Model comparison
Claude Opus 4.7 vs Claude Opus 4.7 (Adaptive)
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Sibling matchup inside the Claude Opus 4.7 family.
Public leaderboard positions: Claude Opus 4.7 #12 (Supported); Claude Opus 4.7 (Adaptive) #27 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 and Claude Opus 4.7 (Adaptive) share 14 comparable benchmark results. 0 of 8 categories are comparable. 7 results are unique to Claude Opus 4.7; 24 to Claude Opus 4.7 (Adaptive).
Updated July 23, 2026- Shared results
- 14
- Claude Opus 4.7 only
- 7
- Claude Opus 4.7 (Adaptive) only
- 24
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.7 and Claude Opus 4.7 (Adaptive) is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 14 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.
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 | Δ | Claude Opus 4.7 (Adaptive) |
|---|---|---|---|
| Agentic | Claude Opus 4.7Not measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)75.1 |
| Coding | Claude Opus 4.7Not measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)78.6 |
| Reasoning | Claude Opus 4.7Not measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)75.8 |
| Knowledge | Claude Opus 4.7Not measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)60.0 |
| Math | Claude Opus 4.738.6 | MarginNo overlap | Claude Opus 4.7 (Adaptive)Not measured |
| Multimodal | Claude Opus 4.7Not measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)65.1 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 | Claude Opus 4.7 (Adaptive) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7$5 input / $25 output | Claude Opus 4.7 (Adaptive)$5 input / $25 output | Listed prices are equal. |
| Generation speedtokens per second | Claude Opus 4.7Not available | Claude Opus 4.7 (Adaptive)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7Not available | Claude Opus 4.7 (Adaptive)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.71M | Claude Opus 4.7 (Adaptive)1M | Listed context windows are equal. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | Claude Opus 4.7 | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| τ²-bench resultsSource | 74% | 88.6% | Claude Opus 4.7 (Adaptive) leads |
| Gert LabsSource | 65.59% | — | Not comparable |
| ResearchClawBenchSource | 20.7% | — | Not comparable |
| OSWorld 2.0Source | 13.9% | 18.2% | Claude Opus 4.7 (Adaptive) leads |
| 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% | Not comparable |
| GDPval-AASource | — | 49.8% | Not comparable |
| GDPval-AASource | — | 1495 | Not comparable |
| JobBenchSource | — | 45.9% | Not comparable |
| AA ITBenchSource | — | 46.7% | Not comparable |
Coding8 benchmarks
| Benchmark | Claude Opus 4.7 | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| Vibe Code BenchSource | 71.00% | — | Not comparable |
| React Native EvalsSource | 82.8% | — | Not comparable |
| AA-SciCodeSource | 50.1% | 54.5% | Claude Opus 4.7 (Adaptive) leads |
| FrontierCode 1.1 MainSource | 38.5% | — | Not comparable |
| 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% | Not comparable |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Opus 4.7 | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 42.7% | 53.5% | Claude Opus 4.7 (Adaptive) leads |
| AA-GPQA DiamondSource | 88.5% | 91.4% | Claude Opus 4.7 (Adaptive) leads |
| AA-HLESource | 31.2% | 39.6% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience IndexSource | 14.2% | 26.2% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience AccuracySource | 43.5% | 45.8% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 51.9% | 36.2% | Claude Opus 4.7 (Adaptive) leads |
| GPQASource | — | 94.2% | Not comparable |
| GPQA-DSource | — | 94.2% | Not comparable |
| HLESource | — | 54.7% | Not comparable |
| HLE w/o toolsSource | — | 46.9% | Not comparable |
Math3 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.7 | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| AA-IFBenchSource | 43.6% | 58.6% | Claude Opus 4.7 (Adaptive) leads |
Frequently Asked Questions (3)
Can I compare Claude Opus 4.7 and Claude Opus 4.7 (Adaptive) 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 and Claude Opus 4.7 (Adaptive) today?
Claude Opus 4.7: $5.00 input / $25.00 output per 1M tokens Claude Opus 4.7 (Adaptive): $5.00 input / $25.00 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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