Compare · ModelsLive · 2 picked · head to head
Claude Opus 4.6 vs GPT-5.3-Codex
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Opus 4.6 wins on 6/7 benchmarks
Claude Opus 4.6 wins 6 of 7 shared benchmarks. Leads in agentic · general · knowledge.
Category leads
agentic·Claude Opus 4.6general·Claude Opus 4.6knowledge·Claude Opus 4.6coding·Claude Opus 4.6
Hype vs Reality
Attention vs performance
Claude Opus 4.6
#153 by perf·#5 by attention
GPT-5.3-Codex
#83 by perf·#9 by attention
Best value
GPT-5.3-Codex
2.2x better value than Claude Opus 4.6
Claude Opus 4.6
3.2 pts/$
$15.00/M
GPT-5.3-Codex
7.0 pts/$
$7.88/M
Vendor risk
Who is behind the model
Anthropic
$965.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
7 benchmarks · 2 models
Claude Opus 4.6GPT-5.3-Codex
APEX-Agents
Claude Opus 4.6 leads by +14.6
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Claude Opus 4.6
46.3
GPT-5.3-Codex
31.7
Metr Time Horizons
Claude Opus 4.6 leads by +4.3
Claude Opus 4.6
78.9
GPT-5.3-Codex
74.5
PostTrainBench
Claude Opus 4.6 leads by +5.4
Claude Opus 4.6
23.2
GPT-5.3-Codex
17.8
SWE Atlas · Codebase QnA
Claude Opus 4.6 leads by +0.7
Claude Opus 4.6
33.3
GPT-5.3-Codex
32.6
SWE-Bench verified
Claude Opus 4.6 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 4.6
78.7
GPT-5.3-Codex
74.8
Terminal Bench
Claude Opus 4.6 leads by +2.5
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 4.6
79.8
GPT-5.3-Codex
77.3
WeirdML
GPT-5.3-Codex leads by +1.4
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Claude Opus 4.6
77.9
GPT-5.3-Codex
79.3
Full benchmark table
| Benchmark | Claude Opus 4.6 | 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. | 46.3 | 31.7 |
Metr Time Horizons | 78.9 | 74.5 |
PostTrainBench | 23.2 | 17.8 |
SWE Atlas · Codebase QnA | 33.3 | 32.6 |
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. | 78.7 | 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. | 79.8 | 77.3 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 77.9 | 79.3 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $5.00 | $25.00 | 1.0M tokens (~500 books) | $100.00 | |
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 |