Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 E2B IT vs Gemma 4 E4B IT
Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories. 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 E2B ITGoogle DeepMind · Open-weight model
C60.4/100#54 of 100 open-weight models
Gemma 4 E4B ITGoogle DeepMind · Open-weight model
B63.2/100#24 of 100 open-weight models
Which one, for what
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
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety evaluation figures published
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
- Capability, 27 against 19
Watch for
No safety evaluation figures published
Score by category
| Category | Weight this run | Gemma 4 E2B IT | Gemma 4 E4B IT | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 19 | 27 | Gemma 4 E4B IT +8 |
| Reach | 25%25 | 95 | 95 | even |
| Risk | 20%20 | 70 | 70 | even |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 75 | 75 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.4/100 | B 63.2/100 |
Facts side by side
| Fact | Gemma 4 E2B IT | Gemma 4 E4B 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 | yes |
| 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 | 3.5/5 (4) | 3.5/5 (4) |
Verdicts
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.
Gemma 4 E4B 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 7 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 E2B IT
- 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--mmprojwith gemma-4-E2B-it-mmproj.gguf for images and audio - With Ollama run
ollama run gemma4:e2band 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
Gemma 4 E4B IT
- On the reference machine run
llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072, and add--mmprojwith gemma-4-E4B-it-mmproj.gguf for images and audio - With Ollama run
ollama run gemma4:e4band 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
Questions
Which is better for AI agents, Gemma 4 E2B IT or Gemma 4 E4B IT?
Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Gemma 4 E2B IT's 60.4 (C), and leads in 1 of 5 scored categories.
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Machine-readable
- This page as Markdown
/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-e2b-it.json·/api/v1/tools/gemma-4-e4b-it.json - From a terminal
anchor compare gemma-4-e2b-it gemma-4-e4b-it(the CLI) - Over MCP
compare_tools {"a": "gemma-4-e2b-it", "b": "gemma-4-e4b-it"}at/mcp, no key