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Nemotron 3 Super vs Qwen3 235B A22B Thinking 2507

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.

Nvidia

Model

Nemotron 3 Super

Tool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Alibaba

Model

Qwen3 235B A22B Thinking 2507

Tool 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.

Nemotron 3 Super262K
Qwen3 235B A22B Thinking 2507262K

Same context window size for both models.

Nemotron 3 Super and Qwen3 235B A22B Thinking 2507 have identical context windows (262K tokens).

Full specs

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

SpecNemotron 3 SuperQwen3 235B A22B Thinking 2507
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateMar 2026Jul 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.

ProviderNemotron 3 Super inNemotron 3 Super outQwen3 235B A22B Thinking 2507 inQwen3 235B A22B Thinking 2507 out
Deepinfra$0.300/M$2.90/M
Fireworks$0.220/M$0.880/M
Novita$0.300/M$3.00/M
Openrouter$0.110/M$0.600/M
Together Ai$0.650/M$3.00/M

Frequently asked questions

Qwen3 235B A22B Thinking 2507 has a larger context window: 262K tokens vs 262K. 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.

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