Model comparison
Claude Fable 5 vs gpt-oss-120b
Claude Fable 5 is the stronger model overall, scoring 66.8 to 36.3 on the Noometry Index. gpt-oss-120b costs 285× less per token, which makes it the better buy when Claude Fable 5's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. Claude Fable 5 scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Fable 5 leads 76.8 to 20.0.
- The biggest single-benchmark swing is Surface Evolver Bench: 95% for Claude Fable 5 and 25% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $10 / $50 for Claude Fable 5.
- Claude Fable 5 accepts more context: 1M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| Claude Fable 5 | gpt-oss-120b | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 66.8 | 36.3 |
| Released | 2026-06-07 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 1M | 131K |
| Max output | 128K | 41K |
| Input $ / M tokens | $10 | $0.037 |
| Output $ / M tokens | $50 | $0.17 |
| Results tracked | 62 | 48 |
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Category by category
Coding Claude Fable 5 leads
Claude Fable 5: 70.6 (#4), gpt-oss-120b: 33.5 (#256)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| SciCode | 61% | 36% |
| WeirdML | 91.9% | 48.2% |
| LMArena Coding | 1519 | 1380 |
| ALE-Bench | 2,041 | 575.62 |
| DeepSWE | 69.9% | — |
| FrontierCode | 53.5% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1625 | — |
| FrontierSWE | 47% | — |
| GSO | 78.4% | — |
| MirrorCode | 63.9% | — |
| AlgoTune | — | 1.41 |
Agentic & Tool Use Claude Fable 5 leads
Claude Fable 5: 54.0 (#2), gpt-oss-120b: 12.2 (#153)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 63.6% | 4.4% |
| Vending-Bench 2 | 5,680 | -21.53 |
| Terminal-Bench | — | 18.7% |
| Remote Labor Index | 16.1% | — |
| τ²-bench Banking | 39.7% | — |
| PostTrainBench | 41.8% | — |
| GBAEval | 74.5% | — |
| GDP.pdf | 30% | — |
| LMArena Search | 1230 | — |
| METR Time Horizons | — | 56.6% |
Reasoning Claude Fable 5 leads
Claude Fable 5: 76.8 (#6), gpt-oss-120b: 20.0 (#245)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| SimpleBench | 81.9% | 22.1% |
| Kagi LLM Benchmark | 91.4% | 58.6% |
| CritPt | 28.6% | 1.1% |
| Chess Puzzles | 41% | 20% |
| LMArena Hard Prompts | 1508 | 1364 |
| Mystery Game Puzzles | 52% | 2% |
| DTBench | 98.4% | 76.3% |
| LMCA | 61.1% | 22.1% |
| Surface Evolver Bench | 95% | 25% |
| Epoch Capabilities Index | 162.06 | 139.93 |
| ARC-AGI-2 | 89.2% | — |
| NYT Connections (extended) | 92.7% | — |
| ARC-AGI-1 | 98.5% | — |
| EnigmaEval | 39.3% | — |
| EBR-Bench | 39.5% | — |
| Bench to the Future 3 | 0.13 | — |
Math Claude Fable 5 leads
Claude Fable 5: 88.5 (#5), gpt-oss-120b: 52.5 (#50)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 88.9% |
| LMArena Math | 1519 | 1389 |
| FrontierMath (Tiers 1-3) | 87% | — |
| FrontierMath Tier 4 | 90.2% | — |
| ProofBench | 95% | — |
| Omni-MATH | — | 68.8% |
| FrontierMath Erdős | 0% | — |
Knowledge Claude Fable 5 leads
Claude Fable 5: 62.2 (#25), gpt-oss-120b: 42.4 (#96)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 85.9% | 75.8% |
| LMArena Expert | 1534 | 1356 |
| SimpleQA Verified | 70.7% | — |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |
Multimodal Not comparable
Claude Fable 5: 45.3 (#17), gpt-oss-120b: —
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1324 | — |
| Blueprint-Bench 2 | 38.6% | — |
| Furniture Assembly | 35.8% | — |
| LMArena Document | 1496 | — |
Multilingual Claude Fable 5 leads
Claude Fable 5: 57.3 (#9), gpt-oss-120b: 48.0 (#147)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1481 | 1351 |
| LMArena Chinese | 1543 | 1385 |
| LMArena French | 1505 | 1369 |
| LMArena German | 1486 | 1353 |
| LMArena Japanese | 1506 | 1331 |
| LMArena Korean | 1488 | 1282 |
| LMArena Russian | 1504 | 1343 |
| LMArena Spanish | 1498 | 1389 |
Instruction Following Claude Fable 5 leads
Claude Fable 5: 78.6 (#8), gpt-oss-120b: 69.3 (#173)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1502 | 1318 |
| IFEval | — | 83.6% |
Long Context Claude Fable 5 leads
Claude Fable 5: 46.3 (#23), gpt-oss-120b: 31.4 (#278)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1509 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference Claude Fable 5 leads
Claude Fable 5: 75.9 (#5), gpt-oss-120b: 46.5 (#217)
| Benchmark | Claude Fable 5 | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1491 | 1365 |
| LMArena Creative Writing | 1494 | 1275 |
| EQ-Bench Creative Writing | 1943 | 961 |
| LMArena Multi-Turn | 1504 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |
| EQ-Bench 4 | 1340 | — |
Frequently asked questions
Is Claude Fable 5 better than gpt-oss-120b?
Claude Fable 5 is the stronger model overall, scoring 66.8 to 36.3 on the Noometry Index. gpt-oss-120b costs 285× 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-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Claude Fable 5 lists at $10 and $50.
Is Claude Fable 5 or gpt-oss-120b better for coding?
Claude Fable 5 scores higher on coding benchmarks: 70.6 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Claude Fable 5 does, with 1M tokens against 131K.
How many benchmarks do Claude Fable 5 and gpt-oss-120b share?
34 benchmarks have published results for both models. Claude Fable 5 has 62 scored results on Noometry and gpt-oss-120b has 48.