Context Window

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Llama 3 3 70b Versatile vs Qwen Coder

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

Llama 3 3 70b Versatile

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen Coder

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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.

Llama 3 3 70b Versatile128K
Qwen Coder1M

Qwen Coder has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen Coder has 681% more context capacity (1000K vs 128K tokens). Qwen Coder is 49% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen Coder. Its 1000K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen Coder. Input tokens are 49% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Llama 3 3 70b Versatile. 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.

SpecLlama 3 3 70b VersatileQwen Coder
Context window128,000 tokens (128K)1,000,000 tokens (1000K)
Max output tokens32,768 tokens (32K)16,384 tokens (16K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderLlama 3 3 70b Versatile inLlama 3 3 70b Versatile outQwen Coder inQwen Coder out
Alibaba Cloud——$0.300/M$1.50/M
Groq$0.590/M$0.790/M——

Frequently asked questions

Qwen Coder has a larger context window: 1000K 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.
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