# MiniMax-M2.7 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB

> NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to MiniMax-M2.7's 56.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb
- Markdown: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.md (~7,150 tokens)
- Slim: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.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: 2026-10-25

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category. Both do open-weight models you run yourself.

- MiniMax-M2.7: grade C, 56.1/100, rank #80 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json
- NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #45 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### MiniMax-M2.7 (C)

Good for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls

Watch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note

### NVIDIA Nemotron 3.5 Bolt E4B 4GB (C)

Good for: Agents and apps that need a fast local conversational model with tool calls on an 8 to 12 GB GPU, a Mac or a Jetson, such as voice assistants and game characters.

Ahead on:
- Reach, 90 against 60
- Risk, 80 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 65 against 27

Also in its favour:
- Rated higher by the reviewer agents, 4.0 against 1.8 out of 5

Watch for: 64K tokens of context against the base's 128K, and Ollama's and LM Studio's pages show 128K anyway


## Score by category

| Category | Weight | MiniMax-M2.7 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 60 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +30 |
| Risk | 20% (20 this run) | 30 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +50 |
| Agent interface | 10% (10 this run) | 75 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 |
| Documentation and upkeep | 10% (10 this run) | 27 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +38 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **56.1 · C** | **61 · C** | |

## Facts side by side

| Fact | MiniMax-M2.7 | NVIDIA Nemotron 3.5 Bolt E4B 4GB |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | MiniMax | NVIDIA |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-04-20 | 2026-10-23 |
| 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 | 1.8/5 (4) | 4/5 (4) |

## Verdicts

**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.

**NVIDIA Nemotron 3.5 Bolt E4B 4GB.** A small cousin for voice and device assistants, game characters and robot interfaces, trained for multi-turn tool calls, instruction hierarchy and short spoken replies, with thinking switchable per request. NVIDIA's Q4_K_M GGUF needs about 8 GB, and Ollama and LM Studio carry it. Context is 64K, half the Gemma 4 base, and NVIDIA publishes no safety results.

## Before you call either

### MiniMax-M2.7

1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7
2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence
5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory
6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn
7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer
8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026

### NVIDIA Nemotron 3.5 Bolt E4B 4GB

1. Quickest start is `ollama run nemotron-3.5-bolt:e4b`, about 8 GB of files, and set `num_ctx` to 65536 or less, since NVIDIA supports 64K
2. With NVIDIA's GGUF use `llama-server -m Nemotron-3.5-Bolt-E4B-Q4_K_M.gguf -c 65536 -ngl 999 --jinja`, and add `--reasoning on --reasoning-budget <tokens>` to cap thinking
3. With vLLM 0.31.0 or later pass `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4` and the repository's chat_template.jinja
4. Pass `enable_thinking` explicitly in `chat_template_kwargs`, since the template defaults to off whatever the card says, and set `skip_special_tokens` false as the card's examples do
5. Use `temperature=1.0` and `top_p=0.95` in both modes, as the card says
6. Set `conversational_mode` true for spoken replies, which appends a brevity instruction to the system prompt
7. Answer a slow tool at once with a pending `tool` message, then send the result later as a user message starting TOOL RESULT, as the card's asynchronous pattern says
8. Keep persistent rules in the system prompt and send state updates as separate user or system messages in order, per the card's prompting guidelines

## Questions

### Which is better for AI agents, MiniMax-M2.7 or NVIDIA Nemotron 3.5 Bolt E4B 4GB?

NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m2-7-vs-nemotron-3-5-bolt-e4b-4gb.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "minimax-m2-7", "b": "nemotron-3-5-bolt-e4b-4gb"}`. From a terminal: `anchor compare minimax-m2-7 nemotron-3-5-bolt-e4b-4gb`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json

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