{ "data": { "a": { "slug": "gemma-4-e4b-it", "name": "Gemma 4 E4B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 E4B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 4.5B effective parameters (8B 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-e4b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-e4b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json", "repo": "https://huggingface.co/google/gemma-4-E4B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-E4B-it" }, { "registry": "huggingface", "name": "google/gemma-4-E4B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-E4B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:e4b" } ], "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, 17.9 GB at BF16, 8.9 GB at SFP8, 4.5 GB at Q4_0, 2.5 GB for the mobile build and 2.2 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": 63.2, "grade": "B", "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": 1211, "categoryRank": 19, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 27, "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 7 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 7.4 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-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio", "With Ollama run `ollama run gemma4:e4b` 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": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "capability": 27, "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:e4b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:e4b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-e4b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-E4B", "params": "8B", "context": 131072, "goodFor": "Small on-device assistant and agent model, 4.5B 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-E4B_q4_0-it", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-gguf/blob/main/gemma-4-E4B_q4_0-it.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 6.15, "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 5.15 GB model and the 0.99 GB projector (gemma-4-E4B-it-mmproj.gguf) for image and audio input" }, { "name": "gemma-4-E4B-it-qat-mobile-ct", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-ct", "who": "publisher", "precision": "wNa8o8", "sizeGB": 3.77, "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-E4B-it-qat-mobile-transformers", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-mobile-transformers", "who": "publisher", "precision": "wNa8o8", "sizeGB": 3.56, "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:e4b", "url": "https://ollama.com/library/gemma4:e4b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (5.5 GB) with a BF16 vision projector (992 MB) and a Q8_0 draft model (99 MB) (digest dc35e8d9c606), the llama.cpp variant. gemma4:e4b-it-qat (6.1 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-e4b", "url": "https://lmstudio.ai/models/google/gemma-4-e4b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 5.9, "memoryGB": 8, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-E4B-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-E4B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-E4B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 11.55, "memoryGB": 16, "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-E4B-it", "url": "https://huggingface.co/google/gemma-4-E4B-it", "who": "publisher", "precision": "bf16", "sizeGB": 16.02, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072", "runBuild": "gemma-4-E4B_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-E2B-it", "url": "https://huggingface.co/google/gemma-4-E2B-it", "params": "2.3B effective (5.1B with embeddings)", "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it" }, { "name": "gemma-4-E4B", "url": "https://huggingface.co/google/gemma-4-E4B", "params": "4.5B effective (8B with embeddings)" }, { "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-e4b-it.json", "live": { "slug": "gemma-4-e4b-it", "securityTxt": { "url": "https://gemma.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-26T15:29:21.729636385Z" }, "llmsTxt": { "url": "https://gemma.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-26T14:55:06.832925719Z" }, "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": "2026-10-26T15:29:21.729636385Z" } }, "answer": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "nanbeige-4-3-33b", "name": "Nanbeige4.3-33B", "vendor": "Nanbeige LLM Lab (BOSS Zhipin)", "vendorUrl": "https://huggingface.co/Nanbeige", "kind": "weights", "category": "open-weights", "summary": "Nanbeige4.3-33B is the Nanbeige LLM Lab's open-weight dense model for agentic coding and office work, 33B parameters on the Llama architecture with a 512K-token context, thinking that switches per request and tool calls in an XML format.", "url": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b", "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json", "repo": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "Nanbeige/Nanbeige4.3-33B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for Nanbeige/Nanbeige4.3-33B and Nanbeige/Nanbeige4.3-33B-Base (checked 2026-10-25).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata names it. Neither repository holds a LICENSE file. Running it costs the hardware, 66.26 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-25).", "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 2026-10-25).