Context Window

Compare

Openai Gpt 4o vs Qwen3 VL 30B A3B Thinking

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 4o

Context window

128K

128,000 tokens · ~96K words

Model page
Alibaba

Model

Qwen3 VL 30B A3B Thinking

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 4o128K
Qwen3 VL 30B A3B Thinking262K

Qwen3 VL 30B A3B Thinking has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen3 VL 30B A3B Thinking has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 VL 30B A3B Thinking. Its 262K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecOpenai Gpt 4oQwen3 VL 30B A3B Thinking
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierBalancedFast
VisionNoYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AOct 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 4o inOpenai Gpt 4o outQwen3 VL 30B A3B Thinking inQwen3 VL 30B A3B Thinking out
Fireworks——$0.150/M$0.600/M
Gradient————
Novita——$0.200/M$1.00/M

Frequently asked questions

Qwen3 VL 30B A3B Thinking has a larger context window: 262K 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.

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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