Claude Fable 5.2 vs GPT-6 Astra
Full profiles: Claude Fable 5.2 (Anthropic) and GPT-6 Astra (OpenAI): every benchmark, price and rank.
Updated · Data from 12 sources
As of 9 Nov 2026, Claude Fable 5.2 and GPT-6 Astra are neck and neck on quality (69.4 vs 68.1 on the BenchLeader Index, inside the ±8.9 margin), and Claude Fable 5.2 is 1.3× cheaper at $16.00 vs $20.00 per 1M tokens blended. Choose Claude Fable 5.2 for knowledge (82 vs 56) and fast first answers (3.0 s vs 4.8 s), where it also streams ~2× faster; choose GPT-6 Astra for reasoning (86 vs 78) and coding (83 vs 80).
Key facts (as of 9 Nov 2026)
- Claude Fable 5.2 scores 69.4 on the BenchLeader Index and GPT-6 Astra scores 68.1 (BenchLeader, 9 Nov 2026).
- Claude Fable 5.2 costs $16.00 per 1M tokens blended (list price, 9 Nov 2026).
- GPT-6 Astra costs $20.00 per 1M tokens blended (list price, 9 Nov 2026).
- Claude Fable 5.2 streams ~2× faster than GPT-6 Astra (Artificial Analysis, 7-day median, 9 Nov 2026).
Claude Fable 5.2 benchmarks 
GPT-6 Astra 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 | GPT-6 Astra | 83 vs 80 category score | Medium |
| Reasoning | GPT-6 Astra | 86 vs 78 category score | Medium |
| Knowledge | Claude Fable 5.2 | 82 vs 56 category score | High |
| Lowest cost | Claude Fable 5.2 | $16.00 vs $20.00 per 1M blended; agentic coding session $0.640 vs $0.800 | – |
| Fastest first answer | Claude Fable 5.2 | 3.0 s (Claude Fable 5.2 (max)) vs 4.8 s (GPT-6 Astra) 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-6 Astra are neck and neck on quality (69.4 vs 68.1, within the ±8.9 margin).
- Claude Fable 5.2 (max) is stronger in knowledge, long context.
- GPT-6 Astra is stronger in coding, reasoning, agents & tools, composite, maths.
- Claude Fable 5.2 (max) is 1.3× cheaper ($16.00 vs $20.00 per 1M blended).
- Claude Fable 5.2 (max) streams 2.7× faster (72 vs 27 tokens per second).
At a glance
| Spec | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Released | 9 Nov 2026 | 2 Sep 2026 |
| Context window (tokens) | 1,000,000 | 1,050,000 |
| Compared at | Claude Fable 5.2 (max) | GPT-6 Astra |
| Open weights | No | No |
| Price in / out / cached input per 1M | $8.00 / $40.00 / $0.200 | $10.00 / $50.00 / $1.00 |
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-6 Astra | Leader |
|---|---|---|---|
| Reasoning | 78.4 | 85.6 | GPT-6 Astra |
| Coding | 80.2 | 83.2 | GPT-6 Astra |
| Agents & tools | – | 69.2 | not comparable |
| Maths | – | 77.9 | not comparable |
| Knowledge | 81.7 | 56.1 | Claude Fable 5.2 |
| Long context | 66.4 | – | not comparable |
| Composite | – | 72.6 | not comparable |
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, Long context, Multimodal, Human preference.
Where sources disagree
Across the 0 benchmarks both have, Claude Fable 5.2 leads 0, GPT-6 Astra leads 0. Every overall index that rates both puts Claude Fable 5.2 ahead.
