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Openai Gpt 5 Nano vs Qwen3.5-27B

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

Openai Gpt 5 Nano

Tool calling

Context window

5M

5,000,000 tokens · ~3.8M words

Model page
Alibaba

Model

Qwen3.5-27B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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.

Openai Gpt 5 Nano5M
Qwen3.5-27B262K

Openai Gpt 5 Nano has about 19.1× the context window of the other in this pair.

Openai Gpt 5 Nano has 1807% more context capacity (5000K vs 262K tokens). Openai Gpt 5 Nano is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Openai Gpt 5 Nano. Its 5000K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Openai Gpt 5 Nano. Input tokens are 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Qwen3.5-27B. Its 65K 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.

SpecOpenai Gpt 5 NanoQwen3.5-27B
Context window5,000,000 tokens (5000K)262,144 tokens (262K)
Max output tokens16,384 tokens (16K)65,536 tokens (65K)
Speed tierFastFast
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AFeb 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.

ProviderOpenai Gpt 5 Nano inOpenai Gpt 5 Nano outQwen3.5-27B inQwen3.5-27B out
Openrouter$0.300/M$2.40/M
Snowflake$0.150/M$0.600/M

Frequently asked questions

Openai Gpt 5 Nano has a larger context window: 5000K tokens vs 262K. 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.

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