# Gemma 4 E4B IT vs Ling-3.0-tiny

> Gemma 4 E4B IT scores 63.2 (B) to Ling-3.0-tiny's 55 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.md (~6,150 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.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 scores 63.2 (B) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 4 of 5 scored categories. 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
- Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.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 90
- Risk, 70 against 50
- Agent interface, 85 against 60
- Documentation and upkeep, 75 against 56

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

### Ling-3.0-tiny (C)

Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT

Watch for: No hosted provider today, and Ollama support is only an unmerged pull request


## Score by category

| Category | Weight | Gemma 4 E4B IT | Ling-3.0-tiny | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 31 | Ling-3.0-tiny +4 |
| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 E4B IT +5 |
| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 E4B IT +20 |
| Agent interface | 10% (10 this run) | 85 | 60 | Gemma 4 E4B IT +25 |
| Documentation and upkeep | 10% (10 this run) | 75 | 56 | Gemma 4 E4B IT +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **55 · C** | |

## Facts side by side

| Fact | Gemma 4 E4B IT | Ling-3.0-tiny |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-08-31 |
| 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.

**Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results.

## 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

### Ling-3.0-tiny

1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3
2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code
3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks
5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20
6. The template keeps earlier reasoning in the prompt, which grows long agent sessions
7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches

## Questions

### Which is better for AI agents, Gemma 4 E4B IT or Ling-3.0-tiny?

Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 4 of 5 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e4b-it", "b": "ling-3-0-tiny"}`. From a terminal: `anchor compare gemma-4-e4b-it ling-3-0-tiny`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json

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