Model comparison
Claude Fable 5 vs GPT-5
Claude Fable 5 is the stronger model overall, scoring 66.8 to 50.9 on the Noometry Index. GPT-5 costs 5.8× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.
Last verified . 44 shared benchmarks.
Summary
- They share 44 benchmarks with published results for both. Claude Fable 5 scores higher in 8 categories and GPT-5 in 2 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Fable 5 leads 76.8 to 38.3.
- The biggest single-benchmark swing is ARC-AGI-2: 89.2% for Claude Fable 5 and 9.9% for GPT-5.
- GPT-5 is cheaper at $1.25 / $10 per million input/output tokens, against $10 / $50 for Claude Fable 5.
- Claude Fable 5 accepts more context: 1M tokens versus 400K.
Side by side
| Claude Fable 5 | GPT-5 | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 66.8 | 50.9 |
| Released | 2026-06-07 | 2025-08-07 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $10 | $1.25 |
| Output $ / M tokens | $50 | $10 |
| Results tracked | 62 | 69 |
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Category by category
Coding Claude Fable 5 leads
Claude Fable 5: 70.6 (#4), GPT-5: 50.3 (#47)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena WebDev | 1625 | 1418 |
| SciCode | 61% | 42.9% |
| GSO | 78.4% | 6.9% |
| WeirdML | 91.9% | 60.7% |
| LMArena Coding | 1519 | 1436 |
| ALE-Bench | 2,041 | 1,162 |
| SWE-bench Verified | — | 73.6% |
| DeepSWE | 69.9% | — |
| FrontierCode | 53.5% | — |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| FrontierSWE | 47% | — |
| MirrorCode | 63.9% | — |
| AlgoTune | — | 1.67 |
Agentic & Tool Use Claude Fable 5 leads
Claude Fable 5: 54.0 (#2), GPT-5: 33.1 (#56)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| Remote Labor Index | 16.1% | 1.7% |
| LMArena Search | 1230 | 1133 |
| Terminal-Bench | — | 49.6% |
| APEX-Agents | 63.6% | — |
| GDPval | — | 34.8% |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | — | 49.6% |
| PostTrainBench | 41.8% | — |
| BALROG | — | 32.8% |
| GBAEval | 74.5% | — |
| GDP.pdf | 30% | — |
| METR Time Horizons | — | 69.6% |
| Vending-Bench 2 | 5,680 | — |
Reasoning Claude Fable 5 leads
Claude Fable 5: 76.8 (#6), GPT-5: 38.3 (#64)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| ARC-AGI-2 | 89.2% | 9.9% |
| SimpleBench | 81.9% | 56.7% |
| Kagi LLM Benchmark | 91.4% | 72.7% |
| ARC-AGI-1 | 98.5% | 65.7% |
| CritPt | 28.6% | 12.6% |
| Chess Puzzles | 41% | 37% |
| EnigmaEval | 39.3% | 10.5% |
| EBR-Bench | 39.5% | 12.7% |
| LMArena Hard Prompts | 1508 | 1416 |
| Mystery Game Puzzles | 52% | 23% |
| DTBench | 98.4% | 90.7% |
| LMCA | 61.1% | 40% |
| Epoch Capabilities Index | 162.06 | 150 |
| NYT Connections (extended) | 92.7% | — |
| Surface Evolver Bench | 95% | — |
| Bench to the Future 3 | 0.13 | — |
| ForecastBench | — | 61.4 |
Math Claude Fable 5 leads
Claude Fable 5: 88.5 (#5), GPT-5: 55.0 (#44)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 87% | 55.4% |
| FrontierMath Tier 4 | 90.2% | 22% |
| OTIS Mock AIME 2024-2025 | 100% | 91.4% |
| ProofBench | 95% | 18% |
| LMArena Math | 1519 | 1407 |
| Omni-MATH | — | 64.7% |
| MATH Level 5 | — | 98.1% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 12.5% |
Knowledge Claude Fable 5 leads
Claude Fable 5: 62.2 (#25), GPT-5: 56.6 (#43)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| GPQA Diamond | 85.9% | 86.2% |
| SimpleQA Verified | 70.7% | 50.1% |
| LMArena Expert | 1534 | 1419 |
| Humanity's Last Exam | — | 25.3% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
| GPQA (HELM) | — | 79.2% |
Multimodal GPT-5 leads
Claude Fable 5: 45.3 (#17), GPT-5: 46.8 (#13)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena Vision | 1324 | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 35.8% | — |
| LMArena Document | 1496 | — |
Multilingual Claude Fable 5 leads
Claude Fable 5: 57.3 (#9), GPT-5: 51.4 (#110)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena Non-English | 1481 | 1397 |
| LMArena Chinese | 1543 | 1422 |
| LMArena French | 1505 | 1410 |
| LMArena German | 1486 | 1416 |
| LMArena Japanese | 1506 | 1409 |
| LMArena Korean | 1488 | 1360 |
| LMArena Russian | 1504 | 1406 |
| LMArena Spanish | 1498 | 1399 |
Instruction Following Claude Fable 5 leads
Claude Fable 5: 78.6 (#8), GPT-5: 73.8 (#113)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1502 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
Claude Fable 5: 46.3 (#23), GPT-5: 69.5 (#2)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1509 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference Claude Fable 5 leads
Claude Fable 5: 75.9 (#5), GPT-5: 63.4 (#65)
| Benchmark | Claude Fable 5 | GPT-5 |
|---|---|---|
| LMArena Text | 1491 | 1406 |
| LMArena Creative Writing | 1494 | 1365 |
| EQ-Bench Creative Writing | 1943 | 1627 |
| LMArena Multi-Turn | 1504 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |
| EQ-Bench 4 | 1340 | — |
Frequently asked questions
Is Claude Fable 5 better than GPT-5?
Claude Fable 5 is the stronger model overall, scoring 66.8 to 50.9 on the Noometry Index. GPT-5 costs 5.8× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.
Which is cheaper, Claude Fable 5 or GPT-5?
GPT-5 is cheaper. It lists at $1.25 per million input tokens and $10 per million output tokens; Claude Fable 5 lists at $10 and $50.
Is Claude Fable 5 or GPT-5 better for coding?
Claude Fable 5 scores higher on coding benchmarks: 70.6 versus 50.3 in the Noometry coding category.
Which has the bigger context window?
Claude Fable 5 does, with 1M tokens against 400K.
How many benchmarks do Claude Fable 5 and GPT-5 share?
44 benchmarks have published results for both models. Claude Fable 5 has 62 scored results on Noometry and GPT-5 has 69.