Context Window

Compare

Claude Instant vs Qwen-Plus

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 Instant

Context window

100K

100,000 tokens · ~75K words

Model page
Alibaba

Model

Qwen-Plus

Tool calling

Context window

129K

129,024 tokens · ~97K 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 Instant100K
Qwen-Plus129K

Qwen-Plus has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen-Plus has 29% more context capacity (129K vs 100K tokens). Qwen-Plus is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen-Plus. Its 129K context fits entire documents without chunking (vs 100K).

  • RAG / high-volume retrieval

    Use Qwen-Plus. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen-Plus. Its 16K 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 InstantQwen-Plus
Context window100,000 tokens (100K)129,024 tokens (129K)
Max output tokens8,191 tokens (8K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
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 Instant inClaude Instant outQwen-Plus inQwen-Plus out
Alibaba Cloud——$0.400/M$1.20/M
Aws Bedrock$0.800/M$2.40/M——

Frequently asked questions

Qwen-Plus has a larger context window: 129K tokens vs 100K. 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