{ "data": { "a": { "slug": "apodex-1-1-mini", "name": "Apodex-1.1-mini", "vendor": "Apodex AI", "vendorUrl": "https://www.apodex.com", "kind": "weights", "category": "open-weights", "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini", "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json", "repo": "https://huggingface.co/apodex/Apodex-1.1-mini", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-FP8" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-NVFP4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GPTQ-Int4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (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. Apodex's hosted API bills by account (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "gptq", "self-hosted" ], "lastRelease": "2026-08-25", "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": 1197, "categoryRank": 29, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "strengths": [ "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls", "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE", "Apache 2.0 through the chain, with the base's licence file carried and no gate", "Apodex's own API serves the same model", "Safetensors and GGUF only, with checksums on every weight file" ], "weaknesses": [ "No safety results, and agent guidance only from the harness", "Apodex answered none of the four discussions", "The card states no parameters, architecture, limitations or knowledge cut-off", "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default", "Structured output isn't documented" ], "agentNotes": [ "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed", "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file", "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks", "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768", "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block", "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "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": 85, "risk": 65, "upkeep": 38 }, "provenanceScore": 40, "checked": { "categories": { "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": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n --tool-call-parser qwen3_coder --reasoning-parser qwen3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apodex-1-1-mini" }, "weights": { "trainedBy": "Apodex AI", "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Apodex-1.1-mini-NVFP4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 24.3, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang" }, { "name": "Apodex-1.1-mini-GPTQ-Int4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4", "who": "publisher", "precision": "int4", "sizeGB": 24.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "The card serves it with SGLang" }, { "name": "apodex1.1-35b-Q4_K_M-MTP", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server" }, { "name": "Apodex-1.1-mini-FP8", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Apodex-1.1-mini", "url": "https://huggingface.co/apodex/Apodex-1.1-mini", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3", "runBuild": "Apodex-1.1-mini", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Apodex AI (apodex)", "family": "Apodex-1", "url": "https://www.anchorterminal.com/open-weights/#apodex", "others": [ { "name": "Apodex-1.0-mini", "url": "https://huggingface.co/apodex/Apodex-1.0-mini", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-4B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-2B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-0.8B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT", "params": "0.8B (873,438,784 by the Hub's safetensors count)" } ], "unrestrictedCopies": 2 }, "area": "models", "provenance": { "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)", "domain": "apodex.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.", "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.", "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.", "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.", "unchecked: the domain's registration date." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json" }, "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 3 of 4 scored categories.", "b": { "slug": "gemma-4-12b-it", "name": "Gemma 4 12B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/gemma-4-12b-it", "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-12b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json", "repo": "https://huggingface.co/google/gemma-4-12B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/gemma-4-12B-it" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-q4_0-gguf" }, { "registry": "huggingface", "name": "google/gemma-4-12B-it-qat-w4a16-ct" }, { "registry": "ollama", "name": "gemma4:12b" } ], "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, 26.7 GB at BF16, 13.4 GB at SFP8 and 6.7 GB at Q4_0 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", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "qat", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 71.3, "grade": "BB", "agentReady": true, "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": 1197, "categoryRank": 3, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 50, "interface": 85, "reach": 95, "risk": 70, "upkeep": 75 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.", "bestFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "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 8.6 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", "262,144 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", "Files unchanged since 20 July 2026, and a startup crash report waits without a reply", "A newer, separate architecture (gemma4_unified) that needs Transformers 5.10.0 or later" ], "agentNotes": [ "On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio", "With Ollama run `ollama run gemma4:12b` 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", "Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "BB", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71.3 } ], "editorialScores": { "capability": 50, "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 gemma4:12b\n# or, with llama.cpp and Google's QAT build:\nllama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"gemma4:12b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/gemma-4-12b-it" }, "sameCompany": [ "embeddinggemma-300m", "diffusiongemma-26b-a4b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it" ], "weights": { "trainedBy": "Google DeepMind", "builtOn": "google/gemma-4-12B", "params": "11.95B", "context": 262144, "goodFor": "Mid-size dense assistant and agent model for a laptop GPU, with tool calling and optional thinking, taking text, images, video frames and audio without separate encoders, and a 256K context.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "gemma-4-12b-it-qat-q4_0", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf/blob/main/gemma-4-12b-it-qat-q4_0.gguf", "who": "publisher", "precision": "q4_0", "sizeGB": 7.15, "memoryGB": 12, "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 6.98 GB model and the 0.18 GB projector (mmproj-gemma-4-12b-it-qat-q4_0.gguf) for image and audio input" }, { "name": "ollama gemma4:12b", "url": "https://ollama.com/library/gemma4:12b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 8, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M weights (7.4 GB) with a BF16 projector (175 MB) and a Q8_0 draft model (465 MB) (digest 6114515d63c1), the llama.cpp variant. gemma4:12b-it-qat (7.2 GB) is Ollama's copy of Google's Q4_0 QAT build" }, { "name": "lmstudio google/gemma-4-12b", "url": "https://lmstudio.ai/models/google/gemma-4-12b", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 7.4, "memoryGB": 12, "format": "gguf", "runsOn": [ "LM Studio" ], "note": "LM Studio's catalogue build, from lmstudio-community/gemma-4-12B-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-4-12B-it-qat-w4a16-ct", "url": "https://huggingface.co/google/gemma-4-12B-it-qat-w4a16-ct", "who": "publisher", "precision": "w4a16", "sizeGB": 10.3, "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" }, { "name": "gemma-4-12B-it", "url": "https://huggingface.co/google/gemma-4-12B-it", "who": "publisher", "precision": "bf16", "sizeGB": 23.95, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072", "runBuild": "gemma-4-12b-it-qat-q4_0", "checked": "2026-10-21", "minMemoryGB": 12 }, "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-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-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 3 June 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-4-12b-it.