# Gemma 4 E4B IT vs Qwen3.6-35B-A3B

> Gemma 4 E4B IT and Qwen3.6-35B-A3B score within a point of each other for open-weight models you run yourself, 63.2 and 63.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 E4B IT and Qwen3.6-35B-A3B score within a point of each other on agent readiness, 63.2 (B) and 63.2 (B). Qwen3.6-35B-A3B leads on capability. Both do open-weight models you run yourself.

- Gemma 4 E4B IT: grade B, 63.2/100, rank #24 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json
- Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 E4B IT (B)

Good 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 85
- Risk, 70 against 55
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 36

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

### Qwen3.6-35B-A3B (B)

Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.

Ahead on:
- Capability, 58 against 27

Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw


## Score by category

| Category | Weight | Gemma 4 E4B IT | Qwen3.6-35B-A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 58 | Qwen3.6-35B-A3B +31 |
| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 E4B IT +10 |
| Risk | 20% (20 this run) | 70 | 55 | Gemma 4 E4B IT +15 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E4B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 75 | 36 | Gemma 4 E4B IT +39 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **63.2 · B** | |

## Facts side by side

| Fact | Gemma 4 E4B IT | Qwen3.6-35B-A3B |
| --- | --- | --- |
| 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 | 2026-04-16 |
| 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 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.

**Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

## Before you call either

### Gemma 4 E4B IT

1. 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 `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio
2. With Ollama run `ollama run gemma4:e4b` 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

### Qwen3.6-35B-A3B

1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

## Questions

### Which is better for AI agents, Gemma 4 E4B IT or Qwen3.6-35B-A3B?

Gemma 4 E4B IT and Qwen3.6-35B-A3B score within a point of each other on agent readiness, 63.2 (B) and 63.2 (B). Qwen3.6-35B-A3B leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e4b-it", "b": "qwen3-6-35b-a3b"}`. From a terminal: `anchor compare gemma-4-e4b-it qwen3-6-35b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json

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