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-25" }, "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "llama", "reasoning", "tool-calling", "coding", "agentic", "long-context", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-24", "graded": true, "anchor": { "graded": true, "score": 61, "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": 1211, "categoryRank": 48, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 55, "risk": 60, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-25" }, "negative": 0, "verdict": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.", "bestFor": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.", "strengths": [ "A plain LlamaForCausalLM with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it", "Apache 2.0 by its metadata, ungated, safetensors only", "524,288 tokens of context set in config.json with no rope scaling", "Thinking switches per request, and preserve_thinking keeps earlier reasoning for agent loops", "Tool calls in an XML format the qwen3_coder parser reads, several per turn", "A Nanbeige member answered four of the first ten discussions on release day" ], "weaknesses": [ "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run", "BF16 only from Nanbeige, about 80 GB to load, and no Ollama or LM Studio build", "No LICENSE file in either repository; Apache 2.0 rests on the metadata tag", "No safety evaluation, agent-use guidance, training data description or knowledge cut-off", "No hosted API or inference provider serves it" ], "agentNotes": [ "Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers", "Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes", "Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends", "Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card", "Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed", "Send one system message at the start only, as the template raises an error for a later one", "In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 55, "risk": 60, "upkeep": 56 }, "provenanceScore": 53, "checked": { "categories": { "interface": "2026-10-25", "reach": "2026-10-25", "risk": "2026-10-25", "upkeep": "2026-10-25" }, "last": "2026-10-25", "oldest": "2026-10-25", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM 0.32.0 or SGLang 0.5.22, as the card's commands (the card uses four GPUs with tensor parallelism)\nhf download Nanbeige/Nanbeige4.3-33B --local-dir Nanbeige4.3-33B\nvllm serve ./Nanbeige4.3-33B --served-model-name nanbeige --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or\npython -m sglang.launch_server --model-path ./Nanbeige4.3-33B --tp-size 4 \\\n --reasoning-parser qwen3 --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nanbeige\", \"messages\": [{\"role\": \"user\", \"content\": \"Which number is bigger, 9.11 or 9.8?\"}],\n \"temperature\": 0.6, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nanbeige-4-3-33b" }, "weights": { "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)", "builtOn": "Nanbeige/Nanbeige4.3-33B-Base (fine-tune, declared in the card's metadata; Apache 2.0 by its tag, no LICENSE file)", "params": "33B", "context": 524288, "goodFor": "A cousin for long agentic coding and office tasks on a machine with about 96 GB of memory, if Nanbeige's own results hold up, with a 512K context and per-request thinking.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "Nanbeige4.3-33B (BF16)", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B", "who": "publisher", "precision": "bf16", "sizeGB": 66.26, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Whole repository, 66.24 GB of weights in 14 shards. 66.26 GB × 1.2 = 79.5 GB. The only build from Nanbeige; no Ollama, LM Studio or ggml-org build exists, and llama.cpp needs a GGUF converted with its own script or taken from someone else" } ], "run": "vllm serve Nanbeige/Nanbeige4.3-33B --tensor-parallel-size 4 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder", "runBuild": "Nanbeige4.3-33B (BF16)", "checked": "2026-10-25", "minMemoryGB": 96 }, "family": { "lab": "Nanbeige LLM Lab (BOSS Zhipin)", "family": "Nanbeige4.3", "url": "https://www.anchorterminal.com/open-weights/#nanbeige", "others": [ { "name": "Nanbeige4.3-33B-Base", "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base", "params": "33B" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed", "domain": "zhipin.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main", "securityTxt": "none", "checked": "2026-10-25", "notes": [ "The card names the Nanbeige Team and nothing else. BOSS Zhipin is identified as the maker by the lab's Nanbeige4.2-3B report on arXiv (Nanbeige LLM Lab, Boss Zhipin), which links huggingface.co/Nanbeige, and by the chat template's default system prompt naming BOSS Zhipin as developer and trainer. The card's contact address is on kanzhun.com. Kanzhun Limited as BOSS Zhipin's operator wasn't confirmed first-hand, since ir.zhipin.com refused our reader. The domain's registration date wasn't checked.", "The Hugging Face organisation Nanbeige (Nanbeige LLM Lab) isn't verified by the Hub. The arXiv report's link to it is the publisher's own documentation pointing there.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history.", "www.zhipin.com/.well-known/security.txt answers 404 and kanzhun.com's redirects to the home page. BOSS Zhipin runs a security response centre at security.zhipin.com for reporting flaws in its products; nothing links it to the model." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Nanbeige LLM Lab (BOSS Zhipin)", "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 (Hub metadata only; the repository has no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-10-24", "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 E4B IT scores 63.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Gemma 4 E4B IT or Nanbeige4.3-33B?" } ], "goodFor": [ { "aheadOn": [ "Reach, 95 against 55", "Risk, 70 against 60", "Documentation and upkeep, 75 against 56" ], "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-e4b-it", "watchFor": "No safety evaluation figures published" }, { "aheadOn": null, "also": null, "goodFor": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.", "slug": "nanbeige-4-3-33b", "watchFor": "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run" } ], "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-e4b-it.json", "title": "Agents-A1-4B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.json", "title": "Agents-A1-4B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.json", "title": "Apertus v1.5 8B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.json", "title": "Apodex-1.1-mini vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b" }, { "json": 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"https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.json", "title": "Cosmos-Reason2-2B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e4b-it.json", "title": "Darwin-36B-Opus vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.json", "title": "Darwin-36B-Opus vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it.json", "title": "DeepSeek-V3.2 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b.json", "title": "DeepSeek-V3.2 vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.json", "title": "DeepSeek V4.1 Flash vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b.json", "title": "DeepSeek V4.1 Flash vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it.json", "title": "DeepSeek-V4-Flash-0731 vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e4b-it" }, { "json": 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"MiniMax-M2.7 vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b.json", "title": "MiniMax-M3.1-Flash vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nanbeige-4-3-33b.json", "title": "Muse Glimmer 30B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Nanbeige4.3-33B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Nanbeige4.3-33B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b.json", "title": "Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-4b.json", "title": "Nanbeige4.3-33B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nanbeige4.3-33B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b.json", "title": "Nanbeige4.3-33B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Nanbeige4.3-33B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0.json", "title": "Nanbeige4.3-33B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-olmo-3-7b-think.json", "title": "Nanbeige4.3-33B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-35b-a3b.json", "title": "Nanbeige4.3-33B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-397b.json", "title": "Nanbeige4.3-33B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.json", "title": "Nanbeige4.3-33B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nanbeige4.3-33B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507.json", "title": "Nanbeige4.3-33B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b.json", "title": "Nanbeige4.3-33B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b.json", "title": "Nanbeige4.3-33B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b.json", "title": "Nanbeige4.3-33B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b.json", "title": "Nanbeige4.3-33B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next.json", "title": "Nanbeige4.3-33B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next.json", "title": "Nanbeige4.3-33B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json", "title": "Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint.json", "title": "Nanbeige4.3-33B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.json", "title": "Nanbeige4.3-33B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.json", "title": "Nanbeige4.3-33B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 95, "key": "reach", "name": "Reach", "nanbeige-4-3-33b": 55, "weight": 25 }, { "by": 10, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 70, "key": "risk", "name": "Risk", "nanbeige-4-3-33b": 60, "weight": 20 }, { "by": 0, "edge": "", "gemma-4-e4b-it": 85, "key": "interface", "name": "Agent interface", "nanbeige-4-3-33b": 85, "weight": 10 }, { "by": 19, "edge": "gemma-4-e4b-it", "gemma-4-e4b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "nanbeige-4-3-33b": 56, "weight": 10 } ], "summary": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "gemma-4-e4b-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 7 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.", "nanbeige-4-3-33b": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b", "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.min.md" }, "markdown": "Gemma 4 E4B IT scores 63.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Gemma 4 E4B IT: grade B, 63.2/100, rank #25 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json\n- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.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 3341 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Gemma 4 E4B IT (B)\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 60\n- Documentation and upkeep, 75 against 56\n\nWatch for: No safety evaluation figures published\n\n### Nanbeige4.3-33B (C)\n\nGood for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.\n\nWatch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run\n\n\n## Score by category\n\n| Category | Weight | Gemma 4 E4B IT | Nanbeige4.3-33B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 95 | 55 | Gemma 4 E4B IT +40 |\n| Risk | 20% (20 this run) | 70 | 60 | Gemma 4 E4B IT +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 75 | 56 | Gemma 4 E4B IT +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Gemma 4 E4B IT | Nanbeige4.3-33B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Nanbeige LLM Lab (BOSS Zhipin) |\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 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-07-20 | 2026-10-24 |\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 E4B 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 7 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**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.\n\n## Before you call either\n\n### Gemma 4 E4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio\n2. With Ollama run `ollama run gemma4:e4b` 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### Nanbeige4.3-33B\n\n1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers\n2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes\n3. Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends\n4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card\n5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed\n6. Send one system message at the start only, as the template raises an error for a later one\n7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed\n\n## Questions\n\n### Which is better for AI agents, Gemma 4 E4B IT or Nanbeige4.3-33B?