Context Window

Compare

Claude 3 5 Sonnet vs Qwen3.5-35B-A3B

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 5 Sonnet

Context window

200K

200,000 tokens · ~150K words

Model page
Alibaba

Model

Qwen3.5-35B-A3B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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 5 Sonnet200K
Qwen3.5-35B-A3B262K

Qwen3.5-35B-A3B has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3.5-35B-A3B has 31% more context capacity (262K vs 200K tokens). Qwen3.5-35B-A3B is 91% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3.5-35B-A3B. Its 262K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Qwen3.5-35B-A3B. Input tokens are 91% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecClaude 3 5 SonnetQwen3.5-35B-A3B
Context window200,000 tokens (200K)262,144 tokens (262K)
Max output tokensN/A65,536 tokens (65K)
Speed tierBalancedFast
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APIYesNo
Release dateN/AFeb 2026

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 5 Sonnet inClaude 3 5 Sonnet outQwen3.5-35B-A3B inQwen3.5-35B-A3B out
Google Vertex$3.00/M$15.00/M——
Gradient$3.00/M$15.00/M——
Openrouter$3.00/M$15.00/M$0.250/M$2.00/M
Replicate$3.75/M$18.75/M——
Snowflake————

Frequently asked questions

Qwen3.5-35B-A3B has a larger context window: 262K tokens vs 200K. 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