1M Token Long Context Window

Anthropic’s Claude model family supports expanding the context window from the default 200K tokens to 1,000,000 tokens (1M)—5× the default capacity. With ZenMux, you can enable this capability effortlessly to handle large-scale document analysis, code review, long-running conversations, and more.

💡 Key Benefits

• Massive context: Process roughly 750,000 English words or thousands of pages in a single request
• Deep analysis: Ideal for large codebase reviews, long-form literature analysis, full conversation history, and more
• Seamless integration: Enable it via ZenMux’s unified interface by adding a single request header

Supported Models

The following Claude models currently support the 1M-token context window:

Model	Default Context Window	Extended Context Window
Claude Opus 4.6	200K tokens	1M tokens
Claude Sonnet 4.5	200K tokens	1M tokens
Claude Sonnet 4	200K tokens	1M tokens

Note

The 1M-token context window is currently a Beta feature, and functionality and pricing may change in future versions.

How to Enable

To use the 1M-token context window, add the request header anthropic-beta: context-1m-2025-08-07. If you don’t include this header, the model will use the default 200K-token context window.

Method 1: Use the OpenAI-Compatible API (Recommended)

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://zenmux.ai/api/v1",
    api_key="<your ZENMUX_API_KEY>", 
)

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4.5",
    messages=[
        {
            "role": "user",
            "content": "Please analyze the core content of the following long document..."
        }
    ],
    extra_headers={
        "anthropic-beta": "context-1m-2025-08-07"
    }
)

print(response.choices[0].message.content)

TypeScript

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://zenmux.ai/api/v1",
  apiKey: "<your ZENMUX_API_KEY>", 
});

async function main() {
  const response = await openai.chat.completions.create(
    {
      model: "anthropic/claude-sonnet-4.5",
      messages: [
        {
          role: "user",
          content: "Please analyze the core content of the following long document...",
        },
      ],
    },
    {
      headers: {
        "anthropic-beta": "context-1m-2025-08-07", 
      },
    },
  );

  console.log(response.choices[0].message.content);
}

main();

Shell (cURL)

curl "https://zenmux.ai/api/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ZENMUX_API_KEY" \
  -H "anthropic-beta: context-1m-2025-08-07" \
  -d '{
    "model": "anthropic/claude-sonnet-4.5",
    "messages": [
      {
        "role": "user",
        "content": "Please analyze the core content of the following long document..."
      }
    ]
  }'

---

Method 2: Use the Native Anthropic API

Python

import anthropic

client = anthropic.Anthropic(
    base_url="https://zenmux.ai/api/anthropic",
    api_key="<your ZENMUX_API_KEY>", 
)

response = client.beta.messages.create(
    model="anthropic/claude-sonnet-4.5",
    betas=["context-1m-2025-08-07"], 
    max_tokens=4096,
    messages=[
        {
            "role": "user",
            "content": "Please analyze the core content of the following long document..."
        }
    ]
)

print(response.content[0].text)

TypeScript

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://zenmux.ai/api/anthropic",
  apiKey: "<your ZENMUX_API_KEY>", 
});

async function main() {
  const response = await client.beta.messages.create({
    model: "anthropic/claude-sonnet-4.5",
    betas: ["context-1m-2025-08-07"], 
    max_tokens: 4096,
    messages: [
      {
        role: "user",
        content: "Please analyze the core content of the following long document...",
      },
    ],
  });

  console.log(response.content[0].text);
}

main();

Shell (cURL)

curl "https://zenmux.ai/api/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $ZENMUX_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "anthropic-beta: context-1m-2025-08-07" \
  -d '{
    "model": "anthropic/claude-sonnet-4.5",
    "max_tokens": 4096,
    "messages": [
      {
        "role": "user",
        "content": "Please analyze the core content of the following long document..."
      }
    ]
  }'

Long-Context Pricing

When the number of tokens in a request exceeds 200K, long-context pricing will apply automatically. The multipliers are as follows:

Pricing Item	Up to 200K	Over 200K
Input tokens	1x (standard rate)	2x (double rate)
Output tokens	1x (standard rate)	1.5x (1.5× standard rate)

💰 Billing Notes

• Only the portion that actually exceeds 200K is billed at the higher multiplier; the portion within 200K is still billed at the standard rate
• The output-token multiplier (1.5x) is lower than the input-token multiplier (2x), keeping the overall cost increase manageable
• We recommend using this together with Prompt Caching to significantly reduce costs in long-context scenarios

Best Practices

Recommended Use Cases

💡 Scenarios where 1M context is recommended

• Large-scale code review: Load an entire repository at once for analysis and refactoring suggestions
• Long-document analysis: Process full legal contracts, academic papers, technical specifications, and more
• Multi-document comparison: Analyze multiple related documents simultaneously for cross-referencing and comparison
• Full conversation history: Preserve complete context in very long conversations to avoid losing information
• Data analysis: Process large volumes of structured or unstructured data

Optimization Tips

💡 Improve long-context request quality

1. Put critical information first: Place the most important content at the beginning of the prompt—models pay the most attention to the start
2. Combine with prompt caching: For long documents reused repeatedly, cache static content via cache_control to save up to 90% of input costs
3. Control context size wisely: You don’t need to use the full 1M window every time—load only what’s relevant to the current task
4. Use structured markup: Organize long documents with clear XML tags or Markdown headings to help the model locate key information

FAQ

What happens if I don’t add the Beta header?

If you don’t add the anthropic-beta: context-1m-2025-08-07 header, the model uses the default 200K-token context window. Any content beyond 200K cannot be processed.

Is the 1M context window stable and production-ready?

This feature is currently in Beta. While it can already be used in production, Anthropic may adjust feature details and pricing in the future.

Can I use it together with other features?

Yes. The 1M context window can be used together with features such as Prompt Caching, Tool Calling, and Streaming. Prompt caching is especially recommended to optimize costs for long-context workloads.