Context Window

Compare

Gpt 5 Search Api vs Qwen3.7 Plus

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 Search Api

Image inputTool calling

Context window

272K

272,000 tokens · ~204K words

Model page
Alibaba

Model

Qwen3.7 Plus

Image inputTool 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.

Gpt 5 Search Api272K
Qwen3.7 Plus1M

Qwen3.7 Plus has about 3.7× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3.7 Plus has 267% more context capacity (1000K vs 272K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3.7 Plus. Its 1000K context fits entire documents without chunking (vs 272K).

  • Long output (reports, code files)

    Use Gpt 5 Search Api. Its 128K 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 Search ApiQwen3.7 Plus
Context window272,000 tokens (272K)1,000,000 tokens (1000K)
Max output tokens128,000 tokens (128K)65,536 tokens (65K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AJun 2026

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 Search Api inGpt 5 Search Api outQwen3.7 Plus inQwen3.7 Plus out
Openai$1.25/M$10.00/M——

Frequently asked questions

Qwen3.7 Plus has a larger context window: 1000K tokens vs 272K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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