# Gemma 4 31B IT vs Nemotron 3 Super 120B A12B

> Gemma 4 31B IT scores 74.1 (BB) to Nemotron 3 Super 120B A12B's 66.2 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b
- Markdown: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.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 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Super 120B A12B's 66.2 (B), and leads in 3 of 5 scored categories. Nemotron 3 Super 120B A12B leads on agent interface. Both do open-weight models you run yourself.

- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json
- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.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 31B IT (BB)

Good for: An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence

Ahead on:
- Capability, 61 against 54
- Reach, 95 against 75
- Risk, 70 against 65

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5

Watch for: No safety evaluation results published

### Nemotron 3 Super 120B A12B (B)

Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data

Ahead on:
- Agent interface, 90 against 85

Watch for: The card's serving commands pass --trust-remote-code


## Score by category

| Category | Weight | Gemma 4 31B IT | Nemotron 3 Super 120B A12B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 61 | 54 | Gemma 4 31B IT +7 |
| Reach | 25% (25 this run) | 95 | 75 | Gemma 4 31B IT +20 |
| Risk | 20% (20 this run) | 70 | 65 | Gemma 4 31B IT +5 |
| Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Super 120B A12B +5 |
| Documentation and upkeep | 10% (10 this run) | 65 | 65 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **74.1 · BB** | **66.2 · B** | |

## Facts side by side

| Fact | Gemma 4 31B IT | Nemotron 3 Super 120B A12B |
| --- | --- | --- |
| 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 | 2026-03-11 |
| 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 | 3.8/5 (4) | 2.8/5 (4) |

## Verdicts

**Gemma 4 31B IT.** A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.

**Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.

## Before you call either

### Gemma 4 31B IT

1. On the reference machine run `llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768`, adding gemma-4-31B-it-mmproj.gguf for images
2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build
3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does
4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block
5. Drop earlier thoughts from the history except on tool-call turns, as the card asks
6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine

### Nemotron 3 Super 120B A12B

1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)
2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs
3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)
4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says
5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks
6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content
7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)

## Questions

### Which is better for AI agents, Gemma 4 31B IT or Nemotron 3 Super 120B A12B?

Gemma 4 31B IT scores 74.1 (BB) on agent readiness against Nemotron 3 Super 120B A12B's 66.2 (B), and leads in 3 of 5 scored categories. Nemotron 3 Super 120B A12B leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-31b-it", "b": "nemotron-3-super-120b-a12b"}`. From a terminal: `anchor compare gemma-4-31b-it nemotron-3-super-120b-a12b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json

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