Model comparison
Claude Opus 4.7 (Adaptive) vs GPT-5.1
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-5.1 #94 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-5.1 share 17 comparable benchmark results. 0 of 8 categories are comparable. 21 results are unique to Claude Opus 4.7 (Adaptive); 4 to GPT-5.1.
Updated July 23, 2026- Shared results
- 17
- Claude Opus 4.7 (Adaptive) only
- 21
- GPT-5.1 only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.7 (Adaptive) and GPT-5.1 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Claude Opus 4.7 (Adaptive) is priced at $5.00 input / $25.00 output per 1M tokens, versus $1.25 input / $10.00 output per 1M tokens for GPT-5.1. Claude Opus 4.7 (Adaptive) has the larger context window at 1M, compared with 200K for GPT-5.1.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude Opus 4.7 (Adaptive) | Δ | GPT-5.1 |
|---|---|---|---|
| Agentic | Claude Opus 4.7 (Adaptive)75.1 | MarginNo overlap | GPT-5.1Not measured |
| Coding | Claude Opus 4.7 (Adaptive)78.6 | MarginNo overlap | GPT-5.1Not measured |
| Reasoning | Claude Opus 4.7 (Adaptive)75.8 | MarginNo overlap | GPT-5.1Not measured |
| Knowledge | Claude Opus 4.7 (Adaptive)60.0 | MarginNo overlap | GPT-5.1Not measured |
| Math | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | GPT-5.126.4 |
| Multimodal | Claude Opus 4.7 (Adaptive)65.1 | MarginNo overlap | GPT-5.1Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 (Adaptive) | GPT-5.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7 (Adaptive)$5 input / $25 output | GPT-5.1$1.25 input / $10 output | GPT-5.1 has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.7 (Adaptive)Not available | GPT-5.1111 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7 (Adaptive)Not available | GPT-5.157.47 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.7 (Adaptive)1M | GPT-5.1200K | Claude Opus 4.7 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 69.4% | — | Not comparable |
| BrowseCompSource | 79.3% | — | Not comparable |
| MCP AtlasSource | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 78% | — | Not comparable |
| CyberGymSource | 73.1% | — | Not comparable |
| AA Agentic IndexSource | 44.4% | 21.0% | Claude Opus 4.7 (Adaptive) leads |
| τ²-bench resultsSource | 88.6% | 81.9% | Claude Opus 4.7 (Adaptive) leads |
| GDPval-AASource | 49.8% | 24.4% | Claude Opus 4.7 (Adaptive) leads |
| GDPval-AASource | 1495 | 987 | Claude Opus 4.7 (Adaptive) leads |
| OSWorld 2.0Source | 18.2% | — | Not comparable |
| JobBenchSource | 45.9% | — | Not comparable |
| AA ITBenchSource | 46.7% | — | Not comparable |
| Gert LabsSource | — | 41.24% | Not comparable |
Coding6 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.1 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 87.6% | — | Not comparable |
| SWE-bench ProSource | 64.3% | — | Not comparable |
| Terminal-Bench 2.0Source | 69.4% | — | Not comparable |
| AA Coding IndexSource | 73.6% | 49.4% | Claude Opus 4.7 (Adaptive) leads |
| AA-SciCodeSource | 54.5% | 43.3% | Claude Opus 4.7 (Adaptive) leads |
| Vibe Code BenchSource | — | 24.61% | Not comparable |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.1 | Result |
|---|---|---|---|
| GPQASource | 94.2% | — | Not comparable |
| GPQA-DSource | 94.2% | — | Not comparable |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 46.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 53.5% | 36.9% | Claude Opus 4.7 (Adaptive) leads |
| AA-GPQA DiamondSource | 91.4% | 87.3% | Claude Opus 4.7 (Adaptive) leads |
| AA-HLESource | 39.6% | 26.5% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience IndexSource | 26.2% | 5.6% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience AccuracySource | 45.8% | 37.6% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 36.2% | 51.3% | Claude Opus 4.7 (Adaptive) leads |
Math3 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.1 | Result |
|---|---|---|---|
| AA-IFBenchSource | 58.6% | 72.9% | GPT-5.1 leads |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.7 (Adaptive) and GPT-5.1 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Claude Opus 4.7 (Adaptive) and GPT-5.1 today?
Claude Opus 4.7 (Adaptive): $5.00 input / $25.00 output per 1M tokens GPT-5.1: $1.25 input / $10.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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