Model comparison
Claude Opus 4.5 vs GPT-5-Codex
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 37.9 on the Noometry Index. GPT-5-Codex costs 2.9× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Summary
- They share 3 benchmarks with published results for both. Claude Opus 4.5 scores higher in 3 categories and GPT-5-Codex in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Claude Opus 4.5 leads 47.3 to 31.0.
- The biggest single-benchmark swing is Terminal-Bench: 63.1% for Claude Opus 4.5 and 44.3% for GPT-5-Codex.
- GPT-5-Codex is cheaper at $1.25 / $10 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GPT-5-Codex accepts more context: 400K tokens versus 200K.
Side by side
| Claude Opus 4.5 | GPT-5-Codex | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 50.5 | 37.9 |
| Released | 2025-11-01 | 2025-09-15 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 400K |
| Max output | 64K | 128K |
| Input $ / M tokens | $5 | $1.25 |
| Output $ / M tokens | $25 | $10 |
| Results tracked | 69 | 3 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Claude Opus 4.5 leads
Claude Opus 4.5: 54.8 (#27), GPT-5-Codex: 42.4 (#103)
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| WeirdML | 63.7% | 54.5% |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Verified (bash only) | 76.8% | — |
| LMArena WebDev | 1494 | — |
| SWE-bench Multilingual | 70.7% | — |
| GSO | 26.5% | — |
| LMArena Coding | 1504 | — |
| ALE-Bench | 1,025 | — |
| AlgoTune | 1.77 | — |
Agentic & Tool Use Claude Opus 4.5 leads
Claude Opus 4.5: 47.3 (#12), GPT-5-Codex: 31.0 (#72)
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| Terminal-Bench | 63.1% | 44.3% |
| Berkeley Function Calling Leaderboard | 77.5% | — |
| GDPval | 45.5% | — |
| Remote Labor Index | 3.8% | — |
| τ²-bench Airline | 84% | — |
| τ²-bench Banking | 24.7% | — |
| τ²-bench Retail | 79.6% | — |
| τ²-bench Telecom | 92.3% | — |
| Cybench | 82% | — |
| DeepResearch Bench | 54.8% | — |
| OSWorld | 66.3% | — |
| BALROG | 43.5% | — |
| LMArena Search | 1180 | — |
| METR Time Horizons | 75% | — |
| Vending-Bench 2 | 4,967 | — |
Reasoning Claude Opus 4.5 leads
Claude Opus 4.5: 42.6 (#51), GPT-5-Codex: 30.9 (#83)
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| Kagi LLM Benchmark | 80.2% | 70.3% |
| ARC-AGI-2 | 37.6% | — |
| SimpleBench | 62% | — |
| NYT Connections (extended) | 52.5% | — |
| ARC-AGI-1 | 80% | — |
| Chess Puzzles | 12% | — |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| LMArena Hard Prompts | 1476 | — |
| Mystery Game Puzzles | 22% | — |
| DTBench | 89.9% | — |
| LMCA | 44.5% | — |
| Epoch Capabilities Index | 150.09 | — |
| ForecastBench | 60.7 | — |
Math Not comparable
Claude Opus 4.5: 38.6 (#132), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 34.4% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 86.1% | — |
| ProofBench | 36% | — |
| LMArena Math | 1463 | — |
| FrontierMath (Feb 2025 set) | 20.7% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Not comparable
Claude Opus 4.5: 56.5 (#44), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| GPQA Diamond | 86% | — |
| Humanity's Last Exam | 25.2% | — |
| SimpleQA Verified | 45.7% | — |
| Vectara Hallucination Rate | 10.9% | — |
| LMArena Expert | 1487 | — |
Multimodal Not comparable
Claude Opus 4.5: 31.4 (#107), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |
Multilingual Not comparable
Claude Opus 4.5: 54.3 (#47), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| LMArena Non-English | 1438 | — |
| LMArena Chinese | 1470 | — |
| LMArena French | 1471 | — |
| LMArena German | 1449 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1424 | — |
| LMArena Russian | 1447 | — |
| LMArena Spanish | 1458 | — |
Instruction Following Not comparable
Claude Opus 4.5: 77.5 (#19), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| LMArena Instruction Following | 1478 | — |
Long Context Not comparable
Claude Opus 4.5: 46.5 (#22), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| CL-bench | 21.1% | — |
| LMArena Longer Query | 1480 | — |
Writing & Preference Not comparable
Claude Opus 4.5: 68.1 (#28), GPT-5-Codex: —
| Benchmark | Claude Opus 4.5 | GPT-5-Codex |
|---|---|---|
| LMArena Text | 1451 | — |
| LMArena Creative Writing | 1445 | — |
| EQ-Bench Creative Writing | 1687 | — |
| LMArena Multi-Turn | 1466 | — |
Frequently asked questions
Is Claude Opus 4.5 better than GPT-5-Codex?
Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 37.9 on the Noometry Index. GPT-5-Codex costs 2.9× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.5 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 4.5 lists at $5 and $25.
Is Claude Opus 4.5 or GPT-5-Codex better for coding?
Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 42.4 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 4.5 and GPT-5-Codex share?
3 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GPT-5-Codex has 3.