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Apodex AI", "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": "2026-08-25", "b": "2026-07-20", "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": "3.3/5 (4)", "b": "3.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Apodex-1.1-mini or Gemma 4 12B IT?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "slug": "apodex-1-1-mini", "watchFor": "No safety results, and agent guidance only from the harness" }, { "aheadOn": [ "Reach, 95 against 85", "Risk, 70 against 65", "Documentation and upkeep, 75 against 38" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.8 against 3.3 out of 5" ], "goodFor": "Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context", "slug": "gemma-4-12b-it", "watchFor": "No safety evaluation figures published" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json", "title": "Agents-A1-4B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json", "title": "Agents-A1-4B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json", "title": "Apertus v1.5 8B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.json", "title": "Apodex-1.1-mini vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.json", "title": "Apodex-1.1-mini vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus.json", "title": "Apodex-1.1-mini vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2.json", "title": "Apodex-1.1-mini vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.json", "title": "Apodex-1.1-mini vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731.json", "title": "Apodex-1.1-mini vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it.json", "title": "Apodex-1.1-mini vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it.json", "title": "Apodex-1.1-mini vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-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": 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"https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b.json", "title": "Apodex-1.1-mini vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b.json", "title": "Apodex-1.1-mini vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.json", "title": "Apodex-1.1-mini vs Hy3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.json", "title": "Apodex-1.1-mini vs Inkling", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-small.json", "title": 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9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5.json", "title": "Gemma 4 12B IT vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b.json", "title": "Gemma 4 12B IT vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5.json", "title": "Gemma 4 12B IT vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.json", "title": "Gemma 4 12B IT vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b.json", "title": "Gemma 4 12B IT vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Gemma 4 12B IT vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Gemma 4 12B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b.json", "title": "Gemma 4 12B IT vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.json", "title": "Gemma 4 12B IT vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think.json", "title": "Gemma 4 12B IT vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Gemma 4 12B IT vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507.json", "title": "Gemma 4 12B IT vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b.json", "title": "Gemma 4 12B IT vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b.json", "title": "Gemma 4 12B IT vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b.json", "title": "Gemma 4 12B IT vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b.json", "title": "Gemma 4 12B IT vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b.json", "title": "Gemma 4 12B IT vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b.json", "title": "Gemma 4 12B IT vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b.json", "title": "Gemma 4 12B IT vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b.json", "title": "Gemma 4 12B IT vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next.json", "title": "Gemma 4 12B IT vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-2b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 12B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b.json", "title": "Gemma 4 12B IT vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.json", "title": "Gemma 4 12B IT vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Gemma 4 12B IT vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b.json", "title": "Gemma 4 12B IT vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apodex-1-1-mini": 85, "by": 10, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 95, "key": "reach", "name": "Reach", "weight": 25 }, { "apodex-1-1-mini": 65, "by": 5, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "apodex-1-1-mini": 85, "by": 0, "edge": "", "gemma-4-12b-it": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "apodex-1-1-mini": 38, "by": 37, "edge": "gemma-4-12b-it", "gemma-4-12b-it": 75, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "gemma-4-12b-it": "A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it", "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.min.md" }, "markdown": "Gemma 4 12B IT scores 71.3 (BB) on agent readiness against Apodex-1.1-mini's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #36 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\n- Gemma 4 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nWatch for: No safety results, and agent guidance only from the harness\n\n### Gemma 4 12B IT (BB)\n\nGood for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context\n\nAhead on:\n- Reach, 95 against 85\n- Risk, 70 against 65\n- Documentation and upkeep, 75 against 38\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.8 against 3.3 out of 5\n\nWatch for: No safety evaluation figures published\n\n\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Gemma 4 12B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 95 | Gemma 4 12B IT +10 |\n| Risk | 20% (20 this run) | 65 | 70 | Gemma 4 12B IT +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 38 | 75 | Gemma 4 12B IT +37 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **71.3 · BB** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Gemma 4 12B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | 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 | 2026-08-25 | 2026-07-20 |\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 | 3.3/5 (4) | 3.8/5 (4) |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\n\n**Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.\n\n## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\n\n### Gemma 4 12B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio\n2. With Ollama run `ollama run gemma4:12b` 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\n6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama\n\n## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Gemma 4 12B IT?\n\nGemma 4 12B IT scores 71.3 (BB) on agent readiness against Apodex-1.1-mini'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/apodex-1-1-mini-vs-gemma-4-12b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-12b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"gemma-4-12b-it\"}`. From a terminal: `anchor compare apodex-1-1-mini gemma-4-12b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json\n\n## Other comparisons with Apodex-1.1-mini or Gemma 4 12B IT\n\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.md)\n- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.md)\n- [Apodex-1.1-mini vs BigBang-v1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1.md)\n- [Apodex-1.1-mini vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cosmos-reason2-2b.md)\n- [Apodex-1.1-mini vs 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