# Gemma 4 E2B IT vs Laguna XS 2.1

> Laguna XS 2.1 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-laguna-xs-2-1
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.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

Laguna XS 2.1 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 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
- Laguna XS 2.1: grade C, 61/100, rank #40 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-xs-2-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.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 55
- Documentation and upkeep, 75 against 46

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

### Laguna XS 2.1 (C)

Good for: Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.

Watch for: No reply from Poolside to any discussion, including reports about the tokenizer and remote code


## Score by category

| Category | Weight | Gemma 4 E2B IT | Laguna XS 2.1 | 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 | 55 | Gemma 4 E2B IT +15 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 75 | 46 | Gemma 4 E2B IT +29 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **60.4 · C** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 E2B IT | Laguna XS 2.1 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Poolside |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-08-19 |
| Terms last updated | no document linked |  |
| Privacy policy last updated | 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.

**Laguna XS 2.1.** A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.

## 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

### Laguna XS 2.1

1. Quickest start is `ollama run laguna-xs-2.1`, 20 GB of files, the same Q4_K_M as Poolside's GGUF
2. On our reference machine use `llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072`, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB)
3. With vLLM use `--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on a build with vllm-project/vllm#47311, or `--tool-call-parser glm47` on older builds
4. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding
5. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped
6. Sampling per the card is temperature 1.0 and top_k 20
7. The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, Gemma 4 E2B IT or Laguna XS 2.1?

Laguna XS 2.1 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 and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e2b-it", "b": "laguna-xs-2-1"}`. From a terminal: `anchor compare gemma-4-e2b-it laguna-xs-2-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json and https://www.anchorterminal.com/api/v1/tools/laguna-xs-2-1.json

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- [Laguna XS 2.1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-397b.md)
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- [Laguna XS 2.1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-2b.md)
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- [Laguna XS 2.1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-5-4b.md)
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- [Laguna XS 2.1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-6-35b-a3b.md)
- [Laguna XS 2.1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-27b.md)
- [Laguna XS 2.1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-8-flash-next.md)
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- [Laguna XS 2.1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-coder-next.md)
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- [Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-30b-a3b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-32b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-4b-instruct.md)
- [Laguna XS 2.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwen3-vl-8b-instruct.md)
- [Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-qwopus3-8-27b-flash-v2.md)
- [Laguna XS 2.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-smollm3-3b.md)
- [Laguna XS 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.md)
- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-27b.md)
- [Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-swift-1-5-qwen3-8-flash-next.md)
- [Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.md)

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