[whatmodel.sh](../../README.md) / Claude Fable 5
# Claude Fable 5
Anthropic · released 2026-06 · closed weights
**score 79.4** ±0.5 · #14 of 732 · from 48 benchmark results across 11 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Claude Fable 5 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄▄●▁ #12 of 313 33% 15
coding ▁▁▁▃▅▇█▇▅▄●▁ #20 of 469 16% 7
chat & writing ▁▁▁▃▆▄▆█▆▇▇● #6 of 402* 2.3% 1
math ▁▁▃▅█▆▄█▄▂●▁ #7 of 431 30% 11
vision & documents ▁▁▂▂▁▃▄▂▆▇█● #2 of 153 3.6% 4
web search ▁▁▁▅▄█●▄·▁·▁ #22 of 84 3.1% 3
agent tasks · #12 of 313 · 33% of its score
Beats [GPT-5.5 Pro](gpt-5-5-pro.md), [GPT-5.6 Sol](gpt-5-6-sol.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Terminal-Bench 4.0 44.5% █▅▅▅▅·▅▃●▅▅▅ beats 14/20 very hard · avg 0.7% 5.9% sharp · at its level · several runs
Arena Agent (October 2026 aggrega… 0.0804 IPS ▁▁▁▃▃▂█▁●▂▁▁ beats 50/59 avg -0.379 IPS 5.8% sharp
SWE-bench Pro (split unspecified)… 80.0% ▄█·▄·▄●█···▄ beats 6/9 very hard · avg 7.4% 4.6% sharp
SWE-bench Multilingual (maker) 86.6% ▃··▃·▅●▃█··▃ beats 4/9 hard · avg 31% 2.2% typical
SWE-bench Verified (maker) 95.0% ▁▁▁▁▁▁▂▄█▆▁● beats 137/138 hard · avg 28% 1.8% sharp · maxed out
τ³-bench banking_knowledge (v1.0.… 39.7% ▃▃▂▃·▆▃▂●▅▅▂ beats 18/25 very hard · avg 4.3% 1.7% at its level
Humanity's Last Exam with tools (… 63.8% ▁······▁·▄·● beats 5/10 very hard · avg 14% 1.4% at its level
E2E-SWE (build a working codebase… 65.3% ▃··▅··▃█▃▅▃● beats 11/12 very hard · avg 3.7% 1.4% sharp · at its level
Terminal-Bench 2.1 (maker) 84.3% ▁·▁·▁▁▄▃▆█▅● beats 50/63 hard · avg 19% 1.3% sharp
Chartography with tools (maker) 84.2% ▃········▃●█ beats 3/7 very hard · avg 6.5% 1.3% sharp
OSWorld-Verified (maker) 85.0% █▃·▂▅▂▃▂▅▃▆● beats 26/27 hard · avg 28% 1.3% typical
Toolathlon (Tool Decathlon) (make… 61.7% █···█··█·██● beats 5/5 hard · avg 16% 1.1% at its level
DeepSWE v1.1 (resolved) (maker) 69.7% █·····██●█·█ beats 3/5 hard · avg 19% 1.1% at its level
BrowseComp with context managemen… 87.4% ▄·▄▂·█▂·▆●·▂ beats 14/15 medium · avg 47% 0.8% typical
OSWorld 2.0, 2026.08.08 release (… 72.9% ●····█·····█ beats 0/2 hard · avg 28% 0.5% at its level · small run
coding · #20 of 469 · 16% of its score
Beats [GPT-5.6 Sol](gpt-5-6-sol.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Qwen3.8 Max (0902)](qwen3-8-max-0902.md), [Kimi K3](kimi-k3.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Pro (split unspecified)… 80.0% ▄█·▄·▄●█···▄ beats 6/9 very hard · avg 7.4% 4.6% sharp
Arena WebDev (Code Arena) 1624.1 ▁▁▃▂▅█▃▆●▅▁▂ beats 106/129 avg 981 4.1% sharp
SWE-bench Multilingual (maker) 86.6% ▃··▃·▅●▃█··▃ beats 4/9 hard · avg 31% 2.2% typical
SWE-bench Verified (maker) 95.0% ▁▁▁▁▁▁▂▄█▆▁● beats 137/138 hard · avg 28% 1.8% sharp · maxed out
E2E-SWE (build a working codebase… 65.3% ▃··▅··▃█▃▅▃● beats 11/12 very hard · avg 3.7% 1.4% sharp · at its level
