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Gemma 4 26B A4B vs GPT-5.6 Sol

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

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Openai

Model

GPT-5.6 Sol

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K 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 A4B262K
GPT-5.6 Sol1.1M

GPT-5.6 Sol has about 4× the context window of the other in this pair.

GPT-5.6 Sol has 300% more context capacity (1050K vs 262K tokens). Gemma 4 26B A4B is 98% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5.6 Sol. Its 1050K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Gemma 4 26B A4B. Input tokens are 98% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gemma 4 26B A4B. Its 262K 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 A4BGPT-5.6 Sol
Context window262,144 tokens (262K)1,050,000 tokens (1050K)
Max output tokens262,144 tokens (262K)128,000 tokens (128K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoYes
Batch APINoNo
Release dateApr 2026Jul 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.

ProviderGemma 4 26B A4B inGemma 4 26B A4B outGPT-5.6 Sol inGPT-5.6 Sol out
Cloudflare$0.100/M$0.300/M
Openai$5.00/M$30.00/M

Frequently asked questions

GPT-5.6 Sol has a larger context window: 1050K 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