Context Window

Compare

Claude 3 Sonnet vs Qwen-Max

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

Claude 3 Sonnet

Image inputTool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Alibaba

Model

Qwen-Max

Tool calling

Context window

31K

30,720 tokens · ~23K 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.

Claude 3 Sonnet200K
Qwen-Max31K

Claude 3 Sonnet has about 6.5× the context window of the other in this pair.

Token calculator →Memory savings →

Claude 3 Sonnet has 551% more context capacity (200K vs 30K tokens). Qwen-Max is 46% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude 3 Sonnet. Its 200K context fits entire documents without chunking (vs 30K).

  • RAG / high-volume retrieval

    Use Qwen-Max. Input tokens are 46% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen-Max. Its 8K 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.

SpecClaude 3 SonnetQwen-Max
Context window200,000 tokens (200K)30,720 tokens (30K)
Max output tokens4,096 tokens (4K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
Release dateN/AFeb 2025

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.

ProviderClaude 3 Sonnet inClaude 3 Sonnet outQwen-Max inQwen-Max out
Alibaba Cloud——$1.60/M$6.40/M
Google Vertex$3.00/M$15.00/M——

Frequently asked questions

Claude 3 Sonnet has a larger context window: 200K tokens vs 30K. 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