Context Window

Compare

Llama3 8b Instruct Maas vs Qwen-Max

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

Llama3 8b Instruct Maas

Context window

32K

32,000 tokens · ~24K words

Model page
Alibaba

Model

Qwen-Max

Tool calling

Context window

31K

30,720 tokens · ~23K 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.

Llama3 8b Instruct Maas32K
Qwen-Max31K

Llama3 8b Instruct Maas has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama3 8b Instruct Maas has 4% more context capacity (32K vs 30K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama3 8b Instruct Maas. Its 32K context fits entire documents without chunking (vs 30K).

  • Long output (reports, code files)

    Use Llama3 8b Instruct Maas. Its 32K 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.

SpecLlama3 8b Instruct MaasQwen-Max
Context window32,000 tokens (32K)30,720 tokens (30K)
Max output tokens32,000 tokens (32K)8,192 tokens (8K)
Speed tierFastBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
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.

ProviderLlama3 8b Instruct Maas inLlama3 8b Instruct Maas outQwen-Max inQwen-Max out
Alibaba Cloud——$1.60/M$6.40/M
Google Vertex————

Frequently asked questions

Llama3 8b Instruct Maas has a larger context window: 32K tokens vs 30K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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