Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct
Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 5 scored categories. Qwen3-VL-4B-Instruct leads on capability. 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
Qwen3-VL-4B-InstructAlibaba (Qwen team) · Open-weight model
D52.6/100#84 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
Ahead on
- Risk, 70 against 55
- Agent interface, 85 against 60
- Documentation and upkeep, 75 against 31
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5
Watch for
No safety evaluation figures published
Best for An agent with 4 GB or more to spare that needs a local model to read images, documents and video
Ahead on
- Capability, 25 against 19
Watch for
No safety evaluation, agent-use guidance or security contact
Score by category
| Category | Weight this run | Gemma 4 E2B IT | Qwen3-VL-4B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 19 | 25 | Qwen3-VL-4B-Instruct +6 |
| Reach | 25%25 | 95 | 95 | even |
| Risk | 20%20 | 70 | 55 | Gemma 4 E2B IT +15 |
| Agent interface | 10%10 | 85 | 60 | Gemma 4 E2B IT +25 |
| Documentation and upkeep | 10%10 | 75 | 31 | Gemma 4 E2B IT +44 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 60.4/100 | D 52.6/100 |
Facts side by side
| Fact | Gemma 4 E2B IT | Qwen3-VL-4B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Alibaba (Qwen team) |
| 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 | 2025-11-01 |
| 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) | 2.8/5 (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.
Qwen3-VL-4B-Instruct
A small vision-language cousin for reading screenshots, documents, charts and video, running in about 3.5 GB from Alibaba's Q4_K_M GGUF in every major runtime under plain Apache 2.0. It has no thinking mode, and Qwen hasn't answered repository questions or published safety work, so add your own guardrails before letting it drive a GUI.
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
Qwen3-VL-4B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-4B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:4b-instruct - In Ollama the plain
qwen3-vl:4btag is the Thinking edition, so name4b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the card's sampling,
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5for vision andtemperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0for text - Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later and Transformers 4.57.0 or later
Questions
Which is better for AI agents, Gemma 4 E2B IT or Qwen3-VL-4B-Instruct?
Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 5 scored categories. Qwen3-VL-4B-Instruct leads on capability.
Other comparisons with Gemma 4 E2B IT or Qwen3-VL-4B-Instruct
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Machine-readable
- This page as Markdown
/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.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/qwen3-vl-4b-instruct.json - From a terminal
anchor compare gemma-4-e2b-it qwen3-vl-4b-instruct(the CLI) - Over MCP
compare_tools {"a": "gemma-4-e2b-it", "b": "qwen3-vl-4b-instruct"}at/mcp, no key