & Tool Use Not comparable
Claude Opus 4.7: 47.9 (#10), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |
Reasoning Claude Opus 4.7 leads
Claude Opus 4.7: 53.8 (#29), GPT-5 Pro: 38.9 (#62)
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| ARC-AGI-2 | 75.8% | 18.3% |
| SimpleBench | 61.7% | 61.6% |
| Kagi LLM Benchmark | 80.7% | 76.8% |
| ARC-AGI-1 | 93.5% | 70.2% |
| Epoch Capabilities Index | 156.25 | 150.28 |
| NYT Connections (extended) | 39% | — |
| CritPt | 12% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | — | 18.8% |
| Thematic Generalization | 72.8% | — |
| EBR-Bench | 19% | — |
| LMArena Hard Prompts | 1506 | — |
| Mystery Game Puzzles | 28% | — |
| DTBench | 94.7% | — |
| LMCA | 52.2% | — |
| ForecastBench | 60.3 | — |
Math Claude Opus 4.7 leads
Claude Opus 4.7: 66.7 (#26), GPT-5 Pro: 48.5 (#63)
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| FrontierMath (Tiers 1-3) | 70.2% | 55.8% |
| FrontierMath Tier 4 | 31.7% | 19.5% |
| FrontierMath Tier 4 (v1) | 22.9% | 14.6% |
| MathArena Final-Answer Competitions | 73.6% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 54% | — |
| LMArena Math | 1499 | — |
| FrontierMath (Feb 2025 set) | 43.8% | — |
Knowledge Claude Opus 4.7 leads
Claude Opus 4.7: 62.6 (#23), GPT-5 Pro: 56.7 (#42)
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| Humanity's Last Exam | 36.2% | 31.6% |
| GPQA Diamond | 90.2% | — |
| SimpleQA Verified | 51.7% | — |
| Vectara Hallucination Rate | 12% | — |
| LMArena Expert | 1521 | — |
Multimodal Not comparable
Claude Opus 4.7: 41.2 (#38), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| LMArena Vision | 1316 | — |
| Blueprint-Bench 2 | 24.5% | — |
| Furniture Assembly | 33.3% | — |
| LMArena Document | 1495 | — |
Multilingual Not comparable
Claude Opus 4.7: 57.3 (#10), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| LMArena Non-English | 1480 | — |
| LMArena Chinese | 1531 | — |
| LMArena French | 1503 | — |
| LMArena German | 1495 | — |
| LMArena Japanese | 1472 | — |
| LMArena Korean | 1464 | — |
| LMArena Russian | 1494 | — |
| LMArena Spanish | 1495 | — |
Instruction Following Not comparable
Claude Opus 4.7: 78.4 (#10), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| LMArena Instruction Following | 1498 | — |
Long Context Not comparable
Claude Opus 4.7: 46.2 (#25), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| LMArena Longer Query | 1505 | — |
Writing & Preference Not comparable
Claude Opus 4.7: 75.1 (#8), GPT-5 Pro: —
| Benchmark | Claude Opus 4.7 | GPT-5 Pro |
|---|---|---|
| LMArena Text | 1490 | — |
| LMArena Creative Writing | 1486 | — |
| EQ-Bench Creative Writing | 1914 | — |
| EQ-Bench 4 | 1311 | — |
| LMArena Multi-Turn | 1505 | — |
Frequently asked questions
Is Claude Opus 4.7 better than GPT-5 Pro?
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 46.4 on the Noometry Index.
Which is cheaper, Claude Opus 4.7 or GPT-5 Pro?
Claude Opus 4.7 is cheaper. It lists at $5 per million input tokens and $25 per million output tokens; GPT-5 Pro lists at $15 and $120.
Is Claude Opus 4.7 or GPT-5 Pro better for coding?
Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.7 does, with 1M tokens against 400K.
How many benchmarks do Claude Opus 4.7 and GPT-5 Pro share?
10 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and GPT-5 Pro has 12.