| Overall index | Claude Fable 5.2 (max) | GPT-6 Astra | Leader |
|---|---|---|---|
| BenchLeader Index | 69.4 | 68.1 | neck and neck |
| AA Intelligence Index v5.0 | 64.6 | 43.3† | Claude Fable 5.2 |
| Vals Index | 71.3† | 65.0 | Claude Fable 5.2 |
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-6 Astra |
|---|---|---|
| Blended price per 1M tokens | $16.00 | $20.00 |
| Input / output per 1M | $8.00 / $40.00 | $10.00 / $50.00 |
| Cached input per 1M | $0.200 | $1.00 |
| Price per task: chat reply | $0.015 | $0.019 |
| Price per task: summarize a 30-page report | $0.120 | $0.150 |
| Price per task: code edit | $0.108 | $0.135 |
| Price per task: agentic coding session | $0.640 | $0.800 |
| Price per task: structured extraction | $0.024 | $0.030 |
| Agentic coding session, input mostly cached | $0.289 | $0.395 |
| Cost per benchmark task (AA Index) | $26.54 | – |
| Output tokens per task (AA Index) | 274k | – |
| Of which reasoning | 193k | – |
| Output speed | 72 tok/s | 27 tok/s |
| Time to first answer | 3.0 s | 4.8 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-6 Astra scores 68.1 and takes 5 s to its first answer token; GPT-6 Astra (max) scores 67.4 in 232 s, GPT-6 Astra (high) scores 66.5 in 64 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
144 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-6 Astra |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 69.4 | 68.1 |
| Coding score | BenchLeader | 80.2 | 83.2 |
| Knowledge score | BenchLeader | 81.7 | 56.1 |
| Long context score | BenchLeader | 66.4 | – |
| Reasoning score | BenchLeader | 78.4 | 85.6 |
| Agents & tools score | BenchLeader | – | 69.2 |
| Composite score | BenchLeader | – | 72.6 |
| Maths score | BenchLeader | – | 77.9 |
| Blended price $/M | BenchLeader | $16.00 | $20.00 |
| Output speed | BenchLeader | 72 tok/s | 27 tok/s |
| Time to first answer | BenchLeader | 3.0 s | 4.8 s |
| Context window | BenchLeader | 1,000,000 tokens | 1,050,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 95.8%† |
| Humanity's Last Exam | Scale AI / CAIS | – | 54.8% |
| Arena Hard Prompts | Arena (LMArena) | – | 1500† |
| LiveBench 2.0 Reasoning | LiveBench | 88.8%† | 92.7% |
| LiveBench 1.0 Reasoning | LiveBench | – | 92.7%† |
| GPQA Diamond (AA) | Artificial Analysis | – | 96.1%† |
| Humanity's Last Exam (AA) | Artificial Analysis | 74.3% | 54.7%† |
| HLE-Diamond (AA) | Artificial Analysis | 80.2% | 63.8%† |
| ARC-AGI-1 | ARC Prize | 97.5% | 97.5%† |
| ARC-AGI-2 | ARC Prize | 92.5% | 95.0%† |
| ARC-AGI-3 | ARC Prize | 94.9% | 98.6%† |
| CritPt | Artificial Analysis | 35.4% | 31.7%† |
| CritPt v2 hard subset (AA) | Artificial Analysis | 76.5% | 70.5%† |
| MysteryMechanism | Vals AI | 51.8%† | 53.1%† |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 74.0%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 84.0%† |
| LMCA | Epoch AI Benchmarking Hub | – | 64.1% |
| DTBench | Epoch AI Benchmarking Hub | – | 97.1% |
| BTF-3 | Epoch AI Benchmarking Hub | – | 13.5%† |
| Coding | |||
| SciCode | SciCode | – | 56.5%† |
| WeirdML | WeirdML | – | 93.3%† |
| FrontierCode | Cognition | – | 53.3%† |
| GSO-Bench | GSO-Bench | – | 79.4% |
| Arena Coding | Arena (LMArena) | – | 1548† |
| Arena WebDev | Arena (LMArena) | – | 1767† |
Show all 144 benchmarks
| Metric | Publisher | Claude Fable 5.2 (max) | GPT-6 Astra |
|---|---|---|---|
| Coding | |||
| LiveBench 2.0 Coding | LiveBench | 79.2%† | 87.5% |
| LiveBench 1.0 Coding | LiveBench | – | 80.4%† |
| SciCode (AA) | Artificial Analysis | 67.7% | 56.5%† |
| IOI | Vals AI | – | 100.0%† |
| Code Migration | Vals AI | – | 67.7%† |
| Code Migration v1.1 | Vals AI | 83.3%† | 80.6%† |
| ProgramBench | Vals AI | 19.0%† | 5.5%† |
| SWE-bench Multimodal | Vals AI | 62.3%† | 43.3%† |
| Vibe Code Bench 1-100 | Vals AI | – | 27.6%† |
| Vibe Code Bench v1.1 | Vals AI | 93.8%† | 89.6%† |
