Context Window

Compare

Jp Anthropic Claude Sonnet 4 6 vs Qwen Coder

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Anthropic

Model

Jp Anthropic Claude Sonnet 4 6

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Alibaba

Model

Qwen Coder

Tool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Jp Anthropic Claude Sonnet 4 61M
Qwen Coder1M

Same context window size for both models.

Token calculator →Memory savings →

Jp Anthropic Claude Sonnet 4 6 and Qwen Coder have identical context windows (1000K tokens). Qwen Coder is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Qwen Coder. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jp Anthropic Claude Sonnet 4 6. Its 64K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecJp Anthropic Claude Sonnet 4 6Qwen Coder
Context window1,000,000 tokens (1000K)1,000,000 tokens (1000K)
Max output tokens64,000 tokens (64K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APIYesNo
Release dateN/AN/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderJp Anthropic Claude Sonnet 4 6 inJp Anthropic Claude Sonnet 4 6 outQwen Coder inQwen Coder out
Alibaba Cloud——$0.300/M$1.50/M
Aws Bedrock$3.30/M$16.50/M——

Frequently asked questions

Qwen Coder has a larger context window: 1000K tokens vs 1000K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Calculate your savingsLearn about Mem0

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model