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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-5.5

Data verified

Head-to-head evidence from 35 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
7.2pts
winning →
GPT-5.5
OpenAI
73.51/100
2 category wins3 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-5.5 share 35 comparable benchmark results. 5 of 8 categories are comparable. 3 results are unique to Claude Opus 4.7 (Adaptive); 22 to GPT-5.5.

Updated July 23, 2026
Shared results
35
Claude Opus 4.7 (Adaptive) only
3
GPT-5.5 only
22
Comparable categories
5 / 8

Pick GPT-5.5 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 35 shared benchmark results across 7 evidence categories; 5 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 66.27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.5's sharpest advantage is in reasoning, where it averages 85 against 75.8. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 82%. 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-5.5 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-5.5
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-5.5
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 20.0GPT-5.558.6
ReasoningClaude Opus 4.7 (Adaptive)75.8Margin→ 9.2GPT-5.585.0
AgenticClaude Opus 4.7 (Adaptive)75.1Margin→ 6.5GPT-5.581.6
MultimodalClaude Opus 4.7 (Adaptive)65.1Margin→ 5.3GPT-5.570.4
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 2.2GPT-5.557.8
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapGPT-5.547.6

Decisive benchmark drivers

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

More
A · Claude Opus 4.7 (Adaptive)B · GPT-5.5

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 82%
    Winner: GPT-5.5Δ 12.6
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark.

  2. OfficeQA Pro

    Multimodal
    Source ↗
    A 43.6%B 54.1%
    Winner: GPT-5.5Δ 10.5
    OfficeQA Pro: Claude Opus 4.7 (Adaptive) scored 43.6%; GPT-5.5 scored 54.1%. GPT-5.5 wins this benchmark.

  3. ARC-AGI-2

    Reasoning
    Source ↗
    A 75.8%B 85%
    Winner: GPT-5.5Δ 9.2
    ARC-AGI-2: Claude Opus 4.7 (Adaptive) scored 75.8%; GPT-5.5 scored 85%. GPT-5.5 wins this benchmark.

  4. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 58.6%
    Winner: Claude Opus 4.7 (Adaptive)Δ 5.7
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GPT-5.5 scored 58.6%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  5. BrowseComp

    Agentic
    Source ↗
    A 79.3%B 84.4%
    Winner: GPT-5.5Δ 5.1
    BrowseComp: Claude Opus 4.7 (Adaptive) scored 79.3%; GPT-5.5 scored 84.4%. GPT-5.5 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-5.5Comparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-5.5$5 input / $30 outputClaude Opus 4.7 (Adaptive) has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-5.5Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-5.5Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-5.51MListed context windows are equal.

Benchmark Deep Dive

AgenticGPT-5.5 wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
Terminal-Bench 2.0Source 69.4%82%GPT-5.5 leads
BrowseCompSource 79.3%84.4%GPT-5.5 leads
MCP AtlasSource 77.3%75.3%Claude Opus 4.7 (Adaptive) leads
OSWorld-VerifiedSource 78%78.7%GPT-5.5 leads
CyberGymSource 73.1%81.8%GPT-5.5 leads
AA Agentic IndexSource 44.4%44.9%GPT-5.5 leads
τ²-bench resultsSource 88.6%93.9%GPT-5.5 leads
GDPval-AASource 49.8%49.5%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 14951490Claude Opus 4.7 (Adaptive) leads
OSWorld 2.0Source 18.2%13.0%Claude Opus 4.7 (Adaptive) leads
JobBenchSource 45.9%42.7%Claude Opus 4.7 (Adaptive) leads
AA ITBenchSource 46.7%45.8%Claude Opus 4.7 (Adaptive) leads
ToolathlonSource —55.6%Not comparable
APEX-Agents-AASource —37.7%Not comparable
Gert LabsSource —72.93%Not comparable
ResearchClawBenchSource —17.0%Not comparable
ExploitGymSource —13.4%Not comparable
AA BriefcaseSource —1154Not comparable
AA AutomationBenchSource —42.1%Not comparable
AA EnterpriseOps-GymSource —46.6%Not comparable
AA Harvey LABSource —86.3%Not comparable
AA Tau3 BankingSource —31.3%Not comparable
terminalBenchHardSource —60.6%Not comparable
aaTerminalBench21Source —84.3%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
SWE-bench VerifiedSource 87.6%—Not comparable
SWE-bench ProSource 64.3%58.6%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%82.0%GPT-5.5 leads
AA Coding IndexSource 73.6%74.9%GPT-5.5 leads
AA-SciCodeSource 54.5%56.1%GPT-5.5 leads
Vibe Code BenchSource —69.85%Not comparable
React Native EvalsSource —84.7%Not comparable
cursorBench31Source —59.2%Not comparable
cursorBench32Source —58.4%Not comparable
FrontierCode 1.1 MainSource —43.0%Not comparable
ReasoningGPT-5.5 wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
MRCR v2 128K-256KSource 59.2%87.5%GPT-5.5 leads
ARC-AGI-2Source 75.8%85%GPT-5.5 leads
AA-LCRSource 70.3%74.3%GPT-5.5 leads
CritPtSource 12.0%27.1%GPT-5.5 leads
MRCR v2 64K-128KSource —83.1%Not comparable
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
GPQASource 94.2%93.6%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%93.6%Claude Opus 4.7 (Adaptive) leads
HLESource 54.7%52.2%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%41.4%Claude Opus 4.7 (Adaptive) leads
Artificial Analysis Intelligence IndexSource 53.5%54.8%GPT-5.5 leads
AA-GPQA DiamondSource 91.4%93.5%GPT-5.5 leads
AA-HLESource 39.6%44.3%GPT-5.5 leads
AA-Omniscience IndexSource 26.2%20.1%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%56.9%GPT-5.5 leads
AA-Omniscience Hallucination RateSource 36.2%85.5%Claude Opus 4.7 (Adaptive) leads
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
FrontierMath (legacy)Source 43.8%51.7%GPT-5.5 leads
FrontierMath v2 (Tiers 1-3)Source —51.700%Not comparable
FrontierMath v2 (Tier 4)Source —35.400%Not comparable
MultimodalGPT-5.5 wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
OfficeQA ProSource 43.6%54.1%GPT-5.5 leads
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%79.9%GPT-5.5 leads
Design Arena WebsiteSource 13251282Claude Opus 4.7 (Adaptive) leads
MMMU-ProSource —81.2%Not comparable
MMMU-Pro w/ PythonSource —83.2%Not comparable
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.5Result
AA-IFBenchSource 58.6%75.9%GPT-5.5 leads
Frequently Asked Questions (6)

Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.5?

GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 66.27. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 82%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or GPT-5.5?

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 57.8. 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-5.5?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 58.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Which is better for reasoning, Claude Opus 4.7 (Adaptive) or GPT-5.5?

GPT-5.5 has the edge for reasoning in this comparison, averaging 85 versus 75.8. Inside this category, MRCR v2 128K-256K is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GPT-5.5?

GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 75.1. Inside this category, Terminal-Bench 2.0 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-5.5?

GPT-5.5 has the edge for multimodal and grounded tasks in this comparison, averaging 70.4 versus 65.1. Inside this category, Design Arena Website is the benchmark that creates the most daylight between them.

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