Model comparison
Claude Opus 4.7 (Adaptive) vs GPT-5.6 Luna
Head-to-head evidence from 24 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-5.6 Luna #22 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-5.6 Luna share 24 comparable benchmark results. 4 of 8 categories are comparable. 14 results are unique to Claude Opus 4.7 (Adaptive); 17 to GPT-5.6 Luna.
Updated July 23, 2026- Shared results
- 24
- Claude Opus 4.7 (Adaptive) only
- 14
- GPT-5.6 Luna only
- 17
- Comparable categories
- 4 / 8
Pick GPT-5.6 Luna if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if coding is the priority.
Confidence note. This is a partial-evidence comparison with 24 shared benchmark results across 6 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.6 Luna has the cleaner BenchAlign overall profile here, landing at 67.17 versus 66.27. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.6 Luna's sharpest advantage is in knowledge, where it averages 92.3 against 60. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 84.7%. Claude Opus 4.7 (Adaptive) does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.00 input / $6.00 output per 1M tokens for GPT-5.6 Luna. That is roughly 4.2x on output cost alone.
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-5.6 Luna |
|---|---|---|---|
| Knowledge | Claude Opus 4.7 (Adaptive)60.0 | Margin→ 32.3 | GPT-5.6 Luna92.3 |
| Coding | Claude Opus 4.7 (Adaptive)78.6 | Margin← 15.9 | GPT-5.6 Luna62.7 |
| Multimodal | Claude Opus 4.7 (Adaptive)65.1 | Margin→ 13.3 | GPT-5.6 Luna78.4 |
| Agentic | Claude Opus 4.7 (Adaptive)75.1 | Margin→ 9.0 | GPT-5.6 Luna84.1 |
| Reasoning | Claude Opus 4.7 (Adaptive)75.8 | MarginNo overlap | GPT-5.6 LunaNot measured |
| Math | Claude Opus 4.7 (Adaptive)Not measured | MarginNo overlap | GPT-5.6 Luna73.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 69.4%B 84.7%Winner: GPT-5.6 LunaΔ 15.3Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GPT-5.6 Luna scored 84.7%. GPT-5.6 Luna wins this benchmark. - Source ↗
BrowseComp
AgenticA 79.3%B 83.3%Winner: GPT-5.6 LunaΔ 4BrowseComp: Claude Opus 4.7 (Adaptive) scored 79.3%; GPT-5.6 Luna scored 83.3%. GPT-5.6 Luna wins this benchmark. - Source ↗
GPQA
KnowledgeA 94.2%B 92.3%Winner: Claude Opus 4.7 (Adaptive)Δ 1.9GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GPT-5.6 Luna scored 92.3%. Claude Opus 4.7 (Adaptive) wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.3%B 62.7%Winner: Claude Opus 4.7 (Adaptive)Δ 1.6SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GPT-5.6 Luna scored 62.7%. Claude Opus 4.7 (Adaptive) wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.7 (Adaptive)$5 input / $25 output | GPT-5.6 Luna$1 input / $6 output | GPT-5.6 Luna has the lower combined listed price. |
| Generation speedtokens per second | Claude Opus 4.7 (Adaptive)Not available | GPT-5.6 LunaNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.7 (Adaptive)Not available | GPT-5.6 LunaNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.7 (Adaptive)1M | GPT-5.6 Luna1M | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGPT-5.6 Luna wins19 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 69.4% | 84.7% | GPT-5.6 Luna leads |
| BrowseCompSource | 79.3% | 83.3% | GPT-5.6 Luna leads |
| MCP AtlasSource | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 78% | — | Not comparable |
| CyberGymSource | 73.1% | 77.9% | GPT-5.6 Luna leads |
| AA Agentic IndexSource | 44.4% | 45.6% | GPT-5.6 Luna leads |
| τ²-bench resultsSource | 88.6% | — | Not comparable |
| GDPval-AASource | 49.8% | 54.2% | GPT-5.6 Luna leads |
| GDPval-AASource | 1495 | 1584 | GPT-5.6 Luna leads |
| OSWorld 2.0Source | 18.2% | 45.6% | GPT-5.6 Luna leads |
