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Model comparison

Claude Opus 4.7 (Adaptive) vs GPT-5.3 Codex

Data verified

Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
0.4pts
winning →
66.69/100
2 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); GPT-5.3 Codex #26 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and GPT-5.3 Codex share 18 comparable benchmark results. 2 of 8 categories are comparable. 20 results are unique to Claude Opus 4.7 (Adaptive); 3 to GPT-5.3 Codex.

Updated July 23, 2026
Shared results
18
Claude Opus 4.7 (Adaptive) only
20
GPT-5.3 Codex only
3
Comparable categories
2 / 8

Pick GPT-5.3 Codex if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if coding is the priority or you need the larger 1M context window.

Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.3 Codex has the cleaner BenchAlign overall profile here, landing at 66.69 versus 66.27. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $1.75 input / $14.00 output per 1M tokens for GPT-5.3 Codex. Claude Opus 4.7 (Adaptive) gives you the larger context window at 1M, compared with 400K for GPT-5.3 Codex.

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-5.3 Codex
CategoryClaude Opus 4.7 (Adaptive)ΔGPT-5.3 Codex
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 11.4GPT-5.3 Codex67.2
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 3.7GPT-5.3 Codex71.4
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapGPT-5.3 CodexNot measured
KnowledgeClaude Opus 4.7 (Adaptive)60.0MarginNo overlapGPT-5.3 CodexNot measured
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapGPT-5.3 CodexNot measured

Decisive benchmark drivers

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

More
A · Claude Opus 4.7 (Adaptive)B · GPT-5.3 Codex

  1. OSWorld-Verified

    Agentic
    Source ↗
    A 78%B 64.7%
    Winner: Claude Opus 4.7 (Adaptive)Δ 13.3
    OSWorld-Verified: Claude Opus 4.7 (Adaptive) scored 78%; GPT-5.3 Codex scored 64.7%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 77.3%
    Winner: GPT-5.3 CodexΔ 7.9
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; GPT-5.3 Codex scored 77.3%. GPT-5.3 Codex wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 56.8%
    Winner: Claude Opus 4.7 (Adaptive)Δ 7.5
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; GPT-5.3 Codex scored 56.8%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. SWE-bench Verified

    Coding
    Source ↗
    A 87.6%B 85%
    Winner: Claude Opus 4.7 (Adaptive)Δ 2.6
    SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; GPT-5.3 Codex scored 85%. 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.

MetricClaude Opus 4.7 (Adaptive)GPT-5.3 CodexComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputGPT-5.3 Codex$1.75 input / $14 outputGPT-5.3 Codex has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableGPT-5.3 Codex79 tok/sA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableGPT-5.3 Codex88.26 sA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MGPT-5.3 Codex400KClaude Opus 4.7 (Adaptive) lists the larger context window.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
Terminal-Bench 2.0Source 69.4%77.3%GPT-5.3 Codex leads
BrowseCompSource 79.3%—Not comparable
MCP AtlasSource 77.3%—Not comparable
OSWorld-VerifiedSource 78%64.7%Claude Opus 4.7 (Adaptive) leads
CyberGymSource 73.1%—Not comparable
AA Agentic IndexSource 44.4%—Not comparable
τ²-bench resultsSource 88.6%86%Claude Opus 4.7 (Adaptive) leads
GDPval-AASource 49.8%—Not comparable
GDPval-AASource 1495—Not comparable
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%33.7%Claude Opus 4.7 (Adaptive) leads
AA ITBenchSource 46.7%—Not comparable
Gert LabsSource —57.47%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
SWE-bench VerifiedSource 87.6%85%Claude Opus 4.7 (Adaptive) leads
SWE-bench ProSource 64.3%56.8%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%—Not comparable
AA Coding IndexSource 73.6%—Not comparable
AA-SciCodeSource 54.5%53.2%Claude Opus 4.7 (Adaptive) leads
SWE-RebenchSource —58.2%Not comparable
Vibe Code BenchSource —61.77%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%74.0%GPT-5.3 Codex leads
CritPtSource 12.0%16.9%GPT-5.3 Codex leads
Knowledge
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
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%44.3%Claude Opus 4.7 (Adaptive) leads
AA-GPQA DiamondSource 91.4%91.5%GPT-5.3 Codex leads
AA-HLESource 39.6%39.9%GPT-5.3 Codex leads
AA-Omniscience IndexSource 26.2%9.9%Claude Opus 4.7 (Adaptive) leads
AA-Omniscience AccuracySource 45.8%51.8%GPT-5.3 Codex leads
AA-Omniscience Hallucination RateSource 36.2%86.9%Claude Opus 4.7 (Adaptive) leads
Math
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
FrontierMath (legacy)Source 43.8%—Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%78.5%Claude Opus 4.7 (Adaptive) leads
Design Arena WebsiteSource 13251193Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)GPT-5.3 CodexResult
AA-IFBenchSource 58.6%75.4%GPT-5.3 Codex leads
Frequently Asked Questions (3)

Which is better, Claude Opus 4.7 (Adaptive) or GPT-5.3 Codex?

GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 66.27. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 78% and 64.7%.

Which is better for coding, Claude Opus 4.7 (Adaptive) or GPT-5.3 Codex?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 67.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or GPT-5.3 Codex?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 71.4. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.

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