AnthropicMultimodal Model

Claude Fable 5

Anthropic's frontier model, built for the hardest reasoning and long-horizon agentic work. A 1M-token context window and adaptive thinking let it sustain complex, multi-step tasks — deep research, large-scale refactors, autonomous coding sessions — that shorter-context models can't hold together.

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://api.inference.net/v1",
  apiKey: "<YOUR_API_KEY>",
});

const completion = await openai.chat.completions.create({
  model: "claude-fable-5",
  messages: [
    {
      role: "user",
      content: "What is the meaning of life?"
    }
  ],
  stream: true,
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta.content as string);
}

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://api.inference.net/v1",
  apiKey: "<YOUR_API_KEY>",
});

const completion = await openai.chat.completions.create({
  model: "claude-fable-5",
  messages: [
    {
      role: "user",
      content: "What is the meaning of life?"
    }
  ],
  stream: true,
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta.content as string);
}

Prompt caching

Keep the stable system content block byte-identical across calls.
Mark it with cache_control: { "type": "ephemeral" } to set a cache point.
Check usage.prompt_tokens_details.cached_tokens in the response to confirm cache reads. Initial writes may appear in cache-creation usage fields.

{
  "messages": [{
    "role": "system",
    "content": [{
      "type": "text",
      "text": "Stable system content...",
      "cache_control": { "type": "ephemeral" }
    }]
  }]
}

{
  "messages": [{
    "role": "system",
    "content": [{
      "type": "text",
      "text": "Stable system content...",
      "cache_control": { "type": "ephemeral" }
    }]
  }]
}

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