Compare · ModelsLive · 2 picked · head to head
GPT-5.3-Codex vs Claude Opus 4.6
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Claude Opus 4.6 wins on 3/5 benchmarks
Claude Opus 4.6 wins 3 of 5 shared benchmarks. Leads in knowledge · coding.
Category leads
agentic·GPT-5.3-Codexknowledge·Claude Opus 4.6coding·Claude Opus 4.6
Hype vs Reality
Attention vs performance
GPT-5.3-Codex
#101 by perf·no signal
Claude Opus 4.6
#72 by perf·#4 by attention
Best value
GPT-5.3-Codex
1.7x better value than Claude Opus 4.6
GPT-5.3-Codex
6.7 pts/$
$7.88/M
Claude Opus 4.6
3.8 pts/$
$15.00/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Anthropic
$380.0B·Tier 1
Head to head
5 benchmarks · 2 models
GPT-5.3-CodexClaude Opus 4.6
APEX-Agents
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
GPT-5.3-Codex
31.7
Claude Opus 4.6
31.7
PostTrainBench
Claude Opus 4.6 leads by +7.1
GPT-5.3-Codex
17.8
Claude Opus 4.6
24.8
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. 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.
GPT-5.3-Codex
74.8
Claude Opus 4.6
78.7
Terminal Bench
Claude Opus 4.6 leads by +1.4
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.
GPT-5.3-Codex
78.4
Claude Opus 4.6
79.8
WeirdML
GPT-5.3-Codex leads by +1.3
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GPT-5.3-Codex
79.3
Claude Opus 4.6
78.0
Full benchmark table
| Benchmark | GPT-5.3-Codex | Claude Opus 4.6 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 31.7 | 31.7 |
PostTrainBench | 17.8 | 24.8 |
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. | 74.8 | 78.7 |
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. | 78.4 | 79.8 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 79.3 | 78.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 | |
| $5.00 | $25.00 | 1.0M tokens (~500 books) | $100.00 |