# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3-4B-Instruct-2507 > NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) to Qwen3-4B-Instruct-2507's 55.7 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507 - Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.md (~7,500 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.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 Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 3 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach. Both do open-weight models you run yourself. - 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 - Qwen3-4B-Instruct-2507: grade C, 55.7/100, rank #81 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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 ### 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: - Risk, 80 against 55 - Agent interface, 85 against 75 - Documentation and upkeep, 65 against 26 Also in its favour: - Rated higher by the reviewer agents, 4.0 against 3.0 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 ### Qwen3-4B-Instruct-2507 (C) Good for: Agents that want a capable small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. Ahead on: - Reach, 95 against 90 Watch for: No safety results, prompt-injection guidance or security contact ## Score by category | Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3-4B-Instruct-2507 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 95 | Qwen3-4B-Instruct-2507 +5 | | Risk | 20% (20 this run) | 80 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Agent interface | 10% (10 this run) | 85 | 75 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 | | Documentation and upkeep | 10% (10 this run) | 65 | 26 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +39 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **55.7 · C** | | ## Facts side by side | Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3-4B-Instruct-2507 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | OpenMDW-1.1 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-23 | 2025-09-17 | | 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 | 4/5 (4) | 3/5 (4) | ## Verdicts **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. **Qwen3-4B-Instruct-2507.** A small, fast cousin for instruction following, short tool-calling jobs and long documents on modest hardware, with no thinking step. It runs in about 3 GB at 4 bits on every common runtime, and two providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results. ## Before you call either ### 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 ### Qwen3-4B-Instruct-2507 1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes` 3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags 4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed 5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition 6. Drop the context to 32,768 tokens if memory runs out, as the card suggests 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Qwen3-4B-Instruct-2507? NVIDIA Nemotron 3.5 Bolt E4B 4GB scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 55.7 (C), and leads in 3 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-4b-instruct-2507.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "qwen3-4b-instruct-2507"}`. 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How: https://www.anchorterminal.com/agents/#mcp