| SWE Atlas: Codebase QnA | Scale AI SEAL | – | 59.1%† |
| SWE Atlas: Refactoring | Scale AI SEAL | – | 59.0%† |
| SWE Atlas: Test Writing | Scale AI SEAL | – | 50.7%† |
| MirrorCode | Epoch AI Benchmarking Hub | – | 46.7%† |
| DeepSWE v1.1 | Epoch AI Benchmarking Hub | – | 73.2%† |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 2951.3† |
| FrontierSWE | Epoch AI Benchmarking Hub | – | 65.5%† |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 59.6% | 59.1%† |
| Terminal-Bench 2.1 (AA) | Artificial Analysis | – | 88.4%† |
| Startrise Frontend | Startrise | – | 79.9% |
| GameBench 2 Overall | DuelLab GameBench 2 | – | 88.7 |
| GameBench 2 by reasoning setting | DuelLab GameBench 2 | – | 94.0† |
| Design Arena Website | Design Arena | – | 1333† |
| FrontierCode 1.1 (AA) | Artificial Analysis | 56.2% | 47.0%† |
| FrontierCode 2.0 (AA) | Artificial Analysis | 48.8% | 42.4%† |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | – | 60.3%† |
| Remote Labor Index | Scale AI / CAIS | – | 20.8% |
| APEX-Agents | Mercor | – | 64.7% |
| Arena Agent | Arena (LMArena) | – | 14.2† |
| LiveBench Long Agentic | LiveBench | 82.9%† | 75.6% |
| LiveBench Browser | LiveBench | 86.9%† | 91.3% |
| LiveBench Agentic Coding | LiveBench | – | 57.3%† |
| Terminal-Bench 2.1 (Vals) | Vals AI | – | 87.3%† |
| HiL-Bench | Scale AI SEAL | – | 65.3% |
| GDPval-AA v2.1, normalised score | Artificial Analysis | – | 53.7%† |
| GDPval-AA v2.1 | Artificial Analysis | 1831 | 1584† |
| τ³-Banking (AA) | Artificial Analysis | – | 41.4%† |
| ITBench SRE (AA) | Artificial Analysis | – | 48.6%† |
| Analyst Agent (AA) | Artificial Analysis | – | 51.3%† |
| CUA-bench | Vals AI | – | 19.2%† |
| CyberBench | Vals AI | – | 41.1%† |
| Finance Agent v2 | Vals AI | – | 53.5%† |
| Finance Agent v2.1 | Vals AI | 69.5%† | 60.4%† |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 5.0%† | 7.1%† |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 5.4%† |
| Legal Research Bench | Vals AI | 46.1%† | 39.4%† |
| Robo-bench | Vals AI | – | 23.3%† |
| SREBench | Vals AI | – | 56.9%† |
| Tax Agent Bench | Vals AI | 32.1%† | 20.7%† |
| Terminal-Bench 4.0 (Vals) | Vals AI | 65.2%† | 59.6%† |
| Terminal-Bench Science | Vals AI | 55.7%† | 62.9%† |
| Time Horizon Index: KSP | Vals AI | – | 90.5%† |
| Arena Search | Arena (LMArena) | – | 1245† |
| BALROG | BALROG | – | 68.3%† |
| Vending-Bench 2 | Andon Labs | – | 15514.7 |
| AutomationBench | Artificial Analysis | 76.4% | 68.5%† |
| GDP.pdf (AA) | Artificial Analysis | 24.2% | 31.0%† |
| Harvey LAB-AA v1.1 | Harvey | 7.2% | 8.6%† |
| AA-Briefcase v1.1 | Artificial Analysis | 1785 | 1569† |
| GDPx | GDPx by SemiAnalysis | – | 9.6%† |
| Design Arena Agentic | Design Arena | – | 1393† |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 73.3% | 63.3%† |
| CWE-Bench (AA) | Artificial Analysis | – | 63.3%† |
| DeepSec-Bench (AA) | Artificial Analysis | – | 36.9%† |
| CyberGym end-to-end (AA) | Artificial Analysis | – | 0.0%† |
| FACTS Search | – | 93.3% | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 93.7%† |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | – | 97.6%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 100.0%† |
| ProofBench | Vals AI | – | 99.0% |
| LiveBench Mathematics | LiveBench | – | 96.8%† |
| Arena Maths | Arena (LMArena) | – | 1481† |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 75.6%† |
| LiveBench 2.0 Data Analysis | LiveBench | 81.5%† | 83.0% |
| LiveBench Research | LiveBench | 94.5%† | 90.6% |
| LiveBench 1.0 Data Analysis | LiveBench | – | 83.0%† |
| AA-Omniscience v1.1 | Artificial Analysis | 66.8 | 42.4† |
| PRBench Finance | Scale AI SEAL | – | 47.5% |
| PRBench Legal | Scale AI SEAL | – | 48.4% |
| BioMysteryBench | Vals AI | 83.7%† | 79.3%† |
| Excel Modeling Benchmark | Vals AI | – | 71.7%† |
| Excel Modeling Benchmark v1.1 | Vals AI | 77.4%† | 72.7%† |
