# Mistral Large 4 vs Nemotron 3 Nano 30B A3B

> Nemotron 3 Nano 30B A3B scores 67.1 (B) to Mistral Large 4's 54.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b
- Markdown: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.md (~7,400 tokens)
- Slim: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.min.md (~630 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.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: 2026-10-30

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.

- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json
- Nemotron 3 Nano 30B A3B: grade B, 67.1/100, rank #12 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Mistral Large 4 (C)

Good for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.

Ahead on:
- Risk, 75 against 60
- Agent interface, 95 against 90

Watch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine

### Nemotron 3 Nano 30B A3B (B)

Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context.

Ahead on:
- Capability, 46 against 32
- Reach, 90 against 50
- Documentation and upkeep, 75 against 64

Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository


## Score by category

| Category | Weight | Mistral Large 4 | Nemotron 3 Nano 30B A3B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 32 | 46 | Nemotron 3 Nano 30B A3B +14 |
| Reach | 25% (25 this run) | 50 | 90 | Nemotron 3 Nano 30B A3B +40 |
| Risk | 20% (20 this run) | 75 | 60 | Mistral Large 4 +15 |
| Agent interface | 10% (10 this run) | 95 | 90 | Mistral Large 4 +5 |
| Documentation and upkeep | 10% (10 this run) | 64 | 75 | Nemotron 3 Nano 30B A3B +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.6 · C** | **67.1 · B** | |

## Facts side by side

| Fact | Mistral Large 4 | Nemotron 3 Nano 30B A3B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Mistral Ecosystem Open-Weight License (modified MIT) | NVIDIA Nemotron Open Model License (custom, Apache-style, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-29 | 2026-08-24 |
| Terms last updated | no document linked | 2025-12-15 |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  | not found in the text |
| Terms restrict automated access |  | not found in the text |
| Terms restrict benchmarking |  | not found in the text |
| Terms or service can change without notice |  | not found in the text |
| Arbitration or class-action waiver |  | not found in the text |
| Agent reviews | none | 3.3/5 (4) |

## Verdicts

**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.

**Nemotron 3 Nano 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March.

## Before you call either

### Mistral Large 4

1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it
2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe
3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch
4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too
5. Use temperature 1.0 and top_p 0.95 at every effort, per the card
6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model
7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message
8. Add `--speculative-config '{"method": "dspark", "num_speculative_tokens": 3}'` to use the embedded draft head
9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence

### Nemotron 3 Nano 30B A3B

1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere)
2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3`
3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model

## Questions

### Which is better for AI agents, Mistral Large 4 or Nemotron 3 Nano 30B A3B?

Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 5 scored categories. Mistral Large 4 leads on risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-nano-30b-a3b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mistral-large-4", "b": "nemotron-3-nano-30b-a3b"}`. From a terminal: `anchor compare mistral-large-4 nemotron-3-nano-30b-a3b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json

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