| JobBenchSource | 45.9% | — | Not comparable |
| AA ITBenchSource | 46.7% | 40.3% | Claude Opus 4.7 (Adaptive) leads |
| ExploitGymSource | — | 12.4% | Not comparable |
| ToolathlonSource | — | 53.4% | Not comparable |
| AA Harvey LABSource | — | 87.9% | Not comparable |
| AA Tau3 BankingSource | — | 27.2% | Not comparable |
| AA AutomationBenchSource | — | 42.2% | Not comparable |
| aaTerminalBench21Source | — | 80.9% | Not comparable |
| APEX-Agents-AASource | — | 35.8% | Not comparable |
CodingClaude Opus 4.7 (Adaptive) wins8 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 87.6% | — | Not comparable |
| SWE-bench ProSource | 64.3% | 62.7% | Claude Opus 4.7 (Adaptive) leads |
| Terminal-Bench 2.0Source | 69.4% | 84.7% | GPT-5.6 Luna leads |
| AA Coding IndexSource | 73.6% | 71.5% | Claude Opus 4.7 (Adaptive) leads |
| AA-SciCodeSource | 54.5% | 52.5% | Claude Opus 4.7 (Adaptive) leads |
| deepSweSource | — | 67.2% | Not comparable |
| FrontierCode 1.1 ExtendedSource | — | 55.1% | Not comparable |
| cursorBench32Source | — | 61.1% | Not comparable |
Reasoning5 benchmarks
KnowledgeGPT-5.6 Luna wins12 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Result |
|---|---|---|---|
| GPQASource | 94.2% | 92.3% | Claude Opus 4.7 (Adaptive) leads |
| GPQA-DSource | 94.2% | 92.3% | Claude Opus 4.7 (Adaptive) leads |
| HLESource | 54.7% | — | Not comparable |
| HLE w/o toolsSource | 46.9% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 53.5% | 51.2% | Claude Opus 4.7 (Adaptive) leads |
| AA-GPQA DiamondSource | 91.4% | 91.1% | Claude Opus 4.7 (Adaptive) leads |
| AA-HLESource | 39.6% | 37.2% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience IndexSource | 26.2% | -11.2% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience AccuracySource | 45.8% | 41.5% | Claude Opus 4.7 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 36.2% | 90.1% | Claude Opus 4.7 (Adaptive) leads |
| HealthBench ProfessionalSource | — | 55.7% | Not comparable |
| HealthBench HardSource | — | 32.0% | Not comparable |
Math3 benchmarks
MultimodalGPT-5.6 Luna wins7 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Result |
|---|---|---|---|
| OfficeQA ProSource | 43.6% | — | Not comparable |
| CharXivSource | 91% | — | Not comparable |
| CharXiv w/o toolsSource | 82.1% | — | Not comparable |
| AA-MMMU-ProSource | 78.8% | 78.6% | Claude Opus 4.7 (Adaptive) leads |
| Design Arena WebsiteSource | 1325 | — | Not comparable |
| MMMU-ProSource | — | 78.4% | Not comparable |
| MMMU-Pro w/ PythonSource | — | 79.5% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.7 (Adaptive) | GPT-5.6 Luna | Result |
|---|---|---|---|
| AA-IFBenchSource | 58.6% | — | Not comparable |
Frequently Asked Questions (5)
Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.6 Luna?
GPT-5.6 Luna is ahead on BenchLM's BenchAlign leaderboard, 67.17 to 66.27. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 84.7%.
Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for knowledge tasks in this comparison, averaging 92.3 versus 60. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, Claude Opus 4.7 (Adaptive) or GPT-5.6 Luna?
Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 62.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for agentic tasks in this comparison, averaging 84.1 versus 75.1. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Which is better for multimodal and grounded tasks, Claude Opus 4.7 (Adaptive) or GPT-5.6 Luna?
GPT-5.6 Luna has the edge for multimodal and grounded tasks in this comparison, averaging 78.4 versus 65.1. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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