Context Window

Compare

Meta Llama3 2 90b Instruct vs Qwen3 30B A3B Instruct 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.

Meta

Model

Meta Llama3 2 90b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 30B A3B Instruct 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.

Meta Llama3 2 90b Instruct128K
Qwen3 30B A3B Instruct 2507262K

Qwen3 30B A3B Instruct 2507 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 30B A3B Instruct 2507 has 104% more context capacity (262K vs 128K tokens). Qwen3 30B A3B Instruct 2507 is 75% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 30B A3B Instruct 2507. Its 262K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 30B A3B Instruct 2507. Input tokens are 75% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen3 30B A3B Instruct 2507. Its 262K 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.

SpecMeta Llama3 2 90b InstructQwen3 30B A3B Instruct 2507
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens4,096 tokens (4K)262,144 tokens (262K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJul 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.

ProviderMeta Llama3 2 90b Instruct inMeta Llama3 2 90b Instruct outQwen3 30B A3B Instruct 2507 inQwen3 30B A3B Instruct 2507 out
Aws Bedrock$2.00/M$2.00/M——
Fireworks——$0.500/M$0.500/M

Frequently asked questions

Qwen3 30B A3B Instruct 2507 has a larger context window: 262K tokens vs 128K. 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.
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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.

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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