Context Window

Compare

Openai Gpt 5 vs Qwen3 30B A3B

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

Image inputTool calling

Context window

300K

300,000 tokens · ~225K words

Model page
Alibaba

Model

Qwen3 30B A3B

Tool calling

Context window

129K

129,024 tokens · ~97K 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 5300K
Qwen3 30B A3B129K

Openai Gpt 5 has about 2.3× the context window of the other in this pair.

Token calculator →Memory savings →

Openai Gpt 5 has 132% more context capacity (300K vs 129K tokens). Qwen3 30B A3B is 93% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Openai Gpt 5. Its 300K context fits entire documents without chunking (vs 129K).

  • RAG / high-volume retrieval

    Use Qwen3 30B A3B. Input tokens are 93% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecOpenai Gpt 5Qwen3 30B A3B
Context window300,000 tokens (300K)129,024 tokens (129K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateN/AApr 2025

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 inOpenai Gpt 5 outQwen3 30B A3B inQwen3 30B A3B out
Alibaba Cloud————
Deepinfra——$0.080/M$0.290/M
Fireworks——$0.150/M$0.600/M
Nebius——$0.100/M$0.300/M
Snowflake$1.25/M$10.00/M——

Frequently asked questions

Openai Gpt 5 has a larger context window: 300K tokens vs 129K. 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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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