Anthropic
Anthropic's most capable widely released model for demanding reasoning and long-horizon agentic work. It is proprietary, generally available through the Claude API and supported cloud platforms, and cannot run locally as an open-weight model.
Anthropic's most capable widely released model for demanding reasoning and long-horizon agentic work.
OpenRouter
Price record: 2026-09-28. Source: openrouter.
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61.9
Quality Score
1289
Arena ELO
Undisclosed
Parameters
1M
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
18 of 22 public signals
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34.2K
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Jun 2026
Released
4/5 signals
4/4 signals
5/5 signals
2/4 signals
3/4 signals
Parameters
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Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Source-reported access: API access · Confident confidence
Gaps we are still tracking
Launches
4
Pricing
1
Benchmarks
1
Open Source
1
Research
2
General
13
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
Anthropic reports Claude Fable 5 production-safeguard results for coding, terminal, and computer-use evaluations in its official system card.
Salesforce in Claude is now available in beta. It brings your accounts, opportunities, and pipeline into Claude, with 37 pre-built sales skills. Prep a call, review a deal, create a pipeline dashboard, or send your forecast without leaving the conversation. https://t.co/juvI0sU3mX
View sourceA conversation with Boris Cherny and Cat Wu on the path from Claude Code to Claude Tag, and how it spread from engineering to the rest of Anthropic. Claude Fable 5 is now available in Claude Tag. https://t.co/8oNM5WaWzj
View sourceBeginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits. Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit. Demand for Fable has been challenging to
View sourceIn the Democratic Republic of the Congo, global health organizations including @CEPIvaccines, @WHOAFRO, and @inrb_kinshasa are using Claude to accelerate their response to an outbreak of an unusual Ebola variant. Read the full piece here: https://t.co/lQKx17Mxc3
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known
Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact. In our latest https://t.co/WxoKAKuud1

Salesforce in Claude is now available in beta. It brings your accounts, opportunities, and pipeline into Claude, with 37 pre-built sales skills. Prep a call, review a deal, create a pipeline dashboard, or send your forecast without leaving the conversation. https://t.co/juvI0sU3mX
Fable 5.1 Build Days start this week. The Claude community is hosting buildathons in cities all around the world from September 11–25. Bring a problem, an idea, or just show up and see what's possible. RSVP at https://t.co/AjMK4OHHBV https://t.co/0U8lcqsdrq
Beginning July 20, Claude Fable 5 will be included in all Max and Team Premium plans, at 50% of limits. Pro and Team Standard users will continue to have access to Fable via usage credits, and will receive a one-time $100 credit. Demand for Fable has been challenging to

A conversation with Boris Cherny and Cat Wu on the path from Claude Code to Claude Tag, and how it spread from engineering to the rest of Anthropic. Claude Fable 5 is now available in Claude Tag. https://t.co/8oNM5WaWzj
Claude Fable 5 will be available again globally tomorrow. After a series of productive conversations with the US government, we're redeploying the model with a new set of classifiers to target and block more cybersecurity tasks. In the near term, some routine tasks like coding
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on

Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal large language models (MLLMs) exercise active observation is an empirical question that current vision-language benchmarks do not answer. We introduce ActiveVision, a benchmark that makes active observation measurable for MLLMs, comprising 17 tasks across 3 categories. Tasks are designed to force repeated visual perception rather than a single static description. Frontier MLLMs collapse on ActiveVision: the highest-scoring model we evaluate, GPT-5.5 at the highest exposed reasoning-effort tier, solves only 10.6% of items and scores zero on 11 of the 17 tasks, and even Claude Fable 5, despite topping most reasoning and coding leaderboards, solves just 3.5%, far behind three human participants who average 96.1%. Furthermore, much of the gap persists even when models write and run their own vision code: such code is unreliable on realistic imagery, and catching its failures itself requires the active perception the models lack. Together, these results indicate that current MLLMs lack robust active visual observation, motivating architectures and training objectives that close the perception-reasoning loop.
Online group chats are social spaces with local conversational norms that are rarely stated explicitly. The ability and willingness of LLM-based agents to recognize and adapt to these norms remains mostly unexplored. We introduce LoSoNA, a benchmark for local social norm adaptation in multi-party chat. Each scenario gives a subject model a curated group-chat transcript in which non-subject participants demonstrate a hidden local norm, followed by a final elicitor turn that forces a response revealing whether the subject has inferred that norm. We evaluate eight frontier and open-weight models under four prompting conditions that vary how explicitly the model is told to treat the prior conversation as evidence for how it should answer. Naive prompting remains limited for most models; explicit norm-aware prompting helps unevenly, with Gemini 3.1 Pro reaching 84.2% and Claude Fable 5 reaching 81.6%, while several other models show small gains or regressions. LoSoNA contributes to recent calls for evaluating LLM social capabilities by testing whether models can infer local conversational norms from precedent and use them in a one-turn group-chat response.