Model comparison
Claude Opus 4.8 vs GPT-5.3 Codex
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 45.8 on the Noometry Index. GPT-5.3 Codex costs 2.1× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Summary
- They share 5 benchmarks with published results for both. Claude Opus 4.8 scores higher in 1 category and GPT-5.3 Codex in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where Claude Opus 4.8 leads 59.9 to 48.6.
- GPT-5.3 Codex is cheaper at $1.75 / $14 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 400K.
Side by side
| Claude Opus 4.8 | GPT-5.3 Codex | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 60.7 | 45.8 |
| Released | 2026-05-28 | 2026-02-05 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $5 | $1.75 |
| Output $ / M tokens | $25 | $14 |
| Results tracked | 65 | 8 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), GPT-5.3 Codex: 48.6 (#56)
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena WebDev | 1556 | 1409 |
| WeirdML | 82.9% | 79.3% |
| ALE-Bench | 1,564 | 1,655 |
| SWE-bench Verified | — | 74.8% |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| LMArena Coding | 1490 | — |
Agentic & Tool Use Too close to call
Claude Opus 4.8: 47.6 (#11), GPT-5.3 Codex: 48.0 (#9)
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| Vending-Bench 2 | 5,787 | 5,940 |
| Terminal-Bench | — | 78.4% |
| APEX-Agents | 48.9% | — |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| METR Time Horizons | — | 74.5% |
Reasoning Not comparable
Claude Opus 4.8: 64.7 (#16), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| Epoch Capabilities Index | 158.21 | 156.77 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| LMArena Hard Prompts | 1482 | — |
| Mystery Game Puzzles | 36% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 59.9 | — |
Math Not comparable
Claude Opus 4.8: 78.4 (#13), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 69% | — |
| LMArena Math | 1487 | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Not comparable
Claude Opus 4.8: 61.3 (#29), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 53% | — |
| LMArena Expert | 1502 | — |
Multimodal Not comparable
Claude Opus 4.8: 42.9 (#26), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena Vision | 1294 | — |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Not comparable
Claude Opus 4.8: 55.2 (#33), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena Non-English | 1450 | — |
| LMArena Chinese | 1507 | — |
| LMArena French | 1481 | — |
| LMArena German | 1472 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |
| LMArena Russian | 1474 | — |
| LMArena Spanish | 1466 | — |
Instruction Following Not comparable
Claude Opus 4.8: 77.4 (#24), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena Instruction Following | 1476 | — |
Long Context Not comparable
Claude Opus 4.8: 45.4 (#35), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena Longer Query | 1483 | — |
Writing & Preference Not comparable
Claude Opus 4.8: 72.0 (#16), GPT-5.3 Codex: —
| Benchmark | Claude Opus 4.8 | GPT-5.3 Codex |
|---|---|---|
| LMArena Text | 1461 | — |
| LMArena Creative Writing | 1454 | — |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
| LMArena Multi-Turn | 1476 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GPT-5.3 Codex?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 45.8 on the Noometry Index. GPT-5.3 Codex costs 2.1× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 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 Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or GPT-5.3 Codex better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 48.6 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 400K.
How many benchmarks do Claude Opus 4.8 and GPT-5.3 Codex share?
5 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GPT-5.3 Codex has 8.