# Mistral Large 4 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB > NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) 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-3-5-bolt-e4b-4gb - Markdown: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.md (~7,550 tokens) - Slim: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/mistral-large-4-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: 2026-10-30 NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on 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 - NVIDIA Nemotron 3.5 Bolt E4B 4GB: grade C, 61/100, rank #46 of 117 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 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: - Agent interface, 95 against 85 Watch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine ### 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 50 - Risk, 80 against 75 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 | Mistral Large 4 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +40 | | Risk | 20% (20 this run) | 75 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +5 | | Agent interface | 10% (10 this run) | 95 | 85 | Mistral Large 4 +10 | | Documentation and upkeep | 10% (10 this run) | 64 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +1 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **54.6 · C** | **61 · C** | | ## Facts side by side | Fact | Mistral Large 4 | NVIDIA Nemotron 3.5 Bolt E4B 4GB | | --- | --- | --- | | 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) | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-29 | 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 | none | 4/5 (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. **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 ### 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 ### 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 ` 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, Mistral Large 4 or NVIDIA Nemotron 3.5 Bolt E4B 4GB? NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 3 of 4 scored categories. Mistral Large 4 leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-nemotron-3-5-bolt-e4b-4gb.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mistral-large-4", "b": "nemotron-3-5-bolt-e4b-4gb"}`. From a terminal: `anchor compare mistral-large-4 nemotron-3-5-bolt-e4b-4gb` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-bolt-e4b-4gb.json ## Other comparisons with Mistral Large 4 or NVIDIA Nemotron 3.5 Bolt E4B 4GB - [Agents-A1-4B vs Mistral Large 4](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mistral-large-4.md) - [Agents-A1-4B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-bolt-e4b-4gb.md) - [Apertus v1.5 8B vs Mistral Large 4](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mistral-large-4.md) - [Apertus v1.5 8B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-bolt-e4b-4gb.md) - [Apodex-1.1-mini vs Mistral Large 4](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mistral-large-4.md) - [Apodex-1.1-mini vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-5-bolt-e4b-4gb.md) - 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How: https://www.anchorterminal.com/agents/#mcp