Claude Fable 5.2 (batch) API
Anthropic's most capable generally available model, built for long-horizon agentic engineering, scientific research and large-scale batch processing with a 1 million token context.
Claude Fable 5.2 is Anthropic's flagship generally available model, released on November 9, 2026. It accepts text, image and PDF input and returns text, with a context window of 1 million tokens. Compared with Claude Fable 5.1, it improves in coding, reasoning and vision, and generates output up to 30% faster. Batch processing makes large-scale runs cost-efficient, which suits workloads that need many long, parallel jobs rather than quick interactive turns.
The model is aimed at ambitious, open-ended technical work. It takes on large, ambiguous problems across multiple repositories and large monorepos, shows strong architectural judgment, tests its own work while in progress, and questions inherited conclusions to find root causes that other models miss. It can act as a lead agent over multi-day ambiguous engineering work and coordinate hundreds to over a thousand subagents, using tools efficiently and maintaining its own integrity during long runs by building controls and flagging non-reproducible results.
Beyond software, Claude Fable 5.2 devises new analysis methods for difficult physics calculations, designs fully functional electronic devices, formalizes mathematical proofs, and runs autonomous research loops of measuring, trying and improving, including ablations, machine-learning metric hill climbing and verification of each change. It brings deep reasoning to quantitative finance, from multi-agent research to forecasting and analysis, and handles 3D worlds, hardware components, simulations, animations, narrated educational videos and robotics environments. It is best suited where judgment and accuracy matter most; Claude Opus 5.5 is recommended for everyday coding and knowledge work, and queries flagged in cybersecurity and biology are automatically routed to less capable models.
Updated:
MODEL ID
claude-fable-5.2:batch- Context
- 1M
- Max output
- 128K
- Recommended API
- messages
- Input
- text / image / file
- Output
- text
Highlights
- 1 million token context window
- Up to 30% faster output than Fable 5.1
- Coordinates over a thousand subagents
- Handles text, image and PDF input
- Batch processing for large-scale runs
- Top rank on Vals Index
Providers
Compare API prices across providers. Select a provider to view all charges and conditions.
| Uptime | ||||
|---|---|---|---|---|
| $4 | $20 | $0.1 |
Historical observations are not a live health check. Missing data does not mean an outage.
What $10 buys
- Input
- 2.5M tokens
- Output
- 500K tokens
Estimated from current input and output prices, before discounts.
Benchmarks
- 71.31%
Vals Index
Ranked 1st among 36 models
Scores as published by the model provider.
Reasoning
- Default effort
- high
- Always enabled
- Yes
- Available effort levels
- max / xhigh / high / medium / low
Quick Start
Drop-in code to call this model with Felo API Platform compatible APIs. Start in the Playground when you want to test prompts before writing code.
Get your API key
Create a key from your Felo API Platform account and set it as an environment variable.
export FELO_API_KEY="YOUR_API_KEY"Make your first request
Use this model ID with the Felo API Platform endpoint. Optional request headers such as X-Request-Id can help troubleshooting.
claude-fable-5.2:batchThis model is reasoning-capable. If your selected protocol and model route expose reasoning controls, preserve the returned reasoning metadata when continuing a conversation.
const response = await fetch("https://openapi.felo.ai/api/v1/messages", {
method: 'POST',
headers: {
Authorization: `Bearer ${process.env.FELO_API_KEY}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
"model": "claude-fable-5.2:batch",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}),
});
if (!response.ok) throw new Error(await response.text());
console.log(await response.json());Using third-party SDKs
Use compatible SDKs by setting baseURL to https://openapi.felo.ai/api/v1. Use the Anthropic-compatible base URL for Claude-style clients. https://openapi.felo.ai/api.
Enable streaming
Add stream: true to receive responses as server-sent events.
curl -N https://openapi.felo.ai/api/v1/messages \
-H "Authorization: Bearer $FELO_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "claude-fable-5.2:batch",
"max_tokens": 1024,
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"stream": true
}'Endpoint
Felo API Platform exposes compatible model-calling surfaces. Pick the surface your app or agent client already supports.
Messages
Anthropic-compatible messages for Claude-style clients.
POST
https://openapi.felo.ai/api/v1/messagesParameters
modelstringclaude-fable-5.2:batchPublic model ID to call.max_tokensinteger—Maximum generated tokens.messagesarray—Conversation messages for Chat Completions.stop———toolsarray—Tool calling definitions following compatible schema.reasoning———verbosity———tool_choicestring or object—Controls whether a tool should be called.response_formatobject—Request structured output when supported.reasoning_effort———include_reasoning———structured_outputs———Frequently asked questions
What is Claude Fable 5.2 (batch)?
Claude Fable 5.2 is Anthropic's flagship generally available model, released on November 9, 2026. It accepts text, image and PDF input and returns text, with a context window of 1 million tokens. Compared with Claude Fable 5.1, it improves in coding, reasoning and vision, and generates output up to 30% faster. Batch processing makes large-scale runs cost-efficient, which suits workloads that need many long, parallel jobs rather than quick interactive turns. The model is aimed at ambitious, open-ended technical work. It takes on large, ambiguous problems across multiple repositories and large monorepos, shows strong architectural judgment, tests its own work while in progress, and questions inherited conclusions to find root causes that other models miss. It can act as a lead agent over multi-day ambiguous engineering work and coordinate hundreds to over a thousand subagents, using tools efficiently and maintaining its own integrity during long runs by building controls and flagging non-reproducible results. Beyond software, Claude Fable 5.2 devises new analysis methods for difficult physics calculations, designs fully functional electronic devices, formalizes mathematical proofs, and runs autonomous research loops of measuring, trying and improving, including ablations, machine-learning metric hill climbing and verification of each change. It brings deep reasoning to quantitative finance, from multi-agent research to forecasting and analysis, and handles 3D worlds, hardware components, simulations, animations, narrated educational videos and robotics environments. It is best suited where judgment and accuracy matter most; Claude Opus 5.5 is recommended for everyday coding and knowledge work, and queries flagged in cybersecurity and biology are automatically routed to less capable models.
How is usage billed?
Your bill depends on the provider you use, its prices and your usage. Longer inputs or other conditions may have different prices.
What input types does Claude Fable 5.2 accept?
It accepts text, image and PDF input, and produces text output.
How large is the context window?
Claude Fable 5.2 supports a context of 1 million tokens.
What kinds of tasks is it best for?
It is designed for ambitious, open-ended technical work such as coding across large monorepos, scientific research, financial analysis, data analysis and machine learning, 3D and hardware design, and long-horizon agentic work.
How does it differ from Claude Fable 5.1?
It outperforms Claude Fable 5.1 in coding, reasoning and vision, and generates output up to 30% faster.
How does it differ from Claude Mythos 5.2?
Claude Mythos 5.2 shares the same base model but is limited to approved cybersecurity and life-science use cases for verified organizations.
Are there any routing restrictions?
Queries flagged in cybersecurity and biology are automatically routed to less capable models.
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