{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 1518, "categoryRank": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "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 repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.", "b": { "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", "dns": "gemma-4-e2b-it.grade.anchorterminal.com", "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": "2026-11-03", "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": 1518, "categoryRank": 70, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 19, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-04" }, "negative": 0, "verdict": "A small open-weight model for light agent work, tool calls, short audio and images 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, and no safety results are published.", "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": "0.7", "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": 72, "checked": { "categories": { "capability": "2026-11-04", "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "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-2", "embeddinggemma-300m", "shieldgemma", "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", "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2" } ], "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": 72 }, "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": "2026-11-10T16:08:36.94110613Z" }, "llmsTxt": { "url": "https://gemma.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:43:14.713838946Z" }, "domain": { "domain": "gemma.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai", "checkedAt": "2026-11-08T15:00:32.790630815Z" }, "updatedAt": "2026-11-10T16:08:36.94110613Z" } }, "edges": [ { "by": 45, "category": "reach", "leads": "gemma-4-e2b-it", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one." }, "scores": { "a": 50, "b": 95 } }, { "by": 25, "category": "interface", "leads": "gemma-4-e2b-it", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line." }, "scores": { "a": 60, "b": 85 } }, { "by": 15, "category": "risk", "leads": "gemma-4-e2b-it", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found." }, "scores": { "a": 55, "b": 70 } }, { "by": 12, "category": "upkeep", "leads": "gemma-4-e2b-it", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.", "b": "Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply." }, "scores": { "a": 63, "b": 75 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "Google DeepMind", "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-11-03", "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": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.", "question": "Which is better for AI agents, Arete-27B or Gemma 4 E2B IT?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Reach, 95 against 50", "Risk, 70 against 55", "Agent interface, 85 against 60", "Documentation and upkeep, 75 against 63" ], "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" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "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/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-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": 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vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-4b.json", "title": "Gemma 4 E2B IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 E2B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b.json", "title": "Gemma 4 E2B IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 E2B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-27b.json", "title": "Gemma 4 E2B IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next.json", "title": "Gemma 4 E2B IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 E2B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 E2B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-next" }, { "json": 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"https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct.json", "title": "Gemma 4 E2B IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct.json", "title": "Gemma 4 E2B IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 E2B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint.json", "title": "Gemma 4 E2B IT vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 E2B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview.json", "title": "Gemma 4 E2B IT vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b.json", "title": "Gemma 4 E2B IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.json", "title": "Gemma 4 E2B IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash.json", "title": "Gemma 4 E2B IT vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.json", "title": "Gemma 4 E2B IT vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 E2B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b.json", "title": "Gemma 4 E2B IT vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b.json", "title": "Gemma 4 E2B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 45, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 15, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 25, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 12, "edge": "gemma-4-e2b-it", "gemma-4-e2b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "gemma-4-e2b-it": "A small open-weight model for light agent work, tool calls, short audio and images 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, and no safety results are published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.min.md" }, "markdown": "Gemma 4 E2B IT scores 60.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\n- Gemma 4 E2B IT: grade C, 60.4/100, rank #88 of 160 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- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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 50\n- Risk, 70 against 55\n- Agent interface, 85 against 60\n- Documentation and upkeep, 75 against 63\n\nWatch for: No safety evaluation figures published\n\n\n## Why the edge\n\n### Reach: Gemma 4 E2B IT +45\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n- Gemma 4 E2B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 4.34 GB, which with a fifth on top is 5.2 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build and two mobile builds, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.\n\n### Agent interface: Gemma 4 E2B IT +25\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- Gemma 4 E2B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, and ships a response schema for Transformers' parser. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 131,072 tokens native, which meets the 128K line.\n\n### Risk: Gemma 4 E2B IT +15\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- Gemma 4 E2B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.\n\n### Documentation and upkeep: Gemma 4 E2B IT +12\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\n- Gemma 4 E2B IT (75): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 233 glitch tokens in tokenizer.json on 3 November 2026, a day before 4 November. Responsiveness 10, Google replied to 9 of 24 questions and reports, the last on 2026-09-15, re-counted on 4 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.\n\n## Score by category\n\n| Category | Weight | Arete-27B | Gemma 4 E2B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 95 | Gemma 4 E2B IT +45 |\n| Risk | 20% (20 this run) | 55 | 70 | Gemma 4 E2B IT +15 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Gemma 4 E2B IT +25 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 75 | Gemma 4 E2B IT +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **60.4 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Gemma 4 E2B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Google DeepMind |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-11-06 | 2026-11-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.5/5 (4) |\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\n\n**Gemma 4 E2B IT.** A small open-weight model for light agent work, tool calls, short audio and images 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, and no safety results are published.