# NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-122B-A10B > Qwen3.5-122B-A10B scores 63.6 (B) to NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (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-qwen3-5-122b-a10b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.md (~7,400 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.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-qwen3-5-122b-a10b.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 Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. 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. - 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 - Qwen3.5-122B-A10B: grade B, 63.6/100, rank #23 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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: - Reach, 90 against 80 - Risk, 80 against 55 - Agent interface, 85 against 70 - Documentation and upkeep, 65 against 39 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 ### Qwen3.5-122B-A10B (B) Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence. Watch for: Too big for a single 64 GB machine even at 4 bits ## Score by category | Category | Weight | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3.5-122B-A10B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 80 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +10 | | Risk | 20% (20 this run) | 80 | 55 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +25 | | Agent interface | 10% (10 this run) | 85 | 70 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +15 | | Documentation and upkeep | 10% (10 this run) | 65 | 39 | NVIDIA Nemotron 3.5 Bolt E4B 4GB +26 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **63.6 · B** | | ## Facts side by side | Fact | NVIDIA Nemotron 3.5 Bolt E4B 4GB | Qwen3.5-122B-A10B | | --- | --- | --- | | 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 | 2026-02-24 | | 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. **Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba. ## 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 ### Qwen3.5-122B-A10B 1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context) 2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151 3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do 4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported 5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text 8. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, NVIDIA Nemotron 3.5 Bolt E4B 4GB or Qwen3.5-122B-A10B? Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against NVIDIA Nemotron 3.5 Bolt E4B 4GB's 61 (C), and leads in 0 of 4 scored categories. 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/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-qwen3-5-122b-a10b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-5-bolt-e4b-4gb", "b": "qwen3-5-122b-a10b"}`. 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How: https://www.anchorterminal.com/agents/#mcp