# Laguna M.1 vs Nemotron 3 Super 120B A12B > Nemotron 3 Super 120B A12B scores 66.2 (B) to Laguna M.1's 55.5 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b - Markdown: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.md (~6,450 tokens) - Slim: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.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 Laguna M.1's 55.5 (C), and leads in every scored category. Both do open-weight models you run yourself. - Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json - 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 - 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 ### Laguna M.1 (C) Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0. Watch for: The smallest build needs about 167 GB, more than a 128 GB machine ### 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: - Capability, 54 against 46 - Reach, 75 against 65 - Risk, 65 against 55 - Agent interface, 90 against 85 - Documentation and upkeep, 65 against 36 Watch for: The card's serving commands pass --trust-remote-code ## Score by category | Category | Weight | Laguna M.1 | Nemotron 3 Super 120B A12B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 46 | 54 | Nemotron 3 Super 120B A12B +8 | | Reach | 25% (25 this run) | 65 | 75 | Nemotron 3 Super 120B A12B +10 | | Risk | 20% (20 this run) | 55 | 65 | Nemotron 3 Super 120B A12B +10 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Super 120B A12B +5 | | Documentation and upkeep | 10% (10 this run) | 36 | 65 | Nemotron 3 Super 120B A12B +29 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **55.5 · C** | **66.2 · B** | | ## Facts side by side | Fact | Laguna M.1 | Nemotron 3 Super 120B A12B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Poolside | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | NVIDIA Nemotron Open Model License | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-14 | 2026-03-11 | | Terms last updated | | no document linked | | Privacy policy last updated | | 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 | 2.5/5 (4) | 2.8/5 (4) | ## Verdicts **Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route. **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. ## Before you call either ### Laguna M.1 1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later 2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding 3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 4. Sampling per the card is temperature 1.0 and top_k 20 5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM 6. It doesn't fit our 128 GB reference machine at any published precision 7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ### 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`) ## Questions ### Which is better for AI agents, Laguna M.1 or Nemotron 3 Super 120B A12B? Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Laguna M.1's 55.5 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "laguna-m-1", "b": "nemotron-3-super-120b-a12b"}`. 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How: https://www.anchorterminal.com/agents/#mcp