# LBS-27B vs Recursive Flint > Recursive Flint scores 61 (C) 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-recursive-flint - Markdown: https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md (~6,450 tokens) - Slim: https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.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 Recursive Flint scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. 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 - Recursive Flint: grade C, 61/100, rank #50 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.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 ### Recursive Flint (C) Good for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence. Ahead on: - Reach, 75 against 55 - Risk, 60 against 50 - Agent interface, 90 against 70 - Documentation and upkeep, 67 against 43 Watch for: No independent benchmark results yet, every published score is Recursive's ## Score by category | Category | Weight | LBS-27B | Recursive Flint | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 55 | 75 | Recursive Flint +20 | | Risk | 20% (20 this run) | 50 | 60 | Recursive Flint +10 | | Agent interface | 10% (10 this run) | 70 | 90 | Recursive Flint +20 | | Documentation and upkeep | 10% (10 this run) | 43 | 67 | Recursive Flint +24 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **53.9 · D** | **61 · C** | | ## Facts side by side | Fact | LBS-27B | Recursive Flint | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | Recursive | | 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) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-26 | 2026-10-21 | | 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 | 3.5/5 (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. **Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off. ## 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 ### Recursive Flint 1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp 2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on 3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out 4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask 5. Strip , , and from untrusted tool output, the template doesn't escape them and a copied can add a call 6. Message content must be a string, flatten content-part arrays first 7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key ## Questions ### Which is better for AI agents, LBS-27B or Recursive Flint? Recursive Flint scores 61 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lbs-27b", "b": "recursive-flint"}`. From a terminal: `anchor compare lbs-27b recursive-flint` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/lbs-27b.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json ## Other comparisons with LBS-27B or Recursive Flint - [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md) - [Agents-A1-4B vs Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.md) - [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md) - [Apertus v1.5 8B vs Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.md) - [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md) - [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md) - [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md) - [BigBang-v1 vs Recursive Flint](https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.md) - 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