{ "data": { "a": { "slug": "gemma-4-e2b-it", "name": "Gemma 4 E2B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-e2b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json", "repo": "https://huggingface.co/google/gemma-4-E2B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-E2B-it" }, { "registry": "huggingface", "name": "google/gemma-4-E2B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-E2B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:e2b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Google's QAT repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. No hosted version, since the Gemini API serves only the 26B A4B and 31B sizes and Hugging Face lists no inference provider for it. Running it yourself costs the hardware, 11.4 GB at BF16, 5.7 GB at SFP8, 2.9 GB at Q4_0, 1.1 GB for the mobile build and 0.84 GB for the mobile build text only to load the weights per Google (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://gemma.ai/models/gemma-4/", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "on-device", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 60.4, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 51, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 19, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "bestFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "strengths": [ "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file", "Google's own 4-bit QAT GGUF needs about 5.2 GB with working space", "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers", "Tool declaration and call format documented by Google, with parallel calls and a shipped response parser", "131,072 tokens of native context and thinking that turns on or off per request", "Takes audio as well as images and video frames" ], "weaknesses": [ "No safety evaluation figures published", "No documented way to ask for JSON or schema output", "No hosted version, since the Gemini API serves only the two largest sizes", "Training data described only by type and cut-off", "Google's own card numbers put it far below the 12B, 26B A4B and 31B on agent and coding tasks", "Chat-template reports since June wait without a reply" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio", "With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K", "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there", "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns", "Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.4 } ], "editorialScores": { "capability": 19, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "provenanceScore": 68, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run gemma4:e2b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:e2b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-e2b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-E2B", "params": "5.1B", "context": 131072, "goodFor": "Small on-device assistant and agent model, 2.3B effective, with tool calling and optional thinking, taking text, images, video frames and short audio clips for transcription and translation.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-E2B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-q4_0-gguf/blob/main/gemma-4-E2B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 4.34, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)", "LM Studio" ], "note": "Quantisation-aware trained by Google. Size is the repository's two files summed, the 3.35 GB model and the 0.99 GB projector (gemma-4-E2B-it-mmproj.gguf) for image and audio input" }, { "name": "gemma-4-E2B-it-qat-mobile-ct", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-ct", "who": "publisher", "precision": "wNa8o8", "sizeGB": 2.7, "memoryGB": 8, "format": "safetensors", "note": "Google's mobile scheme with 2-bit decoding layers, so below 4-bit in places and not the build Reach's memory line reads. Compressed-tensors format" }, { "name": "gemma-4-E2B-it-qat-mobile-transformers", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-mobile-transformers", "who": "publisher", "precision": "wNa8o8", "sizeGB": 2.49, "memoryGB": 8, "format": "safetensors", "note": "The same mobile scheme as a Transformers reference, which Google says other formats are built from. Google's mobile memory figures are for LiteRT-LM" }, { "name": "ollama gemma4:e2b", "url": "https://ollama.com/library/gemma4:e2b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 4.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (3.5 GB) with a BF16 vision projector (987 MB) and a Q8_0 draft model (98 MB) (digest b37049369adf), the llama.cpp variant. gemma4:e2b-it-qat (4.3 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-e2b", "url": "https://lmstudio.ai/models/google/gemma-4-e2b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 4.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E2B-it-GGUF, with MLX builds at 4 to 8 bits beside it. Size as the catalogue shows it, and Q4_K_M is inferred from that repository's files rather than stated by LM Studio" }, { "name": "gemma-4-E2B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-E2B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 8.35, "memoryGB": 12, "format": "safetensors", "note": "Quantisation-aware trained, 4-bit weights with 16-bit activations in compressed-tensors format, which Google routes to vLLM and SGLang. The per-layer embedding tables stay large, so it's bigger than the GGUF" }, { "name": "gemma-4-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "who": "publisher", "precision": "bf16", "sizeGB": 10.28, "memoryGB": 16, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072", "runBuild": "gemma-4-E2B_q4_0-it", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Google (Gemma)", "family": "Gemma 4", "url": "https://www.anchorterminal.com/open-weights/#google", "others": [ { "name": "gemma-4-26B-A4B", "url": "https://huggingface.co/google/gemma-4-26B-A4B", "params": "25.2B total, 3.8B active" }, { "name": "gemma-4-26B-A4B-it", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it" }, { "name": "gemma-4-31B", "url": "https://huggingface.co/google/gemma-4-31B", "params": "30.7B" }, { "name": "gemma-4-31B-it", "url": "https://huggingface.co/google/gemma-4-31B-it", "params": "30.7B", "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it" }, { "name": "gemma-4-E2B", "url": "https://huggingface.co/google/gemma-4-E2B", "params": "2.3B effective (5.1B with embeddings)" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "name": "gemma-4-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "params": "4.5B effective (8B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it" }, { "name": "gemma-4-26B-A4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant", "params": "420M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-31B-it-assistant", "url": "https://huggingface.co/google/gemma-4-31B-it-assistant", "params": "470M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E2B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant", "params": "78M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-E4B-it-assistant", "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant", "params": "79M (file metadata on Hugging Face; drafter)" }, { "name": "gemma-4-12B", "url": "https://huggingface.co/google/gemma-4-12B", "params": "11.95B" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "params": "11.95B", "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it" }, { "name": "gemma-4-12B-it-assistant", "url": "https://huggingface.co/google/gemma-4-12B-it-assistant", "params": "423M (file metadata on Hugging Face; drafter)" }, { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "params": "25.2B total, 3.8B active", "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it" }, { "name": "embeddinggemma-2", "url": "https://huggingface.co/google/embeddinggemma-2", "params": "740M" } ], "olderCount": 171, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Google LLC (RDAP registrant of gemma.ai as recorded for the first batch's Gemma 4 listings; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://gemma.ai/models/releases/", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date are carried from the gemma-4-26b-a4b-it and gemma-4-31b-it facts read earlier on 21 October 2026, since rdap.org rate-limited our request this time and wasn't retried.", "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai keeps a dated releases page for the Gemma family (this size on 31 March 2026). The Hugging Face commit history says what each change fixed.", "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemma-4-e2b-it.json", "live": { "slug": "gemma-4-e2b-it", "securityTxt": { "url": "https://gemma.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-27T15:31:51.320840497Z" }, "llmsTxt": { "url": "https://gemma.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-27T14:58:08.693622626Z" }, "domain": { "domain": "gemma.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai", "checkedAt": "2026-10-25T14:01:21.204554738Z" }, "updatedAt": "2026-10-27T15:31:51.320840497Z" } }, "answer": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.", "b": { "slug": "lbs-27b", "name": "LBS-27B", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "LBS-27B is an open-weight vision-language model from inclusionAI's UI-Venus line, fine-tuned from Qwen3.8-27B to find what to click on a screenshot by zooming into regions over several turns, and applied to visual questions on large images.", "url": "https://www.anchorterminal.com/tools/lbs-27b", "markdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lbs-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lbs-27b.json", "repo": "https://huggingface.co/inclusionAI/LBS-27B", "license": "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)", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/LBS-27B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for inclusionAI/LBS-27B (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card states for the weights and the code. Running it costs the hardware, 54.73 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://github.com/inclusionAI/UI-Venus/tree/LBS", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "qwen", "vision", "gui-grounding", "computer-use", "reasoning", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.9, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 73, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "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.", "bestFor": "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.", "strengths": [ "Qwen3.8-27B's stock architecture with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it", "Apache 2.0 for the weights and the base, ungated, safetensors only", "A simple, documented plain-text crop protocol with a point answer on a 0 to 1000 scale, and the training recipe and evaluation kit published", "Up to two crops per turn, learned in GRPO, and 262,144 tokens of context from the base", "inclusionAI publishes per-item scores and selected trajectories for its own benchmark runs" ], "weaknesses": [ "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs", "BF16 only, about 66 GB to load, and no Ollama, LM Studio or quantised build", "The crop loop and system prompt come from the publisher's Python, not a runtime parser or a template", "No safety evaluation, prompt-injection guidance, training data sources or knowledge cut-off", "No hosted API or inference provider serves it" ], "agentNotes": [ "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", "Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text", "Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, 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", "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", "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", "Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.9 } ], "editorialScores": { "interface": 70, "reach": 55, "risk": 50, "upkeep": 43 }, "provenanceScore": 57, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# The card's route clones the LBS branch and wraps vllm serve (four GPUs in its example)\ngit clone --branch LBS --single-branch https://github.com/inclusionAI/UI-Venus.git\ncd UI-Venus/evaluation\nbash scripts/serve_vllm.sh start inclusionAI/LBS-27B --name lbs --tp 4 --dp 1 --daemon\n# the same server without the wrapper (the script's defaults are a 65,536-token context and 64 sequences)\nvllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"lbs\", \"max_tokens\": 2560,\n \"messages\": [{\"role\": \"system\", \"content\": \"\u003ctask, image size, crop_image spec and answer format, as build_system_prompt in the LBS branch writes them\u003e\"},\n {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"image_path: screenshot.png\"},\n {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/png;base64,...\"}}]}],\n \"chat_template_kwargs\": {\"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lbs-27b" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group), with Wuhan University of Technology, CASIA and Zhejiang University", "builtOn": "Qwen/Qwen3.8-27B (fine-tune, named in the card's text, not in its base_model metadata; Apache 2.0, copyright Alibaba Cloud)", "params": "27B", "context": 262144, "goodFor": "A cousin that finds the point to click on a screenshot, and answers questions about fine detail in large images, by zooming in over a few turns, on a machine with about 96 GB of memory and a client that runs the crops, if inclusionAI's own results hold up.