# Gemma 4 E4B IT vs Nemotron 3 Nano 4B

> Gemma 4 E4B IT scores 63.2 (B) to Nemotron 3 Nano 4B's 56.8 (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-nemotron-3-nano-4b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.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 Nemotron 3 Nano 4B's 56.8 (C), and leads in every scored category. 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
- Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.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 65
- Agent interface, 85 against 70
- Documentation and upkeep, 75 against 55

Watch for: No safety evaluation figures published

### Nemotron 3 Nano 4B (C)

Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English

Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository


## Score by category

| Category | Weight | Gemma 4 E4B IT | Nemotron 3 Nano 4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 25 | Gemma 4 E4B IT +2 |
| Reach | 25% (25 this run) | 95 | 90 | Gemma 4 E4B IT +5 |
| Risk | 20% (20 this run) | 70 | 65 | Gemma 4 E4B IT +5 |
| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E4B IT +15 |
| Documentation and upkeep | 10% (10 this run) | 75 | 55 | Gemma 4 E4B IT +20 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **56.8 · C** | |

## Facts side by side

| Fact | Gemma 4 E4B IT | Nemotron 3 Nano 4B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-03-16 |
| 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) | 3.3/5 (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.

**Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March.

## 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

### Nemotron 3 Nano 4B

1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build
2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file
3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead
5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks
6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says
7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, Gemma 4 E4B IT or Nemotron 3 Nano 4B?

Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in every scored category.


## For agents

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

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