# Gemma 4 E2B IT vs SmolLM3-3B

> SmolLM3-3B and Gemma 4 E2B IT score within a point of each other for open-weight models you run yourself, 61 and 60.4 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.md (~5,950 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.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

SmolLM3-3B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Gemma 4 E2B IT: grade C, 60.4/100, rank #54 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json
- SmolLM3-3B: grade C, 61/100, rank #47 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/smollm3-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/smollm3-3b.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 E2B IT (C)

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 65
- Agent interface, 85 against 80
- Documentation and upkeep, 75 against 54

Watch for: No safety evaluation figures published

### SmolLM3-3B (C)

Good for: Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device

Watch for: 65,536 tokens trained; 131,072 needs a YaRN edit to config.json


## Score by category

| Category | Weight | Gemma 4 E2B IT | SmolLM3-3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 85 | Gemma 4 E2B IT +10 |
| Risk | 20% (20 this run) | 70 | 65 | Gemma 4 E2B IT +5 |
| Agent interface | 10% (10 this run) | 85 | 80 | Gemma 4 E2B IT +5 |
| Documentation and upkeep | 10% (10 this run) | 75 | 54 | Gemma 4 E2B IT +21 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.4 · C** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 E2B IT | SmolLM3-3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Hugging Face |
| 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 | 2025-09-10 |
| 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) | 3.3/5 (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.

**SmolLM3-3B.** A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.

## Before you call either

### 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

### SmolLM3-3B

1. Start it with `vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes`, or tool calls come back as text
2. Pass `chat_template_kwargs` with `enable_thinking` false for short tasks, since thinking is on by default; a `/think` or `/no_think` flag in the system prompt overrides the kwarg
3. Put earlier tool calls in the assistant message content, since the template ignores a `tool_calls` field when it renders history
4. Add the YaRN `rope_scaling` block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
7. Validate JSON output yourself or use the runtime's guided decoding

## Questions

### Which is better for AI agents, Gemma 4 E2B IT or SmolLM3-3B?

SmolLM3-3B and Gemma 4 E2B IT score within a point of each other on agent readiness, 61 (C) and 60.4 (C). Gemma 4 E2B IT leads on reach, risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e2b-it", "b": "smollm3-3b"}`. From a terminal: `anchor compare gemma-4-e2b-it smollm3-3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json and https://www.anchorterminal.com/api/v1/tools/smollm3-3b.json

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- [Laguna S 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-smollm3-3b.md)
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- [MiniMax-M2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.md)
- [MiniMax-M2.7 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-smollm3-3b.md)
- [Muse Glimmer 30B vs SmolLM3-3B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.md)
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- [Nemotron 3 Nano 4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-smollm3-3b.md)
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- [occamy-1.0 vs SmolLM3-3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.md)
- [Olmo 3 7B Think vs SmolLM3-3B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-smollm3-3b.md)
- [Ornith-1.5-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.md)
- [Ornith-1.5-397B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.md)
- [Ornith-1.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.md)
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- [Qwen3-4B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.md)
- [Qwen3.5-0.8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-smollm3-3b.md)
- [Qwen3.5-122B-A10B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-smollm3-3b.md)
- [Qwen3.5-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-smollm3-3b.md)
- [Qwen3.5-397B-A17B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.md)
- [Qwen3.5-4B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-smollm3-3b.md)
- [Qwen3.5-9B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-smollm3-3b.md)
- [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md)
- [Qwen3.6-35B-A3B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-smollm3-3b.md)
- [Qwen3.8-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.md)
- [Qwen3.8-Flash-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-smollm3-3b.md)
- [Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-Coder-Next vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-smollm3-3b.md)
- [Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-2B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-32B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-4B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-smollm3-3b.md)
- [Qwen3-VL-8B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-smollm3-3b.md)
- [Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.md)
- [SmolLM3-3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.md)
- [SmolLM3-3B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-27b.md)
- [SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.md)
- [SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.md)

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