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "LBS-27B (BF16)", "url": "https://huggingface.co/inclusionAI/LBS-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.73, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Whole repository, 54.73 GB, almost all of it 12 BF16 shards. 54.73 GB × 1.2 = 65.7 GB, just over 64 GB. The only build; no Ollama, LM Studio or ggml-org build and no copy by anyone else on 27 October 2026" } ], "run": "vllm serve inclusionAI/LBS-27B --served-model-name lbs --tensor-parallel-size 4 --max-model-len 65536", "runBuild": "LBS-27B (BF16)", "checked": "2026-10-27", "minMemoryGB": 96 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "UI-Venus", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "UI-Venus-2-9B", "url": "https://huggingface.co/inclusionAI/UI-Venus-2-9B", "params": "9B" }, { "name": "UI-Venus-1.5-30B-A3B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-30B-A3B", "params": "30B total, 3B active" }, { "name": "UI-Venus-1.5-8B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-8B", "params": "8B" }, { "name": "UI-Venus-1.5-2B", "url": "https://huggingface.co/inclusionAI/UI-Venus-1.5-2B", "params": "2B" } ], "olderCount": 54 }, "area": "models", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org); the work is credited to authors from Wuhan University of Technology, CASIA, Zhejiang University and Ant Group", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/LBS-27B/commits/main", "securityTxt": "none", "checked": "2026-10-27", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI (read 2026-10-27). The Hub doesn't verify the organisation, which the calibration of 21 October credits as the publisher's own. The domain registration date is carried from the Ming-3.0 listings' check of 21 October 2026.", "The model card links github.com/inclusionAI/UI-Venus on its LBS branch, where the LICENSE, the NOTICE, the training recipe and the evaluation kit sit. The NOTICE reads Copyright 2026 the LBS contributors.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, two commits on 26 October 2026 (initial commit and one upload).", "inclusion-ai.org/.well-known/security.txt reset the connection on 27 October 2026 and answered 404 on 21 October. The LBS branch has no SECURITY.md. Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, answering on 27 October 2026); nothing on the card or the branch links it." ], "score": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lbs-27b.json", "live": { "slug": "lbs-27b", "securityTxt": { "url": "https://inclusion-ai.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-27T15:31:25.810013407Z" }, "updatedAt": "2026-10-27T15:31:25.810013407Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "inclusionAI (Ant Group)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "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)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-10-26", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.", "question": "Which is better for AI agents, Gemma 4 E2B IT or LBS-27B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55", "Risk, 70 against 50", "Agent interface, 85 against 70", "Documentation and upkeep, 75 against 43" ], "also": null, "goodFor": "Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning", "slug": "gemma-4-e2b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": null, "also": null, "goodFor": "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.", "slug": "lbs-27b", "watchFor": "No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.json", "title": "Agents-A1-4B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.json", "title": "Agents-A1-4B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.json", "title": "Apertus v1.5 8B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.json", "title": "Apodex-1.1-mini vs LBS-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.json", "title": "BigBang-v1 vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.json", "title": "BigBang-v1 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.json", "title": "Carbon-X vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.json", "title": "Carbon-X vs LBS-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it.json", "title": "Cosmos-Reason2-2B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.json", "title": "Cosmos-Reason2-2B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.json", "title": "Darwin-36B-Opus vs LBS-27B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.json", "title": "DeepSeek-V4-Flash-0731 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.json", "title": "DiffusionGemma 26B A4B IT vs LBS-27B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it.json", "title": "Gemma 4 12B IT vs Gemma 4 E2B IT", "url": 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"https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash.json", "title": "LBS-27B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b.json", "title": "LBS-27B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b.json", "title": "LBS-27B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LBS-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LBS-27B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b.json", "title": "LBS-27B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b.json", "title": "LBS-27B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LBS-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b.json", "title": "LBS-27B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LBS-27B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json", "title": "LBS-27B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think.json", "title": "LBS-27B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b.json", "title": "LBS-27B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b.json", "title": "LBS-27B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b.json", "title": "LBS-27B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LBS-27B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507.json", "title": "LBS-27B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b.json", "title": "LBS-27B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b.json", "title": "LBS-27B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b.json", "title": "LBS-27B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b.json", "title": "LBS-27B