{ "data": { "a": { "slug": "diffusiongemma-26b-a4b-it", "name": "DiffusionGemma 26B A4B IT", "vendor": "Google DeepMind", "vendorUrl": "https://gemma.ai", "kind": "weights", "category": "open-weights", "summary": "DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it", "markdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json", "repo": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "google/diffusiongemma-26B-A4B-it" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). Google also lists the model on Kaggle and in Vertex AI Model Garden.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google sells no per-call access to these weights through the Gemini API, which serves only gemma-4-31b-it and gemma-4-26b-a4b-it among Gemma models. Running them costs the hardware, 51.7 GB of BF16 safetensors; Google's inference guide asks for a GPU with more than 60 GB of memory (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/diffusiongemma.md", "llmsTxt": "https://gemma.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "diffusion", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted", "experimental" ], "lastRelease": "2026-07-15", "graded": true, "anchor": { "graded": true, "score": 50, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 74, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 30, "interface": 70, "reach": 50, "risk": 70, "upkeep": 60 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.", "bestFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4", "strengths": [ "Apache 2.0 with nothing added, ungated, safetensors only, hashed in Google's own organisation", "Generates 256-token blocks in parallel, which Google says reaches over 1,100 tokens per second per user on an H100 in FP8 (the publisher's claim)", "Gemma 4's chat template with multi-call tool use, thinking on or off per request and a 256K context", "Runs natively in released Transformers (5.11.0 and later) and SGLang (v0.5.22), with no repository code", "The card covers intended use, limitations, architecture, parameters, context, languages and a January 2025 cut-off" ], "weaknesses": [ "No quantised build from Google, so the smallest official build needs the 64 GB band", "Not in vLLM, llama.cpp, Ollama or LM Studio", "Google's own benchmarks put it below Gemma 4 26B A4B on most tasks, τ²-bench 56.2% against 68.2%", "Tool calling isn't documented for this model, and SGLang rejects JSON Schema and grammar constraints for it", "Labelled experimental, with no hosted API and no safety figures" ], "agentNotes": [ "Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions", "In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler", "Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn", "Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself", "Streaming arrives one 256-token canvas at a time, not token by token", "Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50 } ], "editorialScores": { "capability": 30, "interface": 70, "reach": 50, "risk": 70, "upkeep": 60 }, "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": "pip install \"transformers\u003e=5.11.0\" torch accelerate\npython -c \"from transformers import DiffusionGemmaForBlockDiffusion, AutoProcessor; m='google/diffusiongemma-26B-A4B-it'; p=AutoProcessor.from_pretrained(m); model=DiffusionGemmaForBlockDiffusion.from_pretrained(m, dtype='auto', device_map='auto')\"\n# or, with SGLang v0.5.22 or later:\nsglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"google/diffusiongemma-26B-A4B-it\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"max_tokens\": 1024}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/diffusiongemma-26b-a4b-it" }, "sameCompany": [ "embeddinggemma-300m", "gemma-4-12b-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-4-26B-A4B (architecture, per the card; no base_model declared)", "params": "25.2B", "active": "3.8B", "context": 262144, "goodFor": "Fast local text generation from text, image and video prompts at low concurrency, with optional thinking and tool calls, trading some quality against Gemma 4 26B A4B for speed.", "runtimes": [ "SGLang", "Transformers" ], "builds": [ { "name": "diffusiongemma-26B-A4B-it", "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it", "who": "publisher", "precision": "bf16", "sizeGB": 51.7, "memoryGB": 64, "format": "safetensors", "runsOn": [ "Transformers", "SGLang" ], "note": "The only build from Google. Google's guide asks for more than 60 GB of GPU memory" } ], "run": "sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code --port 30000", "runBuild": "diffusiongemma-26B-A4B-it", "checked": "2026-10-21", "minMemoryGB": 64 }, "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", "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": "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; the card names Google DeepMind as author)", "domain": "gemma.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/google/diffusiongemma-26B-A4B-it/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The registrant and registration date of gemma.ai are carried from the gemma-4-26b-a4b-it facts checked on 21 October 2026. gemma.ai links to huggingface.co/collections/google/diffusiongemma, which holds only this repository.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "gemma.ai's dated releases page has no DiffusionGemma entry. The Hugging Face commit history has four commits from 10 June to 15 July 2026 whose messages say what changed (two chat template fixes and the diffusers files).", "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/diffusiongemma-26b-a4b-it.json" }, "answer": "Inkling-Small scores 66.5 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories.", "b": { "slug": "inkling-small", "name": "Inkling-Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling-Small is Thinking Machines Lab's open-weight mixture-of-experts model, 276B total and 12B active parameters, taking text, images and audio and returning text, with adjustable thinking effort, published in BF16 and NVFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling-small", "markdownUrl": "https://www.anchorterminal.com/tools/inkling-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-small.json", "repo": "https://huggingface.co/thinkingmachines/Inkling-Small", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 531.9 GB for the BF16 files and 170.8 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens and $1.20 per million output 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 (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-small/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 66.5, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.", "bestFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "strengths": [ "Thinking Machines answered 8 of the 9 questions in its discussions", "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request from none to the maximum, several tool calls a turn and a 1M-token context", "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers", "A training-content summary with sources, mix and a July 2026 collection cut-off", "Thinking Machines' own Tinker hosts it as beta serverless inference" ], "weaknesses": [ "The smallest build Thinking Machines publishes needs about 205 GB", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "No release of ggml-org's llama.cpp, Ollama or LM Studio runs it", "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance", "Structured output isn't documented, and the card gives no dated knowledge cut-off" ], "agentNotes": [ "Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4", "Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh", "Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card", "Use guided decoding or validate JSON yourself, since structured output isn't documented", "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.5 } ], "editorialScores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "provenanceScore": 67, "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": "# per SGLang's cookbook, NVFP4 on 8 Blackwell GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 \\\n --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-Small-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling-small" }, "sameCompany": [ "inkling" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "276B", "active": "12B", "context": 1048576, "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images and audio in, and thinking effort set per request.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Inkling-Small-NVFP4", "url": "https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 170.8, "memoryGB": 256, "format": "safetensors", "note": "NVFP4 experts with FP8 and BF16 parts. Thinking Machines says W4A4 needs NVIDIA SM100 or newer, and W4A16 runs on H200" }, { "name": "Inkling-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-Small", "who": "publisher", "precision": "bf16", "sizeGB": 531.9, "memoryGB": 1024, "format": "safetensors" } ], "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "runBuild": "Inkling-Small-NVFP4", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines", "others": [ { "name": "Inkling", "url": "https://huggingface.co/thinkingmachines/Inkling", "params": "952.38B (Hub safetensors count; the name states none)", "listing": "https://www.anchorterminal.com/tools/inkling" } ], "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "thinkingmachines.ai's registration date wasn't read on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling-Small/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Thinking Machines' model card page and launch post link the thinkingmachines organisation on Hugging Face, which isn't verified by Hugging Face.", "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 12 commits from 27 to 31 July 2026, an upload, card updates and community evaluation results.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, and the site publishes a coordinated vulnerability disclosure policy that leaves model safety to a separate incident contact." ], "score": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-small.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Google DeepMind", "b": "Thinking Machines Lab", "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": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-07-15", "b": "2026-07-31", "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": "2.5/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling-Small scores 66.5 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories.", "question": "Which is better for AI agents, DiffusionGemma 26B A4B IT or Inkling-Small?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4", "slug": "diffusiongemma-26b-a4b-it", "watchFor": "No quantised build from Google, so the smallest official build needs the 64 GB band" }, { "aheadOn": [ "Capability, 63 against 30", "Reach, 55 against 50", "Agent interface, 85 against 70", "Documentation and upkeep, 82 against 60" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.5 out of 5" ], "goodFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "slug": "inkling-small", "watchFor": "The smallest build Thinking Machines publishes needs about 205 GB" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.json", "title": "Agents-A1-4B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.json", "title": "Agents-A1-4B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json", "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json", "title": "Apertus v1.5 8B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small" }, { "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-inkling-small.json", "title": "Apodex-1.1-mini vs Inkling-Small", "url": 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Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e4b-it.json", "title": "DiffusionGemma 26B A4B IT vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.json", "title": "DiffusionGemma 26B A4B IT vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash.json", "title": "DiffusionGemma 26B A4B IT vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3.json", "title": "DiffusionGemma 26B A4B IT vs GLM-5.3", 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"https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.json", "title": "Inkling-Small vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.json", "title": "Inkling-Small vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.json", "title": "Inkling-Small vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling-Small vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-inkling-small.json", "title": "Inkling vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/inkling-vs-inkling-small" } ], "scores": [ { "by": 33, "diffusiongemma-26b-a4b-it": 30, "edge": "inkling-small", "inkling-small": 63, "key": "capability", "name": "Capability", "weight": 35 }, { "by": 5, "diffusiongemma-26b-a4b-it": 50, "edge": "inkling-small", "inkling-small": 55, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 0, "diffusiongemma-26b-a4b-it": 70, "edge": "", "inkling-small": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 15, "diffusiongemma-26b-a4b-it": 70, "edge": "inkling-small", "inkling-small": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 22, "diffusiongemma-26b-a4b-it": 60, "edge": "inkling-small", "inkling-small": 82, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Inkling-Small scores 66.5 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "diffusiongemma-26b-a4b-it": "A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.", "inkling-small": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small", "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.min.md" }, "markdown": "Inkling-Small scores 66.5 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories. Both do open-weight models you run yourself.\n\n- DiffusionGemma 26B A4B IT: grade D, 50/100, rank #94 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json\n- Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.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### DiffusionGemma 26B A4B IT (D)\n\nGood for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4\n\nWatch for: No quantised build from Google, so the smallest official build needs the 64 GB band\n\n### Inkling-Small (B)\n\nGood for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.\n\nAhead on:\n- Capability, 63 against 30\n- Reach, 55 against 50\n- Agent interface, 85 against 70\n- Documentation and upkeep, 82 against 60\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5\n\nWatch for: The smallest build Thinking Machines publishes needs about 205 GB\n\n\n## Score by category\n\n| Category | Weight | DiffusionGemma 26B A4B IT | Inkling-Small | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 30 | 63 | Inkling-Small +33 |\n| Reach | 25% (25 this run) | 50 | 55 | Inkling-Small +5 |\n| Risk | 20% (20 this run) | 70 | 70 | even |\n| Agent interface | 10% (10 this run) | 70 | 85 | Inkling-Small +15 |\n| Documentation and upkeep | 10% (10 this run) | 60 | 82 | Inkling-Small +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50 · D** | **66.5 · B** | |\n\n## Facts side by side\n\n| Fact | DiffusionGemma 26B A4B IT | Inkling-Small |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Google DeepMind | Thinking Machines Lab |\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 | yes | no |\n| Last release | 2026-07-15 | 2026-07-31 |\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 | 2.5/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**DiffusionGemma 26B A4B IT.** A fast local generation cousin for low-concurrency work on text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. It needs about 62 GB for Google's BF16 files and runs only in Transformers or SGLang. The main risk is quality, since Google's own figures put it below Gemma 4 26B A4B, and it's labelled experimental.\n\n**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.\n\n## Before you call either\n\n### DiffusionGemma 26B A4B IT\n\n1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions\n2. In Transformers, install `transformers\u003e=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype=\"auto\"`; generation_config.json already holds the recommended sampler\n3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `\u003c|think|\u003e` at the start of the system prompt, read the reasoning between `\u003c|channel\u003ethought` and `\u003cchannel|\u003e`, and drop it from history before the next turn\n4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself\n5. Streaming arrives one 256-token canvas at a time, not token by token\n6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs\n\n### Inkling-Small\n\n1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4\n2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself\n3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh\n4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card\n7. Use guided decoding or validate JSON yourself, since structured output isn't documented\n8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n## Questions\n\n### Which is better for AI agents, DiffusionGemma 26B A4B IT or Inkling-Small?\n\nInkling-Small scores 66.5 (B) on agent readiness against DiffusionGemma 26B A4B IT's 50 (D), and leads in 4 of 5 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"diffusiongemma-26b-a4b-it\", \"b\": \"inkling-small\"}`. From a terminal: `anchor compare diffusiongemma-26b-a4b-it inkling-small`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/inkling-small.json\n\n## Other comparisons with DiffusionGemma 26B A4B IT or Inkling-Small\n\n- [Agents-A1-4B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.md)\n- [Agents-A1-4B vs Inkling-Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.md)\n- [Apertus v1.5 8B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.md)\n- [Apertus v1.5 8B vs Inkling-Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.md)\n- [Apodex-1.1-mini vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it.md)\n- [Apodex-1.1-mini vs 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Inkling-Small](https://www.anchorterminal.com/compare/inkling-vs-inkling-small.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DiffusionGemma 26B A4B IT vs Inkling-Small", "url": "" } ], "description": "Inkling-Small scores 66.5 (B) to 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