# LBS-27B vs Nemotron 3 Ultra 550B A55B > Nemotron 3 Ultra 550B A55B scores 68.8 (B) to LBS-27B's 53.9 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.md (~6,900 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.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: 2026-10-27 Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself. - LBS-27B: grade D, 53.9/100, rank #91 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/lbs-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/lbs-27b.json - Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### LBS-27B (D) Good for: Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results. Watch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs ### Nemotron 3 Ultra 550B A55B (B) Good for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs Ahead on: - Risk, 70 against 50 - Agent interface, 90 against 70 - Documentation and upkeep, 65 against 43 Watch for: The smallest build is 352 GB and needs at least four B200-class GPUs ## Score by category | Category | Weight | LBS-27B | Nemotron 3 Ultra 550B A55B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 55 | even | | Risk | 20% (20 this run) | 50 | 70 | Nemotron 3 Ultra 550B A55B +20 | | Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Ultra 550B A55B +20 | | Documentation and upkeep | 10% (10 this run) | 43 | 65 | Nemotron 3 Ultra 550B A55B +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **68.8 · B** | | ## Facts side by side | Fact | LBS-27B | Nemotron 3 Ultra 550B A55B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) | OpenMDW-1.1 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-26 | 2026-06-04 | | 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 | none | 2.8/5 (4) | ## Verdicts **LBS-27B.** A 27B Qwen3.8 fine-tune for GUI grounding that zooms into screenshots over a few turns before naming a point, and inclusionAI says it also helps with fine detail in large images. It needs about 96 GB at BF16 and a client that runs its crop actions. Every result so far is inclusionAI's own, with no safety results or injection guidance. **Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results. ## Before you call either ### LBS-27B 1. Serve with `vllm serve inclusionAI/LBS-27B --tensor-parallel-size 4 --max-model-len 65536`, which is what the card's wrapper script runs; keep the context at 32,768 or more for multi-turn images 2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: `, and don't pass `tools`; the model answers in plain text 3. Parse `crop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)`, cut the crop yourself and return it with its mapping to the original image; allow at most two crops per turn and three per episode 4. Read the final answer as `[x.xx,y.yy]` on a 0 to 1000 scale of the original image, and use your own executor to click 5. Pass `reasoning_effort` as `xhigh`, `medium` or `low` only, since the template raises an error for `high`, and don't count on `thinking_budget`, which the template ignores 6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer ### Nemotron 3 Ultra 550B A55B 1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead 2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more 3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register 4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse 5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens 6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets ## Questions ### Which is better for AI agents, LBS-27B or Nemotron 3 Ultra 550B A55B? Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against LBS-27B's 53.9 (D), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "nemotron-3-ultra-550b-a55b"}`. 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How: https://www.anchorterminal.com/agents/#mcp