Claude Fable 5.1
claude-fable-5-1Compare claude-fable-5-1 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Max output
- 128K
- Maximum tokens per response
- Modalities
- →
- Knowledge cutoff
- Jun 2026
- Released
- Sep 2026
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
Anthropic Claude tokenizerPerformance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Claude Fable 5.1
Intelligence Index
Claude Fable 5.1
53.4
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Claude Fable 5.1
为难题留出足够的分析空间
Claude Fable 5.1 适合需要长期保持目标、反复检查证据和持续完善成果的工作。典型任务包括跨模块代码改造、多步骤研究，以及把复杂资料整理成文档、表格或演示方案。对于已有方法仍无法达到要求的问题，它可以承担更深入的分析与实施过程。
工作不止于给出结论
模型可以接受文本和图片，围绕资料中的细节与关系进行推理。你可以让它先建立判断框架，再逐项核查证据，比较替代方案，最后形成适合目标读者的成果。配合应用提供的文件与执行工具，还可以把分析延伸到文档制作、数据处理和实际代码验证。
长任务需要明确阶段目标。例如研究项目可以依次完成材料整理、冲突分析、验证计划和汇报制作；每一阶段都保存依据与未解决问题，避免后续工作偏离最初约束。复杂任务不应只追求回答长度，更应要求结论能追溯、方案可实施、产物可检查。
保留连续工作的上下文
Fable 5.1 始终开启自适应思考，通过effort调整思考深度。应用接入时应保留对话和工具结果，尤其注意思考内容与原会话的关联。随意修改早期对话或切换旧模型可能破坏连续性。对于长周期工作，清楚记录阶段成果和待办，比频繁重启整段任务更有利于接续。
Use cases and prompting
研究与汇报示例
“基于以下访谈、产品数据和竞品材料，判断下季度应优先改善新用户引导还是团队协作功能。先定义比较标准，再核查支持与反对证据，列出会改变判断的假设。最后形成管理层汇报提纲，并为关键图表列出数据来源与计算方式。”
若要生成实际文件，需要应用提供相应文件处理工具。
怎样管理一个持续多轮的任务？
按阶段保存结论、依据、已完成成果和剩余问题。新的要求作为后续信息补充，并说明对原目标的影响。
为什么修改早期消息后无法继续复用思考内容？
该版本的思考内容与模型和会话相关联，修改早期轮次可能使其失效。接入时应保留完整会话，并按支持的方式续接。
API access
API documentation
Code samples
/v1/messages| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| CCMax-ZL | Unlimited | Unlimited | Unlimited |
| Vertex Claude | Unlimited | Unlimited | Unlimited |
| CCMax | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about claude-fable-5-1
What is claude-fable-5-1?
Compare claude-fable-5-1 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call claude-fable-5-1?
Create an API key with access to claude-fable-5-1, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is claude-fable-5-1 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of claude-fable-5-1?
The model catalog lists a context window of 1000000 tokens. Check the selected endpoint for request limits.
What is the maximum output of claude-fable-5-1?
The model catalog lists a maximum output of 128000 tokens. Your request settings may set a lower limit.
How should I evaluate claude-fable-5-1 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.