Context Window

Compare

Code Qwen 1p5 7b vs Mistral Large 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.

Alibaba

Model

Code Qwen 1p5 7b

Context window

66K

65,536 tokens · ~49K words

Model page
Mistral

Model

Mistral Large Latest

Tool calling

Context window

32K

32,000 tokens · ~24K 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.

Code Qwen 1p5 7b66K
Mistral Large Latest32K

Code Qwen 1p5 7b has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Code Qwen 1p5 7b has 104% more context capacity (65K vs 32K tokens). Code Qwen 1p5 7b is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Code Qwen 1p5 7b. Its 65K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Code Qwen 1p5 7b. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecCode Qwen 1p5 7bMistral Large Latest
Context window65,536 tokens (65K)32,000 tokens (32K)
Max output tokens65,536 tokens (65K)N/A
Speed tierFastDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
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.

ProviderCode Qwen 1p5 7b inCode Qwen 1p5 7b outMistral Large Latest inMistral Large Latest out
Azure——$8.00/M$24.00/M
Fireworks$0.200/M$0.200/M——
Google Vertex——$2.00/M$6.00/M
Mistral——$0.500/M$1.50/M

Frequently asked questions

Code Qwen 1p5 7b has a larger context window: 65K tokens vs 32K. 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