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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-5.6 Sol

Data verified

Head-to-head evidence from 26 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
15.7pts
winning →
81.96/100
1 category wins3 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-5.6 Sol #3 (Supported). 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 Sol share 26 comparable benchmark results. 4 of 8 categories are comparable. 12 results are unique to Claude Opus 4.7 (Adaptive); 20 to GPT-5.6 Sol.

Updated July 23, 2026
Shared results
26
Claude Opus 4.7 (Adaptive) only
12
GPT-5.6 Sol only
20
Comparable categories
4 / 8

Pick GPT-5.6 Sol if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if coding is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 26 shared benchmark results across 7 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 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 66.27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.6 Sol's sharpest advantage is in knowledge, where it averages 94.6 against 60. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 91.9%. 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.

GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $5.00 input / $25.00 output per 1M tokens for Claude Opus 4.7 (Adaptive).

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 scores and score margins for Claude Opus 4.7 (Adaptive) and GPT-5.6 Sol
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-5.6 Sol
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin→ 34.6GPT-5.6 Sol94.6
MultimodalClaude Opus 4.7 (Adaptive)65.1Margin→ 17.9GPT-5.6 Sol83.0
AgenticClaude Opus 4.7 (Adaptive)75.1Margin→ 16.9GPT-5.6 Sol92.0
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 14.0GPT-5.6 Sol64.6
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGPT-5.6 SolNot measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-5.6 Sol87.5

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Claude Opus 4.7 (Adaptive)B · GPT-5.6 Sol

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 91.9%
    Winner: GPT-5.6 SolΔ 22.5
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GPT-5.6 Sol scored 91.9%. GPT-5.6 Sol wins this benchmark.

  2. BrowseComp

    Agentic
    Source ↗
    A 79.3%B 92.2%
    Winner: GPT-5.6 SolΔ 12.9
    BrowseComp: Claude Opus 4.7 (Adaptive) scored 79.3%; GPT-5.6 Sol scored 92.2%. GPT-5.6 Sol wins this benchmark.

  3. GPQA

    Knowledge
    Source ↗
    A 94.2%B 94.6%
    Winner: GPT-5.6 SolΔ 0.4
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; GPT-5.6 Sol scored 94.6%. GPT-5.6 Sol wins this benchmark.

  4. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 64.6%
    Winner: GPT-5.6 SolΔ 0.3
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GPT-5.6 Sol scored 64.6%. GPT-5.6 Sol wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.7 (Adaptive)GPT-5.6 SolComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-5.6 Sol$5 input / $30 outputClaude Opus 4.7 (Adaptive) has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-5.6 SolNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-5.6 SolNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-5.6 Sol1MListed context windows are equal.

Benchmark Deep Dive

AgenticGPT-5.6 Sol wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
Terminal-Bench 2.0Source 69.4%91.9%GPT-5.6 Sol leads
BrowseCompSource 79.3%92.2%GPT-5.6 Sol leads
MCP AtlasSource 77.3%—Not comparable
OSWorld-VerifiedSource 78%—Not comparable
CyberGymSource 73.1%84.5%GPT-5.6 Sol leads
AA Agentic IndexSource 44.4%54.0%GPT-5.6 Sol leads
τ²-bench resultsSource 88.6%85.1%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%61.8%GPT-5.6 Sol leads
GDPval-AASource 14951736GPT-5.6 Sol leads
OSWorld 2.0Source 18.2%62.6%GPT-5.6 Sol leads
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%56.2%GPT-5.6 Sol leads
ExploitGymSource —33.7%Not comparable
ToolathlonSource —58%Not comparable
AA BriefcaseSource —1501Not comparable
AA Tau3 BankingSource —33.0%Not comparable
AA AutomationBenchSource —51.2%Not comparable
AA Harvey LABSource —87.2%Not comparable
terminalBenchHardSource —65.9%Not comparable
aaTerminalBench21Source —88%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
SWE-bench VerifiedSource 87.6%—Not comparable
SWE-bench ProSource 64.3%64.6%GPT-5.6 Sol leads
Terminal-Bench 2.0Source 69.4%91.9%GPT-5.6 Sol leads
AA Coding IndexSource 73.6%77.4%GPT-5.6 Sol leads
AA-SciCodeSource 54.5%56.1%GPT-5.6 Sol leads
deepSweSource —72.7%Not comparable
FrontierCode 1.1 ExtendedSource —60.6%Not comparable
cursorBench32Source —67.2%Not comparable
VulcanBench v3Source —87.0%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%73.7%GPT-5.6 Sol leads
CritPtSource 12.0%32.3%GPT-5.6 Sol leads
ARC-AGI-3Source —7.8%Not comparable
GeneBench-ProSource —28.7%Not comparable
KnowledgeGPT-5.6 Sol wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
GPQASource 94.2%94.6%GPT-5.6 Sol leads
GPQA-DSource 94.2%94.6%GPT-5.6 Sol leads
HLESource 54.7%—Not comparable
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%58.9%GPT-5.6 Sol leads
AA-GPQA DiamondSource 91.4%94.1%GPT-5.6 Sol leads
AA-HLESource 39.6%47.2%GPT-5.6 Sol leads
AA-Omniscience IndexSource 26.2%21.7%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%58.5%GPT-5.6 Sol leads
AA-Omniscience Hallucination RateSource 36.2%88.8%Claude Opus 4.7 (Adaptive) leads
HealthBench ProfessionalSource —60.5%Not comparable
HealthBench HardSource —33.1%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
FrontierMath (legacy)Source 43.8%89%GPT-5.6 Sol leads
FrontierMath v2 (Tiers 1-3)Source —89.000%Not comparable
FrontierMath v2 (Tier 4)Source —83.000%Not comparable
MultimodalGPT-5.6 Sol wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%83.4%GPT-5.6 Sol leads
Design Arena WebsiteSource 1325—Not comparable
MMMU-ProSource —83%Not comparable
MMMU-Pro w/ PythonSource —84.6%Not comparable
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.6 SolResult
AA-IFBenchSource 58.6%72.7%GPT-5.6 Sol leads
Frequently Asked Questions (5)

Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.6 Sol?

GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 66.27. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 91.9%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GPT-5.6 Sol?

GPT-5.6 Sol has the edge for knowledge tasks in this comparison, averaging 94.6 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 Sol?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 64.6. 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 Sol?

GPT-5.6 Sol has the edge for agentic tasks in this comparison, averaging 92 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 Sol?

GPT-5.6 Sol has the edge for multimodal and grounded tasks in this comparison, averaging 83 versus 65.1. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.

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