Context Window

Compare

DeepSeek V3.2 vs Qwen3 235b A22b Thinking 2507 Fp8

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.

Deepseek

Model

DeepSeek V3.2

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Alibaba

Model

Qwen3 235b A22b Thinking 2507 Fp8

Context window

262K

262,141 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.

DeepSeek V3.2164K
Qwen3 235b A22b Thinking 2507 Fp8262K

Qwen3 235b A22b Thinking 2507 Fp8 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 235b A22b Thinking 2507 Fp8 has 59% more context capacity (262K vs 163K tokens). DeepSeek V3.2 is 19% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 235b A22b Thinking 2507 Fp8. Its 262K context fits entire documents without chunking (vs 163K).

  • RAG / high-volume retrieval

    Use DeepSeek V3.2. Input tokens are 19% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepSeek V3.2Qwen3 235b A22b Thinking 2507 Fp8
Context window163,840 tokens (163K)262,141 tokens (262K)
Max output tokens163,840 tokens (163K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateDec 2025N/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.

ProviderDeepSeek V3.2 inDeepSeek V3.2 outQwen3 235b A22b Thinking 2507 Fp8 inQwen3 235b A22b Thinking 2507 Fp8 out
Aws Bedrock$0.740/M$2.22/M——
Azure$0.580/M$1.68/M——
Deepinfra$0.260/M$0.380/M——
Deepseek$0.280/M$0.400/M——
Friendliai$0.500/M$1.50/M——
Gmi$0.280/M$0.400/M$0.350/M$4.20/M
Novita$0.269/M$0.400/M——
Openrouter$0.259/M$0.420/M——
Sambanova$3.00/M$4.50/M——

Frequently asked questions

Qwen3 235b A22b Thinking 2507 Fp8 has a larger context window: 262K tokens vs 163K. 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