Claude Fable 5.2 vs GPT-5.5
Full profiles: Claude Fable 5.2 (Anthropic) and GPT-5.5 (OpenAI): every benchmark, price and rank.
Updated · Data from 13 sources
As of 9 Nov 2026, Claude Fable 5.2 and GPT-5.5 are neck and neck on quality (69.4 vs 64.3 on the BenchLeader Index, inside the ±7.0 margin), and GPT-5.5 is 1.4× cheaper at $11.25 vs $16.00 per 1M tokens blended. Claude Fable 5.2 leads in every category we measure both in.
Key facts (as of 9 Nov 2026)
- Claude Fable 5.2 scores 69.4 on the BenchLeader Index and GPT-5.5 scores 64.3 (BenchLeader, 9 Nov 2026).
- Claude Fable 5.2 costs $16.00 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-5.5 costs $11.25 per 1M tokens blended (list price, 9 Nov 2026).
- Of the 10 benchmarks both models have, Claude Fable 5.2 leads 9 (BenchLeader, 9 Nov 2026).
Claude Fable 5.2 benchmarks 
GPT-5.5 benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Claude Fable 5.2 | 80 vs 61 category score; SciCode (AA) 67.7% vs 55.8% | High |
| Reasoning | Claude Fable 5.2 | 78 vs 68 category score; ARC-AGI-2 92.5% vs 85.0% | High |
| Knowledge | Claude Fable 5.2 | 82 vs 64 category score | High |
| Long documents | Either | 66 vs 67 category score; AA-LCR v1.1 83.3% vs 84.3% | Either |
| Lowest cost | GPT-5.5 | $11.25 vs $16.00 per 1M blended; agentic coding session $0.420 vs $0.640 | – |
| Fastest first answer | Claude Fable 5.2 | 3.0 s (Claude Fable 5.2 (max)) vs 44.1 s (GPT-5.5 (xhigh)) to the first answer token | – |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Claude Fable 5.2 (max) and GPT-5.5 (xhigh) are neck and neck on quality (69.4 vs 64.3, within the ±7.0 margin).
- Claude Fable 5.2 (max) is stronger in coding, knowledge, reasoning.
- GPT-5.5 (xhigh) is stronger in long context, agents & tools, instruction following, maths, multimodal.
- GPT-5.5 (xhigh) is 1.4× cheaper ($11.25 vs $16.00 per 1M blended).
At a glance
| Spec | Claude Fable 5.2 | GPT-5.5 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 9 Nov 2026 | 23 Apr 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $8.00 / $40.00 / $0.200 | $5.00 / $30.00 / $0.500 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) | Leader |
|---|---|---|---|
| Reasoning | 78.4 | 68.3 | Claude Fable 5.2 |
| Coding | 80.2 | 61.0 | Claude Fable 5.2 |
| Agents & tools | – | 61.8 | not comparable |
| Maths | – | 69.8 | not comparable |
| Knowledge | 81.7 | 64.0 | Claude Fable 5.2 |
| Instruction following | – | 74.0 | not comparable |
| Multimodal | – | 62.3 | not comparable |
| Long context | 66.4 | 67.0 | level |
On one chart
Each domain on the index's own scale, where 50 is an average model. Click a domain to see the benchmarks behind it. Pick up to 5 models from the list.
Not drawn, measured for fewer than two of these models: Agents & tools, Maths, Instruction following, Multimodal, Human preference.
ModelIndex
Where sources disagree
Across the 10 benchmarks both have, Claude Fable 5.2 leads 9, GPT-5.5 leads 1.
| Overall index | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 69.4 | 64.3 | neck and neck |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page. Cost, tokens and time per benchmark task are measured: Artificial Analysis’ averages over its whole Intelligence Index run.