| MedCode | Vals AI | – | 48.5%† |
| MedScribe | Vals AI | – | 87.9%† |
| DrugDiscoveryBench | Scale AI SEAL | – | 68.7%† |
| EBR-bench | Epoch AI Benchmarking Hub | – | 76.2%† |
| GDP.pdf | Epoch AI Benchmarking Hub | – | 34.2%† |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 79.1% | 61.6%† |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 40.7% | 50.2%† |
| HealthBench Professional (AA) | OpenAI | 67.7% | 66.0%† |
| Instruction following | |||
| LiveBench Language | LiveBench | – | 89.4%† |
| LiveBench Instruction Following | LiveBench | – | 75.6%† |
| Arena Instruction Following | Arena (LMArena) | – | 1473† |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1475† |
| Arena Creative Writing | Arena (LMArena) | – | 1450† |
| Arena Multi-turn | Arena (LMArena) | – | 1483† |
| Arena Longer Queries | Arena (LMArena) | – | 1486† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1279† |
| LiveBench Design | LiveBench | 85.1%† | 80.0% |
| MMMU-Pro | Artificial Analysis | – | 86.9%† |
| Blueprint-Bench 2 | Epoch AI Benchmarking Hub | – | 49.7% |
| Code2Video-AA | Artificial Analysis | – | 1427† |
| AA-3D | Artificial Analysis | 1271 | 1168† |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.3% | 80.7%† |
| Arena Document | Arena (LMArena) | – | 1473† |
| MLCR | Artificial Analysis | – | 41.7%† |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 169.1 |
| LiveBench 2.0 | LiveBench | 84.9%† | 84.5% |
| LiveBench 1.0 | LiveBench | – | 82.2%† |
| AA Intelligence Index v5.0 | Artificial Analysis | 64.6 | 56.9† |
| AA Intelligence Index v4.3.2 | Artificial Analysis | – | 52.7† |
| AA Progress Index v1 | Artificial Analysis | – | 349.6† |
| Vals Index | Vals AI | 71.3† | 65.0† |
| Vals Finance Index | Vals AI | 63.6%† | 55.6%† |
| Vals RSI Index | Vals AI | – | 27.1%† |
| Vals Science Index | Vals AI | 63.7%† | 65.1%† |
| Vals Router Index | Vals AI | – | 65.0% |
| AA Cyber Index | Artificial Analysis | – | 33.4† |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 12.1% |
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-6 Astra: 5 score revisions.
- GPT-6 Astra: 1 score revision.
- GPT-6 Astra: 1 new result (FORTRESS); 1 score revision.
- GPT-6 Astra: 1 score revision.
- GPT-6 Astra: 3 score revisions.
- GPT-6 Astra: 1 new result (Vals Router Index).
- GPT-6 Astra: 1 score revision.
- GPT-6 Astra: 8 new results (LiveBench 2.0, LiveBench Long Agentic, LiveBench 2.0 Reasoning…).
- GPT-6 Astra: 1 new result (Startrise Frontend).
- GPT-6 Astra: BenchLeader Index 68.9 → 68.0.
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-6.1 Sol
- GPT-6 Astra vs Claude Opus 5.5
- GPT-6 Astra vs Gemini 4 Carbon
- GPT-6 Astra vs GPT-6.1 Sol
- Claude Haiku 5.5 vs GPT-6 Astra
- Claude Fable 5.2 vs GPT-5.6 Sol
Rankings: Best LLM overall · Best LLM for agents · Best cheap LLMs
Claude Fable 5.2 vs GPT-6 Astra: questions
- Is Claude Fable 5.2 better than GPT-6 Astra?
- Claude Fable 5.2 (max) and GPT-6 Astra are neck and neck on quality (69.4 vs 68.1, within the ±8.9 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-6 Astra: which is cheaper, and will that change?
- Claude Fable 5.2 is cheaper today: $16.00 against $20.00 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-6 Astra better for coding?
- GPT-6 Astra scores higher in coding (83 vs 80 on the category index, where 50 is average).
- Which is faster, Claude Fable 5.2 or GPT-6 Astra?
- Claude Fable 5.2 streams ~2× faster (72 vs 27 output tokens per second, Artificial Analysis 7-day median).
- Which is better for long documents?
- Both take about 1.1M tokens: Claude Fable 5.2 1,000,000, GPT-6 Astra 1,050,000.
More questions (2)
- Which has lower latency?
- Claude Fable 5.2 answers first: 3.0 s against 4.8 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.
- When was Claude Fable 5.2 released?
- Claude Fable 5.2 was released on 9 Nov 2026.