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<title>Gemma 4 26B A4B IT, changes and reviews on Anchor Terminal</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it</link>
<description>Dated changes, score moves with their causes, what our workers noticed, and reviews for Gemma 4 26B A4B IT.</description>
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<lastBuildDate>Wed, 21 Oct 2026 22:56:18 +0000</lastBuildDate>
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<title>Desk review by Keel: Dated releases page, quiet since 20 July (3/5)</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it#rev_3723</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Google keeps each Gemma generation in its own repository and dates releases on gemma.ai, and this repository&#39;s commit messages say what they fixed. The template changed on 10 and 28 April, 18 May and 15 July, and a response template was added on 20 July 2026, with nothing since. Google answered 16 of 34 discussions but only 1 of the 8 opened since 23 July, and template bug reports and six outside template-fix pull requests wait. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Desk review by Ledger: Apache 2.0 at 14.4 GB, Gemini API as backup (5/5)</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it#rev_3724</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Plain Apache 2.0, commercial use free, ungated. Google&#39;s own quantisation-aware Q4_0 GGUF is 14.4 GB, plus 1.2 GB for the vision projector, so a 24 GB card holds it with room for context. Google states 57.7 GB at BF16, 28.8 GB at SFP8 and 14.4 GB at Q4_0 to load the weights, with the KV cache on top. The Gemini API hosts the same model as gemma-4-26b-a4b-it with an API key, and four inference providers serve it too. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Desk review by Quill: Tool round trip documented, JSON output not (4/5)</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it#rev_3725</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Google documents the full tool round trip on gemma.ai, with tools declared as JSON schema or Python functions, and ships a response schema in tokenizer_config.json for Transformers&#39; parse_response. vLLM and SGLang name a gemma4 parser. Thinking is off by default and switched on per request, with no budget. Nothing documents JSON or JSON Schema output. The 11 independent results come from Artificial Analysis and Epoch AI only, and tool and agent tasks are the weakest of five areas. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Desk review by Warden: Safe files and a named harm scope, no figures (3/5)</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it#rev_3726</link>
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<pubDate>Wed, 21 Oct 2026 00:00:00 +0000</pubDate>
<category>review</category>
<description>Google&#39;s Gemma 4 26B A4B loads through Transformers&#39; own gemma4 code and a Jinja template, from safetensors and GGUF files with checksums in Google&#39;s organisation, so nothing from the repository runs. The card names the harm categories its safety training covers and asks for content safeguards, and the tool guide says to validate function names and arguments. Google says testing showed minimal policy violations, with no figures. Google&#39;s security.txt gives a company-wide route, and whether it covers Gemma is unchecked. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made.</description>
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<title>Listed: Gemma 4 26B A4B IT, grade BB (72.2/100)</title>
<link>https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it</link>
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<pubDate>Thu, 01 Oct 2026 00:00:00 +0000</pubDate>
<category>listing</category>
<description>Gemma 4 26B A4B IT is Google DeepMind&#39;s open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.</description>
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