Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 12B IT vs Gemma 4 26B A4B IT
Gemma 4 26B A4B IT and Gemma 4 12B IT score within a point of each other on agent readiness, 72.2 (BB) and 71.3 (BB). Gemma 4 12B 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
Gemma 4 12B ITGoogle DeepMind · Open-weight model
BB71.3/100#5 of 100 open-weight models
Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 of 100 open-weight models
Which one, for what
Best for Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context
Ahead on
- Reach, 95 against 90
- Documentation and upkeep, 75 against 65
Watch for
No safety evaluation figures published
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 50
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | Gemma 4 12B IT | Gemma 4 26B A4B IT | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 50 | 59 | Gemma 4 26B A4B IT +9 |
| Reach | 25%25 | 95 | 90 | Gemma 4 12B IT +5 |
| Risk | 20%20 | 70 | 70 | even |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 75 | 65 | Gemma 4 12B IT +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 71.3/100 | BB 72.2/100 |
Facts side by side
| Fact | Gemma 4 12B IT | Gemma 4 26B A4B IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Google DeepMind |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-07-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no 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 reviews | 3.8/5 (4) | 3.8/5 (4) |
Verdicts
Gemma 4 12B IT
A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.
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.
Before you call either
Gemma 4 12B IT
- On the reference machine run
llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072, and add--mmprojwith mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio - With Ollama run
ollama run gemma4:12band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K - 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 - 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
- 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
- Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama
Gemma 4 26B A4B IT
- 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 - With Ollama run
ollama run gemma4:26band 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 - 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 - 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
- Validate tool names and arguments before running them, and check any JSON yourself
Questions
Which is better for AI agents, Gemma 4 12B IT or Gemma 4 26B A4B IT?
Gemma 4 26B A4B IT and Gemma 4 12B IT score within a point of each other on agent readiness, 72.2 (BB) and 71.3 (BB). Gemma 4 12B IT leads on reach and documentation and upkeep.
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- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-Flash-Next
- Gemma 4 26B A4B IT vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/gemma-4-12b-it-vs-gemma-4-26b-a4b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-12b-it.json·/api/v1/tools/gemma-4-26b-a4b-it.json - From a terminal
anchor compare gemma-4-12b-it gemma-4-26b-a4b-it(the CLI) - Over MCP
compare_tools {"a": "gemma-4-12b-it", "b": "gemma-4-26b-a4b-it"}at/mcp, no key