Model comparison
Claude Opus 4.8 vs GPT-4 Turbo
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 30.5 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. Claude Opus 4.8 scores higher in 9 categories and GPT-4 Turbo in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.4 to 9.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 98.3% for Claude Opus 4.8 and 6.7% for GPT-4 Turbo.
- Claude Opus 4.8 is cheaper at $5 / $25 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- Claude Opus 4.8 accepts more context: 1M tokens versus 128K.
Side by side
| Claude Opus 4.8 | GPT-4 Turbo | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 60.7 | 30.5 |
| Released | 2026-05-28 | 2023-11-06 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $5 | $10 |
| Output $ / M tokens | $25 | $30 |
| Results tracked | 65 | 36 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 59.9 (#12), GPT-4 Turbo: 33.8 (#249)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| WeirdML | 82.9% | 18% |
| LMArena Coding | 1490 | 1268 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| BigCodeBench Instruct | — | 48.2% |
| BigCodeBench Complete | — | 58.2% |
| ALE-Bench | 1,564 | — |
| HumanEval+ | — | 86.6% |
| MBPP+ | — | 73.3% |
Agentic & Tool Use Not comparable
Claude Opus 4.8: 47.6 (#11), GPT-4 Turbo: —
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| 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 | — | 36.7% |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.7 (#16), GPT-4 Turbo: 15.3 (#317)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| SimpleBench | 64.8% | 25.1% |
| Chess Puzzles | 34% | 6% |
| LMArena Hard Prompts | 1482 | 1251 |
| DTBench | 94.9% | 61.6% |
| LMCA | 57.5% | 9.8% |
| Epoch Capabilities Index | 158.21 | 127.25 |
| ForecastBench | 59.9 | 59.4 |
| ARC-AGI-2 | 72.1% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.4 (#13), GPT-4 Turbo: 9.0 (#322)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| FrontierMath (Tiers 1-3) | 80% | 0.7% |
| OTIS Mock AIME 2024-2025 | 98.3% | 6.7% |
| LMArena Math | 1487 | 1272 |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| ProofBench | 69% | — |
| MATH Level 5 | — | 46.7% |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#29), GPT-4 Turbo: 24.3 (#268)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| GPQA Diamond | 91% | 46.6% |
| LMArena Expert | 1502 | 1223 |
| SimpleQA Verified | 53% | — |
| Confabulations | — | 28.4% |
| MMLU | — | 81.3% |
Multimodal Claude Opus 4.8 leads
Claude Opus 4.8: 42.9 (#26), GPT-4 Turbo: 30.6 (#110)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| LMArena Vision | 1294 | 1090 |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |
Multilingual Claude Opus 4.8 leads
Claude Opus 4.8: 55.2 (#33), GPT-4 Turbo: 40.5 (#216)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| LMArena Non-English | 1450 | 1245 |
| LMArena Chinese | 1507 | 1242 |
| LMArena French | 1481 | 1276 |
| LMArena German | 1472 | 1259 |
| LMArena Japanese | 1440 | 1194 |
| LMArena Korean | 1432 | 1187 |
| LMArena Russian | 1474 | 1259 |
| LMArena Spanish | 1466 | 1260 |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.4 (#24), GPT-4 Turbo: 65.8 (#216)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| LMArena Instruction Following | 1476 | 1249 |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#35), GPT-4 Turbo: 38.0 (#206)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| LMArena Longer Query | 1483 | 1254 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.0 (#16), GPT-4 Turbo: 47.7 (#206)
| Benchmark | Claude Opus 4.8 | GPT-4 Turbo |
|---|---|---|
| LMArena Text | 1461 | 1272 |
| LMArena Creative Writing | 1454 | 1269 |
| LMArena Multi-Turn | 1476 | 1267 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GPT-4 Turbo?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 30.5 on the Noometry Index.
Which is cheaper, Claude Opus 4.8 or GPT-4 Turbo?
Claude Opus 4.8 is cheaper. It lists at $5 per million input tokens and $25 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is Claude Opus 4.8 or GPT-4 Turbo better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 59.9 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 128K.
How many benchmarks do Claude Opus 4.8 and GPT-4 Turbo share?
28 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GPT-4 Turbo has 36.