Model comparison
Claude Sonnet 4.5 vs GPT-5.3 Codex
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 44.1 on the Noometry Index.
Last verified . 8 shared benchmarks.
Summary
- They share 8 benchmarks with published results for both. Claude Sonnet 4.5 scores higher in 0 categories and GPT-5.3 Codex in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 38.3.
- The biggest single-benchmark swing is Terminal-Bench: 46.5% for Claude Sonnet 4.5 and 78.4% for GPT-5.3 Codex.
- GPT-5.3 Codex is cheaper at $1.75 / $14 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.5.
- GPT-5.3 Codex accepts more context: 400K tokens versus 200K.
Side by side
| Claude Sonnet 4.5 | GPT-5.3 Codex | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 44.1 | 45.8 |
| Released | 2025-09-29 | 2026-02-05 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 400K |
| Max output | 64K | 128K |
| Input $ / M tokens | $3 | $1.75 |
| Output $ / M tokens | $15 | $14 |
| Results tracked | 73 | 8 |
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Category by category
Coding GPT-5.3 Codex leads
Claude Sonnet 4.5: 47.3 (#61), GPT-5.3 Codex: 48.6 (#56)
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| SWE-bench Verified | 71.3% | 74.8% |
| LMArena WebDev | 1393 | 1409 |
| WeirdML | 47.7% | 79.3% |
| ALE-Bench | 796.15 | 1,655 |
| SWE-bench Verified (bash only) | 71.4% | — |
| SWE-bench Multilingual | 67% | — |
| SciCode | 44.7% | — |
| GSO | 14.7% | — |
| LMArena Coding | 1489 | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use GPT-5.3 Codex leads
Claude Sonnet 4.5: 38.3 (#32), GPT-5.3 Codex: 48.0 (#9)
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| Terminal-Bench | 46.5% | 78.4% |
| METR Time Horizons | 67.4% | 74.5% |
| Vending-Bench 2 | 3,839 | 5,940 |
| Berkeley Function Calling Leaderboard | 73.2% | — |
| GDPval | 42.5% | — |
| Remote Labor Index | 2.1% | — |
| τ²-bench Airline | 72% | — |
| τ²-bench Banking | 25.3% | — |
| τ²-bench Retail | 72.4% | — |
| τ²-bench Telecom | 84.9% | — |
| Cybench | 60% | — |
| DeepResearch Bench | 52.6% | — |
| OSWorld | 62.9% | — |
| LMArena Search | 1159 | — |
Reasoning Not comparable
Claude Sonnet 4.5: 26.9 (#125), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 146.84 | 156.77 |
| ARC-AGI-2 | 13.6% | — |
| SimpleBench | 54.3% | — |
| Kagi LLM Benchmark | 57.9% | — |
| NYT Connections (extended) | 37.3% | — |
| ARC-AGI-1 | 63.7% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 12% | — |
| EnigmaEval | 6% | — |
| EBR-Bench | 2.4% | — |
| LMArena Hard Prompts | 1462 | — |
| Mystery Game Puzzles | 17% | — |
| DTBench | 83.2% | — |
| LMCA | 38.8% | — |
| ForecastBench | 61.9 | — |
Math Not comparable
Claude Sonnet 4.5: 32.3 (#216), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 23.9% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 77.8% | — |
| ProofBench | 19% | — |
| Omni-MATH | 55.3% | — |
| LMArena Math | 1449 | — |
| MATH Level 5 | 97.7% | — |
| FrontierMath (Feb 2025 set) | 15.2% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Not comparable
Claude Sonnet 4.5: 48.4 (#76), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 82.3% | — |
| Humanity's Last Exam | 13.7% | — |
| SimpleQA Verified | 30.7% | — |
| MMLU-Pro | 86.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 68.6% | — |
| LMArena Expert | 1482 | — |
Multimodal Not comparable
Claude Sonnet 4.5: 34.8 (#89), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| VPCT | 39.8% | — |
| LMArena Document | 1450 | — |
Multilingual Not comparable
Claude Sonnet 4.5: 53.4 (#69), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1425 | — |
| LMArena Chinese | 1459 | — |
| LMArena French | 1458 | — |
| LMArena German | 1427 | — |
| LMArena Japanese | 1390 | — |
| LMArena Korean | 1403 | — |
| LMArena Russian | 1437 | — |
| LMArena Spanish | 1457 | — |
Instruction Following Not comparable
Claude Sonnet 4.5: 75.0 (#78), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| IFEval | 85% | — |
| LMArena Instruction Following | 1459 | — |
Long Context Not comparable
Claude Sonnet 4.5: 45.2 (#46), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1476 | — |
Writing & Preference Not comparable
Claude Sonnet 4.5: 66.5 (#34), GPT-5.3 Codex: —
| Benchmark | Claude Sonnet 4.5 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1439 | — |
| LMArena Creative Writing | 1442 | — |
| EQ-Bench Creative Writing | 1678 | — |
| WildBench | 85.4% | — |
| LMArena Multi-Turn | 1465 | — |
Frequently asked questions
Is Claude Sonnet 4.5 better than GPT-5.3 Codex?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 44.1 on the Noometry Index.
Which is cheaper, Claude Sonnet 4.5 or GPT-5.3 Codex?
GPT-5.3 Codex is cheaper. It lists at $1.75 per million input tokens and $14 per million output tokens; Claude Sonnet 4.5 lists at $3 and $15.
Is Claude Sonnet 4.5 or GPT-5.3 Codex better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 47.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 200K.
How many benchmarks do Claude Sonnet 4.5 and GPT-5.3 Codex share?
8 benchmarks have published results for both models. Claude Sonnet 4.5 has 73 scored results on Noometry and GPT-5.3 Codex has 8.