Luau Leetcode fully solved (Torpe… 85.1% ▁▁▁▁·▁▂▃▅▃█● beats 46/49 hard · avg 17% 1.1% sharp
DeepSWE v1.1 (resolved) (maker) 69.7% █·····██●█·█ beats 3/5 hard · avg 19% 1.1% at its level
chat & writing · #6 of 402 · 2.3% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [GPT-6.1 Sol](gpt-6-1-sol.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md) here, though they rank higher overall. Trails [Muse Wonder](muse-wonder.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1503.9 ▁▁▁▂▃▃▃▆▆▇█● beats 380/385 avg 1260 2.3% blunt
math · #7 of 431 · 30% of its score
Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [Claude Opus 5](claude-opus-5.md), [GPT-5.5 Pro](gpt-5-5-pro.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tier 4 v2 90.2% ▁▂▁▂█▅▂▃●▁▁▁ beats 63/67 very hard · avg 0.1% 8.8% sharp · at its level
FrontierMath tiers 1–3 v2 87.0% ▂··▁▁▃▄▄█▆●▃ beats 77/86 very hard · avg 1.2% 5.7% sharp
MathArena ArXivMath April 2026 (4… 72.5% ▁·····▅██●·▁ beats 18/20 very hard · avg 7.6% 4.0% sharp · at its level
MathArena ArXivMath June 2026 (48… 85.4% ▂···▄·██▆●▄▂ beats 16/19 very hard · avg 5.1% 3.6% sharp
MathArena BrokenArXiv April 2026 … 54.1% ▂▂▆█▃●▂▂▂··▂ beats 14/18 very hard · avg 0.8% 1.9% sharp · at its level · noisy
MathArena ArXivLean June 2026 (46… 15.2% ▄····▄▄●██·▄ beats 3/9 very hard · avg 0.9% 1.7% sharp · at its level
MathArena BrokenArXiv May 2026 (5… 44.5% ██·▄▄▄····●▄ beats 7/8 very hard · avg 1.9% 1.4% sharp · at its level
MathArena ArXivMath May 2026 (40 … 87.5% ▁··▁▄█▄▁·●·▁ beats 16/17 very hard · avg 11% 1.3% at its level
MathArena BrokenArXiv June 2026 (… 47.8% ▄··█▂▂▂●▄··▂ beats 9/14 very hard · avg 1.3% 1.3% sharp · at its level · noisy
FrontierMath: Erdős problems 0.0% ●··········▆ beats 2/6 very hard · avg 0.0% 0.3% sharp · too hard for it · noisy
OTIS mock AIME 2024–2025 99.7% ▃▃▄▃▃▃▇▇█▃▂● beats 193/197 very hard · avg 7.2% 0.1% sharp · maxed out · noisy
vision & documents · #2 of 153 · 3.6% of its score
Beats [GPT-6 Astra](gpt-6-astra.md), [Claude Opus 5.5](claude-opus-5-5.md), [GPT-6.1 Sol](gpt-6-1-sol.md) here, though they rank higher overall. Trails [Claude Mythos Preview (limited preview)](claude-mythos-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1308.7 ▁▁▂▂▂▂▃▄▅▅█● beats 152/152 avg 1061 1.5% blunt
CharXiv (reasoning questions) 86.6% ▂▅▃▅▅▆▆▅▆█▆● beats 99/101 hard · avg 31% 0.9% small run
Arena document (style controlled) 1498.9 ▁▁▃▃▄▃▇█▅▇▄● beats 37/37 avg 1331 0.8% blunt
CharXiv descriptive questions 94.3% ▂▄▄▅▇▄██▇▂●▂ beats 57/59 medium · avg 64% 0.5% maxed out · small run
web search · #22 of 84 · 3.1% of its score
Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 63.8% ▁······▁·▄·● beats 5/10 very hard · avg 14% 1.4% at its level
BrowseComp with context managemen… 87.4% ▄·▄▂·█▂·▆●·▂ beats 14/15 medium · avg 47% 0.8% typical
Arena search systems (style contr… 1229.0 ▁·▁▁▂▂▂█▆▃●▂ beats 32/36 avg 1069 0.8% blunt
other tests (13)
