Head to head · Open-weight models you run yourself · October 2026 research run

Gemma 4 26B A4B IT vs Gemma 4 E2B IT

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons

Which one, for what

Gemma 4 26B A4B IT BB

Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context

Ahead on

  • Capability, 59 against 19

Also in its favour

  • Agent-ready, a grade of BB or better

Watch for

No safety evaluation figures published

Gemma 4 E2B IT C

Best for Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning

Ahead on

  • Reach, 95 against 90
  • Documentation and upkeep, 75 against 65

Watch for

No safety evaluation figures published

Score by category

CategoryWeight this runGemma 4 26B A4B ITGemma 4 E2B ITEdge
Capability35%355919Gemma 4 26B A4B IT +40
Reach25%259095Gemma 4 E2B IT +5
Risk20%207070even
Agent interface10%108585even
Documentation and upkeep10%106575Gemma 4 E2B IT +10
Negative events≤1500
TotalBB 72.2/100C 60.4/100

Facts side by side

FactGemma 4 26B A4B ITGemma 4 E2B IT
KindOpen-weight modelOpen-weight model
VendorGoogle DeepMindGoogle DeepMind
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnoyes
Last release2026-07-202026-07-20
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews3.8/5 (4)3.5/5 (4)

Verdicts

Gemma 4 26B A4B IT

A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.

Gemma 4 E2B IT

A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.

Before you call either

Gemma 4 26B A4B IT

  1. On the reference machine run llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images
  2. With Ollama run ollama run gemma4:26b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K
  3. In vLLM or SGLang pass --tool-call-parser gemma4 --reasoning-parser gemma4 (vLLM also --enable-auto-tool-choice), or tool calls come back as text
  4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
  5. Validate tool names and arguments before running them, and check any JSON yourself

Gemma 4 E2B IT

  1. On the reference machine run llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072, and add --mmproj with gemma-4-E2B-it-mmproj.gguf for images and audio
  2. With Ollama run ollama run gemma4:e2b and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
  3. In vLLM or SGLang pass --tool-call-parser gemma4 --reasoning-parser gemma4 (vLLM also --enable-auto-tool-choice), or tool calls come back as text; Google's w4a16 build is the 4-bit route there
  4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
  5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them

Questions

Which is better for AI agents, Gemma 4 26B A4B IT or Gemma 4 E2B IT?

Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. Gemma 4 E2B IT leads on reach and documentation and upkeep.

Other comparisons with Gemma 4 26B A4B IT or Gemma 4 E2B IT

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