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b.json", "title": "LBS-27B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b.json", "title": "LBS-27B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b.json", "title": "LBS-27B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b.json", "title": "LBS-27B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b.json", "title": "LBS-27B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next.json", "title": "LBS-27B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next.json", "title": "LBS-27B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct.json", "title": "LBS-27B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LBS-27B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct.json", "title": "LBS-27B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct.json", "title": "LBS-27B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct.json", "title": "LBS-27B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "LBS-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.json", "title": "LBS-27B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b.json", "title": "LBS-27B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.json", "title": "LBS-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b.json", "title": "LBS-27B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LBS-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "LBS-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.json", "title": "LBS-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 95, "key": "reach", "lbs-27b": 55, "name": "Reach", "weight": 25 }, { "by": 20, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 70, "key": "risk", "lbs-27b": 50, "name": "Risk", "weight": 20 }, { "by": 15, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 85, "key": "interface", "lbs-27b": 70, "name": "Agent interface", "weight": 10 }, { "by": 32, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 75, "key": "upkeep", "lbs-27b": 43, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 E2B IT scores 60.4 (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.", "tutorials": [], "verdicts": { "gemma-4-e2b-it": "A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b", "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.min.md" }, "markdown": "Gemma 4 E2B IT scores 60.4 (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.\n\n- Gemma 4 E2B IT: grade C, 60.4/100, rank #59 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e2b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 E2B IT (C)\n\nGood for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning\n\nAhead on:\n- Reach, 95 against 55\n- Risk, 70 against 50\n- Agent interface, 85 against 70\n- Documentation and upkeep, 75 against 43\n\nWatch for: No safety evaluation figures published\n\n### LBS-27B (D)\n\nGood 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.\n\nWatch for: No independent benchmark result yet; ScreenSpot-Pro, OS-World-G, PerceptionBench and HR-Bench numbers are inclusionAI's own runs\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 E2B IT | LBS-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 E2B IT +40 |\n| Risk | 20% (20 this run) | 70 | 50 | Gemma 4 E2B IT +20 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Gemma 4 E2B IT +15 |\n| Documentation and upkeep | 10% (10 this run) | 75 | 43 | Gemma 4 E2B IT +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.4 · C** | **53.9 · D** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E2B IT | LBS-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | inclusionAI (Ant Group) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | 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) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.5/5 (4) | none |\n\n## Verdicts\n\n**Gemma 4 E2B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 5 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.\n\n**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.\n\n## Before you call either\n\n### Gemma 4 E2B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E2B-it-qat-q4_0-gguf --hf-file gemma-4-E2B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E2B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e2b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them\n\n### LBS-27B\n\n1. 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\n2. Build the system prompt with `build_system_prompt` from the LBS branch's `zoomin_grounding_protocol.py`, send the screenshot after `image_path: \u003cname\u003e`, and don't pass `tools`; the model answers in plain text\n3. Parse `\u003caction\u003ecrop_image(image_path=..., x1=..., y1=..., x2=..., y2=...)\u003c/action\u003e`, 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\n4. 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\n5. 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\n6. Don't turn on MTP speculative decoding; the MTP weights aren't in the repository though config.json names one MTP layer\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E2B IT or LBS-27B?\n\nGemma 4 E2B IT scores 60.4 (C) on agent readiness against LBS-27B's 53.9 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e2b-it\", \"b\": \"lbs-27b\"}`. From a terminal: `anchor compare gemma-4-e2b-it lbs-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json and https://www.anchorterminal.com/api/v1/tools/lbs-27b.json\n\n## Other comparisons with Gemma 4 E2B IT or LBS-27B\n\n- [Agents-A1-4B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.md)\n- [Agents-A1-4B vs LBS-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lbs-27b.md)\n- [Apertus v1.5 8B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.md)\n- [Apertus v1.5 8B vs LBS-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lbs-27b.md)\n- [Apodex-1.1-mini vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.md)\n- [Apodex-1.1-mini vs LBS-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lbs-27b.md)\n- [BigBang-v1 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.md)\n- [BigBang-v1 vs LBS-27B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lbs-27b.md)\n- [Carbon-X vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.md)\n- [Carbon-X vs LBS-27B](https://www.anchorterminal.com/compare/carbon-x-vs-lbs-27b.md)\n- [Cosmos-Reason2-2B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e2b-it.md)\n- [Cosmos-Reason2-2B vs LBS-27B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lbs-27b.md)\n- [Darwin-36B-Opus vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.md)\n- [Darwin-36B-Opus vs LBS-27B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lbs-27b.md)\n- [DeepSeek-V3.2 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e2b-it.md)\n- [DeepSeek-V3.2 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lbs-27b.