test result vs the others in its run difficulty counts why
EBR-bench (Epoch) 39.5% ▄▂·▆█▂█●▄▆·▆ beats 17/25 very hard · avg 0.3% 5.8% sharp · at its level
Mystery Game Puzzles (Epoch) 52.0% █··▂▃▇▄█▂●▂▁ beats 67/77 very hard · avg 9.4% 5.0% sharp · at its level · noisy
Furniture Assembly (Epoch) 35.8% █▆▆●▆▂▆▂▂▂▂▅ beats 13/32 very hard · avg 1.2% 4.2% sharp · at its level
Humanity's Last Exam (maker) 57.8% ▂·····▂·▄▂●▄ beats 8/10 very hard · avg 5.9% 2.7% at its level
Chess Puzzles (Epoch) 41.0% █▁▁▁▁▂▃▃▃●▁▁ beats 122/137 very hard · avg 5.8% 2.5% sharp · at its level · noisy
Global-MMLU (maker) 93.6% ▄▄····▄▄··●█ beats 4/6 medium · avg 64% 1.0% typical
SimpleQA Verified 70.7% ▁▁▃▂▃▃▆▆█▄▂● beats 76/81 very hard · avg 8.0% 0.9% at its level
GPQA Diamond 85.9% ▁▁▁▁▂▄▆▄▅●█▅ beats 148/211 hard · avg 39% 0.8% sharp · maxed out
MirrorCode (Epoch) 63.9% ▄·█▄▄▄██·▄●█ beats 11/13 very hard · avg 1.6% 0.7% sharp · at its level · noisy
AutomationBench public set (Zapie… 46.2% ▄······▄●··▄ beats 2/4 very hard · avg 11% 0.7% at its level
HealthBench (maker) 61.2% █·●·····▄▄▄▄ beats 2/6 hard · avg 30% 0.6% blunt · at its level
RobloxQA v2.0 multiple choice (To… 97.0% ▁▁▃▁█▄▃▃▆●▂▂ beats 41/49 easy · avg 77% 0.4% maxed out
NTCIR SciClaimEval Subtask 1 pair… 74.4% ▃▃···▃·▃▃█·● beats 8/8 medium · avg 45% 0.3% blunt · at its level
source results share of the score
Epoch AI 11 35% ▇▇▇▇
Anthropic system cards and model announ… 17 25% ▇▇▇
LMArena leaderboard dataset 6 15% ▇▇
MathArena ArXivMath / BrokenArXiv / ArX… 7 15% ▇▇
Terminal-Bench 4.0 official leaderboard… 1 3% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 1 2% ▇
Roblox LLM Leaderboard 2 2% ▇
E2E-SWE: Benchmarking LLMs on Building … 1 1% ▇
Meridian Labs inspect_charxiv evaluatio… 2 1% ▇
Zapier AutomationBench README: public-s… 1 1% ▇
SciTrue: Reliable Scientific Claim Vali… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 79.4
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Meridian Labs inspect\_charxiv evaluation report ([MIT](https://github.com/meridianlabs-ai/inspect_charxiv/blob/317e4ff4335d7d4242d8e3c803898b7345973bd2/LICENSE)) · Zapier AutomationBench README: public-set pass rates ([MIT](../../licenses/zapier-automationbench-readme-MIT.txt)) · SciTrue: Reliable Scientific Claim Validation with Frontier and Open Language Models at the NTCIR SciClaimEval Task ([CC BY 4.0](../../licenses/paper-2609.00654v1-CC-BY-4.0.txt))
## How to run it
### Through an API
api (Anthropic) · $20.00/M blended · $10 in / $50 out per 1M tokens · [docs.anthropic.com](https://docs.anthropic.com/en/docs/about-claude/models)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/anthropic/models/claude-fable-5.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/anthropic/claude-fable-5.toml)
[interactive version](https://whatmodel.sh/model/claude-fable-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)