| Measure | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $16.00 | $11.25 |
| Input / output per 1M | $8.00 / $40.00 | $5.00 / $30.00 |
| Cached input per 1M | $0.200 | $0.500 |
| Price per task: chat reply | $0.015 | $0.011 |
| Price per task: summarize a 30-page report | $0.120 | $0.078 |
| Price per task: code edit | $0.108 | $0.075 |
| Price per task: agentic coding session | $0.640 | $0.420 |
| Price per task: structured extraction | $0.024 | $0.016 |
| Agentic coding session, input mostly cached | $0.289 | $0.217 |
| Cost per benchmark task (AA Index) | $26.54 | – |
| Output tokens per task (AA Index) | 274k | – |
| Of which reasoning | 193k | – |
| Output speed | 72 tok/s | 80 tok/s |
| Time to first answer | 3.0 s | 44.1 s |
| Time to a complete answer | – | 98.5 s |
Each model is compared at its best-ranked reasoning-effort setting. Claude Fable 5.2 (max) scores 69.4 and takes 3 s to its first answer token; Claude Fable 5.2 (high) scores 68.9 in 3 s, Claude Fable 5.2 (xhigh) scores 68.7 in 3 s. GPT-5.5 (xhigh) scores 64.3 and takes 44 s to its first answer token; GPT-5.5 (high) scores 63.7 in 23 s, GPT-5.5 scores 61.7 in 2 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
160 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 69.4 | 64.3 |
| Coding score | BenchLeader | 80.2 | 61.0 |
| Knowledge score | BenchLeader | 81.7 | 64.0 |
| Long context score | BenchLeader | 66.4 | 67.0 |
| Reasoning score | BenchLeader | 78.4 | 68.3 |
| Agents & tools score | BenchLeader | – | 61.8 |
| Instruction following score | BenchLeader | – | 74.0 |
| Maths score | BenchLeader | – | 69.8 |
| Multimodal score | BenchLeader | – | 62.3 |
| Blended price $/M | BenchLeader | $16.00 | $11.25 |
| Output speed | BenchLeader | 72 tok/s | 80 tok/s |
| Time to first answer | BenchLeader | 3.0 s | 44.1 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 90.7%† |
| SimpleBench | SimpleBench | – | 69.0%† |
| Arena Hard Prompts | Arena (LMArena) | – | 1499† |
| LiveBench 2.0 Reasoning | LiveBench | 88.8%† | – |
| LiveBench 1.0 Reasoning | LiveBench | – | 89.7% |
| GPQA Diamond (AA) | Artificial Analysis | – | 93.5% |
| Humanity's Last Exam (AA) | Artificial Analysis | 74.3% | 45.8% |
| HLE-Diamond (AA) | Artificial Analysis | 80.2% | – |
| GPQA Diamond (Vals) | Vals AI | – | 93.2% |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 88.8%† |
| ARC-AGI-1 | ARC Prize | 97.5% | 95.0% |
| ARC-AGI-2 | ARC Prize | 92.5% | 85.0% |
| ARC-AGI-3 | ARC Prize | 94.9% | 0.4%† |
| CritPt | Artificial Analysis | 35.4% | 27.1% |
| CritPt v2 hard subset (AA) | Artificial Analysis | 76.5% | – |
| MysteryMechanism | Vals AI | 51.8%† | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 63.0% |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 56.0% |
| CL-bench Life | Epoch AI Benchmarking Hub | – | 22.2%† |
| LMCA | Epoch AI Benchmarking Hub | – | 54.3% |
| DTBench | Epoch AI Benchmarking Hub | – | 96.0% |
| ForecastBench | ForecastBench | – | 64.5%† |
| BTF-3 | Epoch AI Benchmarking Hub | – | 14.3%† |
| Coding | |||
| SciCode | SciCode | – | 55.8% |
| WeirdML | WeirdML | – | 84.9% |
Show all 160 benchmarks
| Metric | Publisher | Claude Fable 5.2 (max) | GPT-5.5 (xhigh) |
|---|---|---|---|
| Coding | |||
| FrontierCode | Cognition | – | 43.0%† |
| GSO-Bench | GSO-Bench | – | 40.2% |
| Arena Coding | Arena (LMArena) | – | 1510† |
| Arena WebDev | Arena (LMArena) | – | 1512 |
| LiveBench 2.0 Coding | LiveBench | 79.2%† | – |
| LiveBench 1.0 Coding | LiveBench | – | 82.2% |
| SciCode (AA) | Artificial Analysis | 67.7% | 55.8% |
| LiveCodeBench | Vals AI | – | 85.3% |
| SWE-bench (Vals) | Vals AI | – | 82.6% |
| Code Migration | Vals AI | – | 45.2% |
| Code Migration v1.1 | Vals AI | 83.3%† | – |
| ProgramBench | Vals AI | 19.0%† | 0.5% |
| SWE-bench Multimodal | Vals AI | 62.3%† | – |
| Vibe Code Bench v1.1 | Vals AI | 93.8%† | 69.8% |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 45.4% |
| SWE Atlas: Refactoring | Scale AI SEAL | – | 44.8% |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 42.6% |
| MirrorCode | Epoch AI Benchmarking Hub | – | 10.0%† |