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.md)\n- [DeepSeek V4.1 Flash vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.md)\n- [DeepSeek-V4-Flash-0731 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.md)\n- [DeepSeek-V4-Flash-0731 vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lbs-27b.md)\n- [DiffusionGemma 26B A4B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it.md)\n- [DiffusionGemma 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lbs-27b.md)\n- [Gemma 4 12B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 12B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lbs-27b.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 26B A4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lbs-27b.md)\n- [Gemma 4 31B IT vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e2b-it.md)\n- [Gemma 4 31B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lbs-27b.md)\n- [Gemma 4 E2B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 E2B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.md)\n- [Gemma 4 E2B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E2B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3.md)\n- [Gemma 4 E2B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E2B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 E2B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E2B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 E2B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.md)\n- [Gemma 4 E2B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling.md)\n- [Gemma 4 E2B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small.md)\n- [Gemma 4 E2B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 E2B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.md)\n- [Gemma 4 E2B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1.md)\n- [Gemma 4 E2B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kronos.md)\n- [Gemma 4 E2B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-m-1.md)\n- [Gemma 4 E2B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E2B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E2B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E2B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E2B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 E2B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E2B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minicpm5-2b.md)\n- [Gemma 4 E2B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5.md)\n- [Gemma 4 E2B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E2B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 E2B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E2B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E2B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 E2B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 E2B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 E2B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.md)\n- [Gemma 4 E2B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E2B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 E2B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 E2B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 E2B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 E2B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 E2B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 E2B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 E2B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 E2B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct.md)\n- [Gemma 4 E2B IT vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2.md)\n- [Gemma 4 E2B IT vs Recursive Flint](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.md)\n- [Gemma 4 E2B IT vs SmolLM3-3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.md)\n- [Gemma 4 E2B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 E4B IT vs LBS-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lbs-27b.md)\n- [GLM-4.5-Air vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lbs-27b.md)\n- [GLM-4.7-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lbs-27b.md)\n- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md)\n- [GLM-5.3 vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-vs-lbs-27b.md)\n- [gpt-oss-120b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lbs-27b.md)\n- [gpt-oss-20b vs LBS-27B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lbs-27b.md)\n- [Granite 4.2 3B vs LBS-27B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lbs-27b.md)\n- [Hy3 vs LBS-27B](https://www.anchorterminal.com/compare/hy3-vs-lbs-27b.md)\n- [Inkling-Small vs LBS-27B](https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.md)\n- [Inkling vs LBS-27B](https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LBS-27B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lbs-27b.md)\n- [Kimi K3 vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.md)\n- [Kolibri-1 vs LBS-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.md)\n- [Kronos vs LBS-27B](https://www.anchorterminal.com/compare/kronos-vs-lbs-27b.md)\n- [Laguna M.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lbs-27b.md)\n- [Laguna S 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lbs-27b.md)\n- [Laguna XS 2.1 vs LBS-27B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lbs-27b.md)\n- [LBS-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/lbs-27b-vs-lfm2-5-2-6b.md)\n- [LBS-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lbs-27b-vs-ling-3-0-tiny.md)\n- [LBS-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LBS-27B vs MiniCPM-o 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(Qwen3.8 27B)](https://www.anchorterminal.com/compare/lbs-27b-vs-thinkingcap-qwen3-8-27b.md)\n- [LBS-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-27", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 E2B IT vs LBS-27B", "url": "" } ], "description": "Gemma 4 E2B IT scores 60.4 (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.", "facts": [ "Gemma 4 E2B IT C 60.4", "LBS-27B D 53.9", "scores" ], "h1": "Gemma 4 E2B IT vs LBS-27B", "image": "https://www.anchorterminal.com/assets/og/compare-gemma-4-e2b-it-vs-lbs-27b.png", "path": "/compare/gemma-4-e2b-it-vs-lbs-27b", "published": "2026-10-01", "section": "tools", "title": "Gemma 4 E2B IT vs LBS-27B for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-27", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lbs-27b" }, "tokens": { "markdown": 6350, "slim": 480 }, "version": 1 }