Long Context 1m

1M Context Window

Claude Opus 4.6 and Sonnet 4.6 support 1M token context with the context-1m-2025-08-07 beta header. Use it well or burn money for nothing.

When to Use

• You have a codebase, book, log bundle, or document set that fits in 1M tokens
• You need cross-document reasoning that chunked RAG can't deliver
• You're deciding between 1M-context vs a RAG pipeline
• You want to cache a giant system prompt / knowledge base across requests

Enabling 1M Context

import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();

const response = await client.messages.create(
  {
    model: "claude-sonnet-4-6",
    max_tokens: 4096,
    messages: [{ role: "user", content: giantDocument + "\n\nSummarize." }],
  },
  { headers: { "anthropic-beta": "context-1m-2025-08-07" } },
);

Without the beta header, requests over 200K tokens will error.

Pricing Tiers

Long context is priced differently above 200K input tokens. Check your provider's current rates; as a rule of thumb input above 200K costs ~2× the base rate. Output price is unchanged.

Rule: if you're only going to use 200K, don't enable 1M. Only pay for long-context pricing when you actually need > 200K.

1M Context vs RAG

Hybrid: RAG retrieves the top 500K tokens; stuff those into 1M context. Best of both.

Structuring Long Inputs for Recall

Claude's long-context recall is strong but not uniform. Tips:

1. Put the instruction at the END — "Given the above, answer X" recalls better than instruction-then-context
2. Section headers with XML tags — <document index="1" title="...">...</document> — the model indexes on these
3. Repeat critical instructions — once at top, once at bottom
4. Avoid homogeneous blobs — chunk with delimiters; recall degrades in undifferentiated text

const prompt = `
You will analyze the codebase below, then answer questions.

<codebase>
<file path="src/auth.ts">...</file>
<file path="src/db.ts">...</file>
...
</codebase>

Given the codebase above, answer: <question>How does auth flow work?</question>
`;

Combine with Prompt Caching

1M context is expensive per call. If you're asking multiple questions against the same corpus, cache it:

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 4096,
  system: [
    { type: "text", text: "You are a code reviewer." },
    { type: "text", text: giantCodebase, cache_control: { type: "ephemeral", ttl: "1h" } },
  ],
  messages: [{ role: "user", content: "What's the auth flow?" }],
});

First call: full cost. Subsequent calls in the TTL window: ~10% of input cost for the cached portion. See the prompt-caching-ttl skill.

Latency

1M input takes longer to process — TTFT can be 10-30s for a full context. Mitigate:

• Stream the response so the user sees output early
• Extended thinking on top only if you need reasoning (adds latency)
• Cache so subsequent calls skip the bulk of input processing

Token Accounting

Use the token counter before sending:

const { input_tokens } = await client.messages.countTokens({
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: text }],
});
if (input_tokens > 1_000_000) throw new Error("Over context limit");

Budget with a 5% safety margin — actual tokenization varies slightly.

Anti-Patterns

1. Always-on 1M: enabling the beta even for small requests. Wastes money via long-context pricing
2. Dump-and-pray: no structure in the input. Model can't find what it needs
3. Instruction at top only: with 900K of content below, the instruction gets diluted
4. No caching for repeat queries: every question against the same document pays full price

Best Practices

1. Check the token count before enabling 1M beta
2. Cache the static portion (the document/codebase) with 1h TTL
3. Put actionable instructions at the end, not the start
4. Use XML tags to structure sections
5. Stream responses — users don't want to wait 30s staring at nothing
6. For queries on a large corpus with many questions, prefer cached-1M over RAG