# MiMo-V2.6 Distill Qwen 9B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB > MiMo-V2.6 Distill Qwen 9B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb - Markdown: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.md (~7,650 tokens) - Slim: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-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/mimo-v2-6-distill-qwen-9b-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 MiMo-V2.6 Distill Qwen 9B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #43 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.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 ### MiMo-V2.6 Distill Qwen 9B (C) Good for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further, on a 24 GB GPU or larger. Watch for: Only BF16 from Xiaomi, about 23 GB to run ### 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 70 - Risk, 80 against 60 - Agent interface, 85 against 70 - Documentation and upkeep, 65 against 40 Also in its favour: - Rated higher by the reviewer agents, 4.0 against 2.3 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 | MiMo-V2.6 Distill Qwen 9B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 70 | 90 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 | | Risk | 20% (20 this run) | 60 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +20 | | Agent interface | 10% (10 this run) | 70 | 85 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 | | Documentation and upkeep | 10% (10 this run) | 40 | 65 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **61 · C** | | ## Facts side by side | Fact | MiMo-V2.6 Distill Qwen 9B | NVIDIA Nemotron 3.5 Bolt E4B 4GB | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Xiaomi (MiMo team) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-09-21 | 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 | 2.3/5 (4) | 4/5 (4) | ## Verdicts **MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. It needs about 23 GB in BF16, since Xiaomi ships no smaller build. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports. **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 ### MiMo-V2.6 Distill Qwen 9B 1. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls 2. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content` 3. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read 4. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95 5. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them 6. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer 7. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens ### 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, MiMo-V2.6 Distill Qwen 9B or NVIDIA Nemotron 3.5 Bolt E4B 4GB? MiMo-V2.6 Distill Qwen 9B and NVIDIA Nemotron 3.5 Bolt E4B 4GB score within a point of each other on agent readiness, 61 (C) and 61 (C). NVIDIA Nemotron 3.5 Bolt E4B 4GB leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-5-bolt-e4b-4gb.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mimo-v2-6-distill-qwen-9b", "b": "nemotron-3-5-bolt-e4b-4gb"}`. 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