Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.5 vs GPT-5.3-Codex
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.3-Codex wins on 4/6 benchmarks
GPT-5.3-Codex wins 4 of 6 shared benchmarks. Leads in agentic · knowledge · coding.
Category leads
agentic·GPT-5.3-Codexgeneral·Claude Opus 4.5knowledge·GPT-5.3-Codexcoding·GPT-5.3-Codex
Hype vs Reality
Attention vs performance
Claude Opus 4.5
#179 by perf·#6 by attention
GPT-5.3-Codex
#73 by perf·#4 by attention
Best value
GPT-5.3-Codex
2.6x better value than Claude Opus 4.5
Claude Opus 4.5
2.8 pts/$
$15.00/M
GPT-5.3-Codex
7.1 pts/$
$7.88/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
6 benchmarks · 2 models
Claude Opus 4.5GPT-5.3-Codex
APEX-Agents
GPT-5.3-Codex leads by +13.3
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-5.3-Codex
31.7
Metr Time Horizons
Claude Opus 4.5 leads by +0.4
Claude Opus 4.5
75.0
GPT-5.3-Codex
74.5
PostTrainBench
GPT-5.3-Codex leads by +0.5
Claude Opus 4.5
17.3
GPT-5.3-Codex
17.8
SWE-Bench verified
Claude Opus 4.5 leads by +1.9
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. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
Claude Opus 4.5
76.7
GPT-5.3-Codex
74.8
Terminal Bench
GPT-5.3-Codex leads by +15.3
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 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
Claude Opus 4.5
63.1
GPT-5.3-Codex
78.4
WeirdML
GPT-5.3-Codex leads by +15.6
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-5.3-Codex
79.3
Full benchmark table
| Benchmark | Claude Opus 4.5 | GPT-5.3-Codex |
|---|---|---|
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 | 31.7 |
Metr Time Horizons | 75.0 | 74.5 |
PostTrainBench | 17.3 | 17.8 |
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. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability. | 76.7 | 74.8 |
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. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 63.1 | 78.4 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 63.7 | 79.3 |
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 | |
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 |
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