Claude Fable 5
claude-fable-5Compare claude-fable-5 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
- Jan 2026
- Released
- Jun 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
Intelligence Index
Claude Fable 5
49.6
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
为需要多轮推进的工作建立主线
Claude Fable 5 适合问题尚不完全清楚、步骤较多且需要持续验证的任务。它可以阅读文本与图片，整理研究材料、分析复杂问题、参与代码开发，并在工具支持下逐步完成更完整的交付。
这类工作的起点往往是一句宽泛需求，例如“研究这个方向是否可行”或“把这套流程做成应用”。可以先让模型把目标转换成问题清单，识别现有材料、关键假设和缺失信息，再按优先级推进。提供项目背景、交付形式和判断标准，有助于让每一步围绕同一目标展开。
知识工作中，它可以比较多份报告、解释图表、整理相互矛盾的证据，并形成分析稿或方案。编程工作中，则可以围绕完整功能拆解实现、调用工具检查结果、根据失败反馈修正方案。模型的价值在于持续组织和改进工作，具体文件生成、查询和执行需要应用配备相应工具。
Fable 5 使用始终开启的自适应思考，推理投入通过 effort 调整。复杂任务可以分阶段定义成果，让模型在每个阶段说明发现、完成状态和下一步依据。对尚未解决的争议，要求保留不同解释及其证据，而不是为了形成漂亮结论提前消除不确定性。
Use cases and prompting
研究方案提示词
“请基于这些访谈、产品资料和使用数据，研究是否值得开发团队协作功能。先建立需要回答的问题，再按问题整理支持与反对证据。提出两种可实施方案及各自前提，最后设计一个能验证关键假设的小规模试验。缺少证据的判断单独列出。”
常见问题
- 目标太模糊，应该怎样开始？先让模型列出交付物、关键问题和需要确认的约束，再决定推进顺序。
- 可以关闭思考吗？Fable 5 的自适应思考始终开启，可通过 effort 调整投入。
- 长任务怎样避免偏离？为每个阶段设置可检查的成果，保留目标、证据和未解决问题，再接续下一阶段。
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 |
|---|---|---|---|
| Anthropic | Unlimited | Unlimited | Unlimited |
| CCMax | Unlimited | Unlimited | Unlimited |
| CCMax-ZL | Unlimited | Unlimited | Unlimited |
| Claude kiro | Unlimited | Unlimited | Unlimited |
| claude特供 | Unlimited | Unlimited | Unlimited |
| Vertex Claude | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about claude-fable-5
What is claude-fable-5?
Compare claude-fable-5 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call claude-fable-5?
Create an API key with access to claude-fable-5, 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 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?
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?
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 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.