| Surface Evolver Bench | Epoch AI Benchmarking Hub | – | 88.1%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 67.0% |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 1943.0 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | 14.7% |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 84.3% |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 49.0† |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 59.7 |
| Design Arena Website | Design Arena | – | 1253† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 56.2% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 48.8% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 84.7%† |
| OSWorld-Verified 2.0 | OSWorld | – | 13.0% |
| Remote Labor Index | Scale AI / CAIS | – | 6.3%† |
| APEX-Agents | Mercor | – | 55.1%† |
| LiveBench Long Agentic | LiveBench | 82.9%† | – |
| LiveBench Browser | LiveBench | 86.9%† | – |
| LiveBench Agentic Coding | LiveBench | – | 54.0% |
| Terminal-Bench Hard | Artificial Analysis | – | 60.6% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 93.9% |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 76.4%† |
| MCP Atlas | Scale AI SEAL | – | 75.3% |
| HiL-Bench | Scale AI SEAL | – | 39.7%† |
| τ³-bench Banking | τ²-bench | – | 44.6% |
| GDPval-AA v2.1 | Artificial Analysis | 1831 | – |
| τ³-Banking (AA) | Artificial Analysis | – | 39.0% |
| ITBench SRE (AA) | Artificial Analysis | – | 45.8% |
| Analyst Agent (AA) | Artificial Analysis | – | 50.0% |
| APEX-Agents (AA) | Artificial Analysis | – | 37.7% |
| Finance Agent v2 | Vals AI | – | 51.8% |
| Finance Agent v2.1 | Vals AI | 69.5%† | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 5.0%† | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 3.8% |
| Legal Research Bench | Vals AI | 46.1%† | 40.4% |
| SkillsBench | Vals AI | – | 62.2% |
| SREBench | Vals AI | – | 3.8% |
| Tax Agent Bench | Vals AI | 32.1%† | 21.8% |
| Terminal-Bench 2.0 (Vals) | Vals AI | – | 73.2%† |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | – |
| Terminal-Bench Science | Vals AI | 55.7%† | – |
| Time Horizon Index: KSP | Vals AI | – | 8.3% |
| Arena Search | Arena (LMArena) | – | 1226† |
| DeepResearch Bench | DeepResearch Bench | – | 54.0%† |
| PostTrainBench | Epoch AI Benchmarking Hub | – | 27.2% |
| ExploitBench | Epoch AI Benchmarking Hub | – | 47.4%† |
| GBAEval | Epoch AI Benchmarking Hub | – | 53.2%† |
| Vending-Bench 2 | Andon Labs | – | 7523.8† |
| AutomationBench | Artificial Analysis | 76.4% | 47.3% |
| GDP.pdf (AA) | Artificial Analysis | 24.2% | 21.2% |
| Harvey LAB-AA v1.1 | Harvey | 7.2% | – |
| EnterpriseOps-Gym | Artificial Analysis | – | 46.6% |
| AA-Briefcase v1.1 | Artificial Analysis | 1785 | 1135 |
| Design Arena Agentic | Design Arena | – | 1108† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 73.3% | – |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 85.3% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 72.5% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 84.4%† |
| ProofBench | Vals AI | – | 50.0% |
| LiveBench Mathematics | LiveBench | – | 95.9% |
| AIME 2026 | MathArena | – | 100.0% |
| HMMT February 2026 | MathArena | – | 98.5% |
| USAMO 2026 | MathArena | – | 98.2% |
| MathArena Apex | MathArena | – | 80.2% |
| Arena Maths | Arena (LMArena) | – | 1500† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 63.0% |
| LiveBench 2.0 Data Analysis | LiveBench | 81.5%† | – |
| LiveBench Research | LiveBench | 94.5%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | – | 81.6% |
| AA-Omniscience v1.1 | Artificial Analysis | 66.8 | – |
| MMLU-Pro | Vals AI | – | 88.1% |
| LegalBench | Vals AI | – | 86.5% |
| CorpFin | Vals AI | – | 68.4% |
| TaxEval | Vals AI | – | 75.0% |
| BioMysteryBench | Vals AI | 83.7%† | – |
| CaseLaw v2 | Vals AI | – | 66.2% |
| Excel Modeling Benchmark | Vals AI | – | 64.5% |
| Excel Modeling Benchmark v1.1 | Vals AI | 77.4%† | – |
| MedCode | Vals AI | – | 49.1% |
| MedScribe | Vals AI | – | 86.9% |
| Public Benefits Bench v1.1 | Vals AI | – | 60.9% |
| DrugDiscoveryBench | Scale AI SEAL | – | 51.6% |
| EBR-bench | Epoch AI Benchmarking Hub | – | 34.3% |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 26.0% |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 79.1% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 40.7% | – |
| HealthBench Professional (AA) | OpenAI | 67.7% | – |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 87.4% |
