# Nemotron 3 Super 120B A12B vs Qwen3.6-35B-A3B > Nemotron 3 Super 120B A12B scores 66.2 (B) to Qwen3.6-35B-A3B's 63.2 (B) 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-super-120b-a12b-vs-qwen3-6-35b-a3b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.md (~6,700 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.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: 2026-10-21 Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach. Both do open-weight models you run yourself. - Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #15 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json - Qwen3.6-35B-A3B: grade B, 63.2/100, rank #25 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-35b-a3b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Nemotron 3 Super 120B A12B (B) Good for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data Ahead on: - Risk, 65 against 55 - Agent interface, 90 against 70 - Documentation and upkeep, 65 against 36 Watch for: The card's serving commands pass --trust-remote-code ### Qwen3.6-35B-A3B (B) Good for: Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision. Ahead on: - Reach, 85 against 75 Watch for: No safety evaluation, no agent-use guidance and no way to report a security flaw ## Score by category | Category | Weight | Nemotron 3 Super 120B A12B | Qwen3.6-35B-A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 54 | 58 | Qwen3.6-35B-A3B +4 | | Reach | 25% (25 this run) | 75 | 85 | Qwen3.6-35B-A3B +10 | | Risk | 20% (20 this run) | 65 | 55 | Nemotron 3 Super 120B A12B +10 | | Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3 Super 120B A12B +20 | | Documentation and upkeep | 10% (10 this run) | 65 | 36 | Nemotron 3 Super 120B A12B +29 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **66.2 · B** | **63.2 · B** | | ## Facts side by side | Fact | Nemotron 3 Super 120B A12B | Qwen3.6-35B-A3B | | --- | --- | --- | | 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 | NVIDIA Nemotron Open Model License | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-03-11 | 2026-04-16 | | 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 | 2.8/5 (4) | 2.8/5 (4) | ## Verdicts **Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results. **Qwen3.6-35B-A3B.** A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports. ## Before you call either ### Nemotron 3 Super 120B A12B 1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin) 2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs 3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`) 4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says 5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks 6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content 7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`) ### Qwen3.6-35B-A3B 1. Start it with `ollama run qwen3.6:35b` (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory) 2. On the reference machine use `vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, BF16, since vLLM doesn't document FP8 compute on gfx1151 3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later 4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning 5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8 6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens 7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none ## Questions ### Which is better for AI agents, Nemotron 3 Super 120B A12B or Qwen3.6-35B-A3B? Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Qwen3.6-35B-A3B's 63.2 (B), and leads in 3 of 5 scored categories. Qwen3.6-35B-A3B leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-super-120b-a12b", "b": "qwen3-6-35b-a3b"}`. 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How: https://www.anchorterminal.com/agents/#mcp