Model comparison
Claude Opus 4 vs GPT-5-Codex
Claude Opus 4 is the stronger model overall, scoring 42.9 to 37.3 on the Noometry Index. GPT-5-Codex costs 8.7× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Summary
- They share 2 benchmarks with published results for both. Claude Opus 4 scores higher in 2 categories and GPT-5-Codex in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in coding, where Claude Opus 4 leads 47.8 to 41.9.
- The biggest single-benchmark swing is WeirdML: 43.7% for Claude Opus 4 and 54.5% for GPT-5-Codex.
- GPT-5-Codex is cheaper at $1.25 / $10 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- GPT-5-Codex accepts more context: 400K tokens versus 200K.
Side by side
| Claude Opus 4 | GPT-5-Codex | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 42.9 | 37.3 |
| Released | 2025-05-22 | 2025-09-15 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 400K |
| Max output | 32K | 128K |
| Input $ / M tokens | $15 | $1.25 |
| Output $ / M tokens | $75 | $10 |
| Results tracked | 56 | 3 |
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Category by category
Coding Claude Opus 4 leads
Claude Opus 4: 47.8 (#55), GPT-5-Codex: 41.9 (#100)
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| WeirdML | 43.7% | 54.5% |
| SWE-bench Verified | 70.7% | — |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| GSO | 6.9% | — |
| LMArena Coding | 1442 | — |
| AlgoTune | 1.33 | — |
Agentic & Tool Use Claude Opus 4 leads
Claude Opus 4: 34.5 (#39), GPT-5-Codex: 30.8 (#66)
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| Terminal-Bench | — | 44.3% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
Reasoning GPT-5-Codex leads
Claude Opus 4: 27.4 (#104), GPT-5-Codex: 30.0 (#80)
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| Kagi LLM Benchmark | 74.3% | 70.3% |
| ARC-AGI-2 | 8.6% | — |
| SimpleBench | 58.8% | — |
| ARC-AGI-1 | 35.7% | — |
| CritPt | 0.3% | — |
| EnigmaEval | 5.6% | — |
| LMArena Hard Prompts | 1399 | — |
| DTBench | 81.6% | — |
| LMCA | 37.4% | — |
| Epoch Capabilities Index | 142.67 | — |
| ForecastBench | 61.1 | — |
Math Not comparable
Claude Opus 4: 40.2 (#80), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | — |
| Omni-MATH | 61.6% | — |
| LMArena Math | 1391 | — |
| MATH Level 5 | 85% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Not comparable
Claude Opus 4: 43.9 (#82), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| GPQA Diamond | 76.3% | — |
| Humanity's Last Exam | 10.7% | — |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 70.8% | — |
| LMArena Expert | 1384 | — |
Multimodal Not comparable
Claude Opus 4: 30.9 (#101), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| LMArena Vision | 1192 | — |
| GeoBench | 49% | — |
| VPCT | 38% | — |
Multilingual Not comparable
Claude Opus 4: 49.0 (#133), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| LMArena Non-English | 1362 | — |
| LMArena Chinese | 1386 | — |
| LMArena French | 1370 | — |
| LMArena German | 1391 | — |
| LMArena Japanese | 1332 | — |
| LMArena Korean | 1318 | — |
| LMArena Russian | 1392 | — |
| LMArena Spanish | 1388 | — |
Instruction Following Not comparable
Claude Opus 4: 77.3 (#26), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| IFEval | 91.8% | — |
| LMArena Instruction Following | 1406 | — |
Long Context Not comparable
Claude Opus 4: 39.5 (#167), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| Fiction.LiveBench | 61.1% | — |
| LMArena Longer Query | 1422 | — |
Writing & Preference Not comparable
Claude Opus 4: 61.4 (#85), GPT-5-Codex: —
| Benchmark | Claude Opus 4 | GPT-5-Codex |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1386 | — |
| Short-Story Creative Writing | 83.6% | — |
| EQ-Bench Creative Writing | 1580 | — |
| WildBench | 85.2% | — |
| LMArena Multi-Turn | 1396 | — |
Frequently asked questions
Is Claude Opus 4 better than GPT-5-Codex?
Claude Opus 4 is the stronger model overall, scoring 42.9 to 37.3 on the Noometry Index. GPT-5-Codex costs 8.7× less per token, which makes it the better buy when Claude Opus 4's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4 or GPT-5-Codex?
GPT-5-Codex is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; Claude Opus 4 lists at $15 and $75.
Is Claude Opus 4 or GPT-5-Codex better for coding?
Claude Opus 4 scores higher on coding benchmarks: 47.8 versus 41.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5-Codex does, with 400K tokens against 200K.
How many benchmarks do Claude Opus 4 and GPT-5-Codex share?
2 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and GPT-5-Codex has 3.