| LiveBench Instruction Following | LiveBench | – | 70.7% |
| IFBench | Artificial Analysis | – | 75.8% |
| Arena Instruction Following | Arena (LMArena) | – | 1475† |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1476† |
| EQ-Bench 4 | EQ-Bench | – | 1295† |
| Arena Creative Writing | Arena (LMArena) | – | 1448† |
| Arena Multi-turn | Arena (LMArena) | – | 1484† |
| Arena Longer Queries | Arena (LMArena) | – | 1487† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1296† |
| LiveBench Design | LiveBench | 85.1%† | – |
| MMMU-Pro | Artificial Analysis | – | 79.9% |
| MMMU-Pro (Vals) | Vals AI | – | 88.3% |
| MortgageTax | Vals AI | – | 68.8% |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 36.2%† |
| Code2Video-AA | Artificial Analysis | – | 1000 |
| AA-3D | Artificial Analysis | 1271 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 84.3% |
| Arena Document | Arena (LMArena) | – | 1479† |
| MLCR | Artificial Analysis | – | 28.9% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 160.0† |
| LiveBench 2.0 | LiveBench | 84.9%† | – |
| LiveBench 1.0 | LiveBench | – | 80.2% |
| AA Intelligence Index v5.0 | Artificial Analysis | 64.6 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 38.4 |
| AA Progress Index v1 | Artificial Analysis | – | 320 |
| Vals Index | Vals AI | 71.3† | – |
| Vals Finance Index | Vals AI | 63.6%† | – |
| Vals RSI Index | Vals AI | – | 16.5% |
| Vals Science Index | Vals AI | 63.7%† | – |
| Vals Multimodal Index | Vals AI | – | 68.1% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 16.3% |
Data as of 2026-11-09. Best configuration of each model; every score links to its source on the model pages.
DuelLab GameBench 2, release gb2-20261103-eaea31df94e6 (https://benchmarks.duellab.org/releases/gb2-20261103-eaea31df94e6/), CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Scores mapped to BenchLeader model names; not endorsed by DuelLab.
What changed
- GPT-5.5 (xhigh): 1 score revision.
- GPT-5.5 (xhigh): 1 new result (AA Progress Index v1).
- GPT-5.5 (xhigh): cost per task moved from $2.63/M to $2.10/M (20% down).
- GPT-5.5 (xhigh): 1 new result (AA-Briefcase v1.1).
- GPT-5.5 (xhigh): 2 new results (Harvey's Legal Agent Benchmark v1, Vals RSI Index).
- GPT-5.5 (xhigh): 1 score revision.
- GPT-5.5 (xhigh): BenchLeader Index 65.1 → 64.6.
- GPT-5.5 (xhigh): 6 new results (USAMO 2026, AutomationBench, GDP.pdf (AA)…).
- GPT-5.5 (xhigh): 1 score revision.
- GPT-5.5 (xhigh): 1 new result (Chess Puzzles); 1 score revision.
Related comparisons
- Claude Fable 5.2 vs Claude Opus 5.5
- Claude Fable 5.2 vs Gemini 4 Carbon
- Claude Fable 5.2 vs GPT-6 Astra
- GPT-5.5 vs Claude Opus 5.5
- GPT-5.5 vs Gemini 4 Carbon
- GPT-5.5 vs GPT-6 Astra
- Claude Sonnet 5.5 vs GPT-5.5
- Claude Fable 5.2 vs GPT-5.4
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Claude Fable 5.2 vs GPT-5.5: questions
- Is Claude Fable 5.2 better than GPT-5.5?
- Claude Fable 5.2 (max) and GPT-5.5 (xhigh) are neck and neck on quality (69.4 vs 64.3, within the ±7.0 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 9 Nov 2026, so check the category scores for your use.
- Claude Fable 5.2 vs GPT-5.5: which is cheaper, and will that change?
- GPT-5.5 is cheaper today: $11.25 against $16.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is Claude Fable 5.2 or GPT-5.5 better for coding?
- Claude Fable 5.2 scores higher in coding (80 vs 61 on the category index, where 50 is average).
- Which is faster, Claude Fable 5.2 or GPT-5.5?
- GPT-5.5 streams faster (80 vs 72 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- They are level on long-context benchmarks (66 vs 67). Both take about 1.1M tokens: Claude Fable 5.2 1,000,000, GPT-5.5 1,050,000.
More questions (3)
- Which has lower latency?
- Claude Fable 5.2 answers first: 3.0 s against 44.1 s to the first answer token, as Artificial Analysis measures it. Reasoning effort drives most of this: a higher setting thinks longer before it answers.
- Do independent benchmarks agree on Claude Fable 5.2 vs GPT-5.5?
- Across the 10 benchmarks both have, Claude Fable 5.2 leads 9, GPT-5.5 leads 1.
- When was Claude Fable 5.2 released?
- Claude Fable 5.2 was released on 9 Nov 2026.