Context Window

Compare

Gpt 5 2 Chat Latest vs Qwen3 1p7b

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 Chat Latest

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 1p7b

Context window

131K

131,072 tokens · ~98K 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 Chat Latest128K
Qwen3 1p7b131K

Qwen3 1p7b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 1p7b has 2% more context capacity (131K vs 128K tokens). Qwen3 1p7b is 94% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 1p7b. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Qwen3 1p7b. Input tokens are 94% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen3 1p7b. Its 131K 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.

SpecGpt 5 2 Chat LatestQwen3 1p7b
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokens16,384 tokens (16K)131,072 tokens (131K)
Speed tierBalancedFast
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 Chat Latest inGpt 5 2 Chat Latest outQwen3 1p7b inQwen3 1p7b out
Fireworks——$0.100/M$0.100/M
Openai$1.75/M$14.00/M——

Frequently asked questions

Qwen3 1p7b has a larger context window: 131K 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