\n\nGemma 4 E4B IT scores 63.2 (B) on agent readiness against Nanbeige4.3-33B's 61 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemma-4-e4b-it\", \"b\": \"nanbeige-4-3-33b\"}`. From a terminal: `anchor compare gemma-4-e4b-it nanbeige-4-3-33b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json\n\n## Other comparisons with Gemma 4 E4B IT or Nanbeige4.3-33B\n\n- [Agents-A1-4B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.md)\n- [Agents-A1-4B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.md)\n- [Apertus v1.5 8B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.md)\n- [Apertus v1.5 8B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.md)\n- [Apodex-1.1-mini vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e4b-it.md)\n- [Apodex-1.1-mini vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.md)\n- [BigBang-v1 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.md)\n- [BigBang-v1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.md)\n- [Carbon-X vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e4b-it.md)\n- [Carbon-X vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.md)\n- [Cosmos-Reason2-2B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-gemma-4-e4b-it.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.md)\n- [Darwin-36B-Opus vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e4b-it.md)\n- [Darwin-36B-Opus vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V3.2 vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-gemma-4-e4b-it.md)\n- 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IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 12B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 26B A4B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 26B A4B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 31B IT vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-gemma-4-e4b-it.md)\n- [Gemma 4 31B IT vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b.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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b.md)\n- [Gemma 4 E4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air.md)\n- [Gemma 4 E4B IT vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-7-flash.md)\n- [Gemma 4 E4B IT vs GLM-5.3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.md)\n- [Gemma 4 E4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E4B IT vs gpt-oss-120b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-120b.md)\n- [Gemma 4 E4B IT vs gpt-oss-20b](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-gpt-oss-20b.md)\n- [Gemma 4 E4B IT vs Granite 4.2 3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-granite-4-2-3b.md)\n- [Gemma 4 E4B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.md)\n- [Gemma 4 E4B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling.md)\n- [Gemma 4 E4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling-small.md)\n- [Gemma 4 E4B IT vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Gemma 4 E4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.md)\n- [Gemma 4 E4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1.md)\n- [Gemma 4 E4B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kronos.md)\n- [Gemma 4 E4B IT vs Laguna M.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-m-1.md)\n- [Gemma 4 E4B IT vs Laguna S 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-s-2-1.md)\n- [Gemma 4 E4B IT vs Laguna XS 2.1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-laguna-xs-2-1.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ling-3-0-tiny.md)\n- [Gemma 4 E4B IT vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Gemma 4 E4B IT vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm-o-4-5.md)\n- [Gemma 4 E4B IT vs MiniCPM5-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minicpm5-2b.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5.md)\n- [Gemma 4 E4B IT vs MiniMax-M2.7](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-7.md)\n- [Gemma 4 E4B IT vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m3-1-flash.md)\n- [Gemma 4 E4B IT vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-muse-glimmer-30b.md)\n- [Gemma 4 E4B IT vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Gemma 4 E4B IT vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-30b-a3b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-4b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 E4B IT vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-ultra-550b-a55b.md)\n- [Gemma 4 E4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.md)\n- [Gemma 4 E4B IT vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-olmo-3-7b-think.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E4B IT vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Gemma 4 E4B IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-4b-instruct-2507.md)\n- [Gemma 4 E4B IT vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-0-8b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-122b-a10b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-2b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-397b-a17b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-4b.md)\n- [Gemma 4 E4B IT vs Qwen3.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-5-9b.md)\n- [Gemma 4 E4B IT vs Qwen3.6-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-27b.md)\n- [Gemma 4 E4B IT vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-6-35b-a3b.md)\n- [Gemma 4 E4B IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-8-flash-next.md)\n- [Gemma 4 E4B IT vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-next.md)\n- [Gemma 4 E4B IT vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-2b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-32b-instruct.md)\n- [Gemma 4 E4B IT vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-vl-4b-instruct.md)\n- [Gemma 4 E4B IT vs 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"https://www.anchorterminal.com/compare/" }, { "name": "Gemma 4 E4B IT vs Nanbeige4.3-33B", "url": "" } ], "description": "Gemma 4 E4B IT scores 63.2 (B) to Nanbeige4.3-33B's 61 (C) for open-weight models you run yourself. 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