Compare

Gemma 4 26b A4b It Maas vs Qwen3 8 Omni Flash

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.

Google

Model

Gemma 4 26b A4b It Maas

Image inputTool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Alibaba

Model

Qwen3 8 Omni Flash

Image inputTool calling

Context window

992K

991,808 tokens · ~744K 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.

Gemma 4 26b A4b It Maas256K
Qwen3 8 Omni Flash992K

Qwen3 8 Omni Flash has about 3.9× the context window of the other in this pair.

Qwen3 8 Omni Flash has 287% more context capacity (991K vs 256K tokens). Qwen3 8 Omni Flash is 0% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen3 8 Omni Flash. Its 991K context fits entire documents without chunking (vs 256K).

  • Long output (reports, code files)

    Use Qwen3 8 Omni Flash. 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.

SpecGemma 4 26b A4b It MaasQwen3 8 Omni Flash
Context window256,000 tokens (256K)991,808 tokens (991K)
Max output tokens128,000 tokens (128K)131,072 tokens (131K)
Speed tierBalancedFast
VisionYesYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesYes
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.

ProviderGemma 4 26b A4b It Maas inGemma 4 26b A4b It Maas outQwen3 8 Omni Flash inQwen3 8 Omni Flash out
Alibaba Cloud$0.150/M$0.470/M
Google Vertex$0.150/M$0.600/M
Openrouter$0.150/M$0.470/M

Frequently asked questions

Qwen3 8 Omni Flash has a larger context window: 991K tokens vs 256K. 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