Context Window

Compare

Llama V3p2 90b Vision vs Qwen Turbo Latest

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 V3p2 90b Vision

Image input

Context window

16K

16,384 tokens · ~12K words

Model page
Alibaba

Model

Qwen Turbo Latest

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 V3p2 90b Vision16K
Qwen Turbo Latest1M

Qwen Turbo Latest has about 61× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen Turbo Latest has 6003% more context capacity (1000K vs 16K tokens). Qwen Turbo Latest is 94% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen Turbo Latest. Its 1000K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Qwen Turbo Latest. Input tokens are 94% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecLlama V3p2 90b VisionQwen Turbo Latest
Context window16,384 tokens (16K)1,000,000 tokens (1000K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesNo
Function callingNoYes
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 V3p2 90b Vision inLlama V3p2 90b Vision outQwen Turbo Latest inQwen Turbo Latest out
Alibaba Cloud——$0.050/M$0.200/M
Fireworks$0.900/M$0.900/M——

Frequently asked questions

Qwen Turbo Latest has a larger context window: 1000K tokens vs 16K. 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