Context Window

Compare

Gpt 5 2 2025 12 11 vs Qwen2 1 5b

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.

Openai

Model

Gpt 5 2 2025 12 11

Image inputTool calling

Context window

272K

272,000 tokens · ~204K words

Model page
Alibaba

Model

Qwen2 1 5b

Context window

33K

32,768 tokens · ~25K 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.

Gpt 5 2 2025 12 11272K
Qwen2 1 5b33K

Gpt 5 2 2025 12 11 has about 8.3× the context window of the other in this pair.

Token calculator →Memory savings →

Gpt 5 2 2025 12 11 has 730% more context capacity (272K vs 32K tokens). Qwen2 1 5b is 98% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gpt 5 2 2025 12 11. Its 272K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Qwen2 1 5b. Input tokens are 98% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt 5 2 2025 12 11Qwen2 1 5b
Context window272,000 tokens (272K)32,768 tokens (32K)
Max output tokens128,000 tokens (128K)N/A
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderGpt 5 2 2025 12 11 inGpt 5 2 2025 12 11 outQwen2 1 5b inQwen2 1 5b out
Azure$1.75/M$14.00/M——
Openai$1.75/M$14.00/M——
Together Ai——$0.020/M$0.020/M

Frequently asked questions

Gpt 5 2 2025 12 11 has a larger context window: 272K 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.
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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