\n\n## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or Gemma 4 E2B IT?\n\nGemma 4 E2B IT scores 60.4 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"gemma-4-e2b-it\"}`. From a terminal: `anchor compare arete-27b gemma-4-e2b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-e2b-it.json\n\n## Other comparisons with Arete-27B or Gemma 4 E2B IT\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-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- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-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- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs Carbon-X](https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.md)\n- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.md)\n- [Arete-27B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.md)\n- [Arete-27B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.md)\n- [Arete-27B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.md)\n- [Arete-27B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.md)\n- [Arete-27B vs Devstral Small 2](https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md)\n- [Arete-27B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.md)\n- [Arete-27B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.md)\n- [Arete-27B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.md)\n- [Arete-27B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.md)\n- [Arete-27B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.md)\n- [Arete-27B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.md)\n- [Arete-27B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.md)\n- [Arete-27B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.md)\n- [Arete-27B vs GLM-5.3](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.md)\n- [Arete-27B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.md)\n- [Arete-27B vs gpt-oss-120b](https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.md)\n- [Arete-27B vs gpt-oss-20b](https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.md)\n- [Arete-27B vs Granite 4.2 30B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.md)\n- [Arete-27B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.md)\n- [Arete-27B vs Granite 4.2 8B](https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs Hy3](https://www.anchorterminal.com/compare/arete-27b-vs-hy3.md)\n- [Arete-27B vs Inkling](https://www.anchorterminal.com/compare/arete-27b-vs-inkling.md)\n- [Arete-27B vs Inkling-Small](https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small.md)\n- [Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Arete-27B vs Kimi K3](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.md)\n- [Arete-27B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.md)\n- [Arete-27B vs Kolibri-1](https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.md)\n- [Arete-27B vs Kronos](https://www.anchorterminal.com/compare/arete-27b-vs-kronos.md)\n- [Arete-27B vs Laguna M.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.md)\n- [Arete-27B vs Laguna S 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.md)\n- [Arete-27B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.md)\n- [Arete-27B vs LBS-27B](https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.md)\n- [Arete-27B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.md)\n- [Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.md)\n- [Arete-27B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.md)\n- [Arete-27B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Arete-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Arete-27B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm5-2b.md)\n- [Arete-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.md)\n- [Arete-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.md)\n- [Arete-27B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m3-1-flash.md)\n- [Arete-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.md)\n- [Arete-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.md)\n- [Arete-27B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.md)\n- [Arete-27B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-3-33b.md)\n- [Arete-27B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Arete-27B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-4b.md)\n- [Arete-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Arete-27B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b.md)\n- [Arete-27B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Arete-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [Arete-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.md)\n- [Arete-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.md)\n- [Arete-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b.md)\n- [Arete-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.md)\n- [Arete-27B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.md)\n- [Arete-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md)\n- [Arete-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Arete-27B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-4b-instruct-2507.md)\n- [Arete-27B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-0-8b.md)\n- [Arete-27B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-122b-a10b.md)\n- [Arete-27B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-2b.md)\n- [Arete-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b.md)\n- [Arete-27B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-4b.md)\n- [Arete-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.md)\n- [Arete-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b.md)\n- [Arete-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-35b-a3b.md)\n- [Arete-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-27b.md)\n- [Arete-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.md)\n- [Arete-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-next.md)\n- [Arete-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-2b-instruct.md)\n- [Arete-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)\n- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)\n- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)\n- [Arete-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.md)\n- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)\n- [Arete-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.md)\n- [Arete-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.md)\n- [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md)\n- [Arete-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.md)\n- [Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)\n- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)\n- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)\n- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)\n- [Arete-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.md)\n- [BigBang-v1 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.md)\n- [Carbon-X vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/carbon-x-vs-gemma-4-e2b-it.md)\n- [Cola2 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/cola2-vs-gemma-4-e2b-it.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- [Darwin-36B-Opus vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-gemma-4-e2b-it.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 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-Flash-0731 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-gemma-4-e2b-it.md)\n- [Devstral Small 2 vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-gemma-4-e2b-it.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- [EXAONE 4.5 33B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-gemma-4-e2b-it.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 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 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 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 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"Arete-27B vs Gemma 4 E2B IT", "url": "" } ], "description": "Gemma 4 E2B IT scores 60.4 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. 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