Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.5 vs GPT-5.5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.5 wins on 25/26 benchmarks
GPT-5.5 wins 25 of 26 shared benchmarks. Leads in agentic · reasoning · arena.
Category leads
agentic·GPT-5.5reasoning·GPT-5.5arena·GPT-5.5knowledge·GPT-5.5general·GPT-5.5math·GPT-5.5coding·GPT-5.5
Hype vs Reality
Attention vs performance
Claude Opus 4.5
#197 by perf·#5 by attention
GPT-5.5
#65 by perf·#9 by attention
Best value
GPT-5.5
1.2x better value than Claude Opus 4.5
Claude Opus 4.5
2.7 pts/$
$15.00/M
GPT-5.5
3.3 pts/$
$17.50/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
26 benchmarks · 2 models
Claude Opus 4.5GPT-5.5
APEX-Agents
GPT-5.5 leads by +36.7
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Opus 4.5
18.4
GPT-5.5
55.1
ARC-AGI
GPT-5.5 leads by +15.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Claude Opus 4.5
80.0
GPT-5.5
95.0
ARC-AGI-2
GPT-5.5 leads by +47.4
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Claude Opus 4.5
37.6
GPT-5.5
85.0
Chatbot Arena Elo · Overall
GPT-5.5 leads by +7.0
Claude Opus 4.5
1469.8
GPT-5.5
1476.8
Chess Puzzles
GPT-5.5 leads by +49.1
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Claude Opus 4.5
12.0
GPT-5.5
61.1
DeepResearch Bench
Claude Opus 4.5 leads by +0.8
DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses.
Claude Opus 4.5
54.8
GPT-5.5
54.0
Dtbench
GPT-5.5 leads by +10.2
Claude Opus 4.5
83.1
GPT-5.5
93.3
Ebr Bench
GPT-5.5 leads by +20.0
Claude Opus 4.5
14.3
GPT-5.5
34.3
FrontierMath-2025-02-28-Private
GPT-5.5 leads by +31.0
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Claude Opus 4.5
20.7
GPT-5.5
51.7
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.5 leads by +31.2
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Claude Opus 4.5
4.2
GPT-5.5
35.4
FrontierMath-Tier-4-v2-Private
GPT-5.5 leads by +67.6
Claude Opus 4.5
4.9
GPT-5.5
72.5
FrontierMath-Tiers-1-3-v2-Private
GPT-5.5 leads by +53.5
Claude Opus 4.5
36.2
GPT-5.5
89.8
Furniture Assembly
GPT-5.5 leads by +20.2
Claude Opus 4.5
0.0
GPT-5.5
20.2
GPQA diamond
GPT-5.5 leads by +14.4
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Claude Opus 4.5
81.4
GPT-5.5
95.8
GSO-Bench
GPT-5.5 leads by +13.7
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
Claude Opus 4.5
26.5
GPT-5.5
40.2
Lmca
GPT-5.5 leads by +11.5
Claude Opus 4.5
52.3
GPT-5.5
63.9
Mystery Game Puzzles
GPT-5.5 leads by +37.5
Claude Opus 4.5
14.1
GPT-5.5
51.5
OTIS Mock AIME 2024-2025
GPT-5.5 leads by +13.9
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Claude Opus 4.5
86.1
GPT-5.5
100.0
PostTrainBench
GPT-5.5 leads by +9.9
Claude Opus 4.5
17.3
GPT-5.5
27.2
Proofbench
GPT-5.5 leads by +14.0
Claude Opus 4.5
36.0
GPT-5.5
50.0
Remote Labor Index
GPT-5.5 leads by +2.5
Claude Opus 4.5
3.8
GPT-5.5
6.3
SimpleBench
GPT-5.5 leads by +8.4
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Claude Opus 4.5
54.4
GPT-5.5
62.8
SimpleQA Verified
GPT-5.5 leads by +21.2
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Claude Opus 4.5
41.8
GPT-5.5
63.0
SWE-Bench verified
GPT-5.5 leads by +3.9
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability.
Claude Opus 4.5
76.7
GPT-5.5
80.6
Terminal Bench
GPT-5.5 leads by +21.6
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency.
Claude Opus 4.5
63.1
GPT-5.5
84.7
WeirdML
GPT-5.5 leads by +21.2
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.5
63.7
GPT-5.5
84.9
Full benchmark table
| Benchmark | Claude Opus 4.5 | GPT-5.5 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 18.4 | 55.1 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 80.0 | 95.0 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 37.6 | 85.0 |
Chatbot Arena Elo · Overall | 1469.8 | 1476.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 12.0 | 61.1 |
DeepResearch Bench DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses. | 54.8 | 54.0 |
Dtbench | 83.1 | 93.3 |
Ebr Bench | 14.3 | 34.3 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 20.7 | 51.7 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 4.2 | 35.4 |
FrontierMath-Tier-4-v2-Private | 4.9 | 72.5 |
FrontierMath-Tiers-1-3-v2-Private | 36.2 | 89.8 |
Furniture Assembly | 0.0 | 20.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 81.4 | 95.8 |
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 26.5 | 40.2 |
Lmca | 52.3 | 63.9 |
Mystery Game Puzzles | 14.1 | 51.5 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 86.1 | 100.0 |
PostTrainBench | 17.3 | 27.2 |
Proofbench | 36.0 | 50.0 |
Remote Labor Index | 3.8 | 6.3 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 54.4 | 62.8 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 41.8 | 63.0 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability. | 76.7 | 80.6 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. | 63.1 | 84.7 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 63.7 | 84.9 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $5.00 | $25.00 | 200K tokens (~100 books) | $100.00 | |
| $5.00 | $30.00 | 1.1M tokens (~525 books) | $112.50 |