{ "data": { "a": { "slug": "inkling", "name": "Inkling", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling is Thinking Machines Lab's large open-weight mixture-of-experts model, 975B total and 41B 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", "markdownUrl": "https://www.anchorterminal.com/tools/inkling.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling.json", "dns": "inkling.grade.anchorterminal.com", "repo": "https://huggingface.co/thinkingmachines/Inkling", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-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, 1,904.8 GB for the BF16 files and 592.0 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $1.00 per million input tokens and $4.05 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": "2026-11-02" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-23", "graded": true, "anchor": { "graded": true, "score": 64.7, "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": 1518, "categoryRank": 14, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 60, "interface": 85, "reach": 50, "risk": 70, "upkeep": 87 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.", "bestFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.", "strengths": [ "Three inference providers and Thinking Machines' own Tinker host it today", "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request, several tool calls a turn and a 1M-token context", "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers", "Thinking Machines answered 8 of the 12 questions and reports in its discussions", "A training-content summary with sources, mix and a July 2026 collection cut-off" ], "weaknesses": [ "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation", "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": [ "Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it", "On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`", "Use the inkling tool parser, not Hermes, and the inkling reasoning parser", "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 list xhigh", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card", "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, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.7 } ], "editorialScores": { "capability": 60, "interface": 85, "reach": 50, "risk": 70, "upkeep": 87 }, "provenanceScore": 71, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per SGLang's cookbook, NVFP4 on 8 B200 GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 \\\n --quantization modelopt_fp4 --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-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" }, "sameCompany": [ "inkling-2-small", "inkling-small" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "975B", "active": "41B", "context": 1048576, "goodFor": "Large mixture-of-experts generalist for coding, agents, factual recall and audio, with images in and thinking effort set per request.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Inkling-NVFP4", "url": "https://huggingface.co/thinkingmachines/Inkling-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 592, "memoryGB": 1024, "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", "url": "https://huggingface.co/thinkingmachines/Inkling", "who": "publisher", "precision": "bf16", "sizeGB": 1904.8, "memoryGB": 4096, "format": "safetensors", "note": "1,904.8 GB × 1.2 = 2,285.7 GB, so the 4,096 GB band. Thinking Machines states at least 2 TB of GPU memory" } ], "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "runBuild": "Inkling-NVFP4", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines", "others": [ { "name": "Inkling-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-Small", "params": "265.96B (Hub safetensors count; the name states none)", "listing": "https://www.anchorterminal.com/tools/inkling-small" } ], "unrestrictedCopies": 1 }, "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/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Thinking Machines' Inkling page links the thinkingmachines collection on Hugging Face, and its model card page links the repository. Hugging Face hasn't verified the organisation.", "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, 13 commits from 15 to 23 July 2026, the upload, a Transformers processor and template change on 15 July, 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": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling.json", "live": { "slug": "inkling", "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-11-10T16:08:06.712462129Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-11-08T15:05:10.558189316Z" }, "pages": [ { "url": "https://huggingface.co/thinkingmachines/Inkling/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:50.70345348Z", "changedAt": "2026-11-10T16:23:50.70345348Z", "fingerprint": "60baeb3c18bc" } ], "updatedAt": "2026-11-10T16:23:50.70345348Z" } }, "answer": "Inkling scores 64.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach.", "b": { "slug": "ornith-1-5-122b", "name": "Ornith-1.5-122B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json", "dns": "ornith-1-5-122b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit" }, { "registry": "ollama", "name": "ornith-1.5:122b-q4_K_M" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (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/ornith-ai/Ornith-1.5-122B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-24", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 64, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 75, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.", "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required", "strengths": [ "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M", "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no gate and no code in the repository" ], "weaknesses": [ "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published", "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`", "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends", "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and 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": 61 } ], "editorialScores": { "interface": 85, "reach": 75, "risk": 50, "upkeep": 36 }, "provenanceScore": 19, "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": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-122b" }, "sameCompany": [ "ornith-1-5-35b-a3b", "ornith-1-5-397b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it", "params": "125B", "context": 262144, "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-122B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 77.7, "memoryGB": 96, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-122B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 75.8, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input" }, { "name": "Ornith-1.5-122B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 69.7, "memoryGB": 96, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "ollama ornith-1.5:122b-q4_K_M", "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 77, "memoryGB": 96, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build" }, { "name": "Ornith-1.5-122B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 102.4, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-122B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 88.7, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-122B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 133, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 132.6, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 130.7, "memoryGB": 192, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "who": "publisher", "precision": "bf16", "sizeGB": 250.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 249.4, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-122B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 245.2, "memoryGB": 512, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:122b-q4_K_M", "runBuild": "ollama ornith-1.5:122b-q4_K_M", "checked": "2026-11-10", "minMemoryGB": 96 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "params": "397B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ] }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. The commit messages are uploads, deletions and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json" }, "edges": [ { "by": 51, "category": "upkeep", "leads": "inkling", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the weights files last changed on 23 July 2026, and Thinking Machines' Inkling renderer in tinker-cookbook changed on 30 October 2026, 3 days ago. Responsiveness 20, Thinking Machines answered 8 of the 12 questions and reports in its discussions. Version policy 5, a first release. File health 10, the files agree and the reports were answered.", "b": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36." }, "scores": { "a": 87, "b": 36 } }, { "by": 25, "category": "reach", "leads": "ornith-1-5-122b", "name": "Reach", "reasons": { "a": "Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Together AI, Fireworks and DeepInfra are live on Hugging Face, and Thinking Machines' Tinker serves it too.", "b": "Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75." }, "scores": { "a": 50, "b": 75 } }, { "by": 20, "category": "risk", "leads": "inkling", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and commissioned external testers who published nothing. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.", "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact." }, "scores": { "a": 70, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Thinking Machines Lab", "b": "Ornith", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-23", "b": "2026-10-24", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling scores 64.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach.", "question": "Which is better for AI agents, Inkling or Ornith-1.5-122B?" } ], "goodFor": [ { "aheadOn": [ "Risk, 70 against 50", "Documentation and upkeep, 87 against 36" ], "also": null, "goodFor": "Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.", "slug": "inkling", "watchFor": "The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation" }, { "aheadOn": [ "Reach, 75 against 50" ], "also": null, "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-122b", "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on" } ], "history": { "inkling": [ { "causes": [ "product" ], "date": "2026-11-02", "delta": 0.5, "from": 64.2, "fromGrade": "B", "to": 64.7, "toGrade": "B" } ], "ornith-1-5-122b": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json", "title": "Agents-A1-4B vs Inkling", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.json", "title": "Agents-A1-4B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.json", "title": "Apertus v1.5 8B vs Inkling", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b" }, { "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-ornith-1-5-122b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling.json", "title": "Arete-27B vs Inkling", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.json", "title": "Arete-27B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.json", "title": "BigBang-v1 vs Inkling", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.json", "title": "BigBang-v1 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling.json", "title": "Carbon-X vs Inkling", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b.json", "title": "Carbon-X vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-inkling.json", "title": "Cola2 vs Inkling", "url": "https://www.anchorterminal.com/compare/cola2-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json", "title": "Cola2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling.json", "title": "Cosmos-Reason2-2B vs Inkling", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling.json", "title": "Darwin-36B-Opus vs Inkling", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling.json", "title": "DeepSeek-V3.2 vs Inkling", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.json", "title": "DeepSeek V4.1 Flash vs Inkling", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling.json", "title": "DeepSeek-V4-Flash-0731 vs Inkling", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b.json", "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-inkling.json", "title": "Devstral Small 2 vs Inkling", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b.json", "title": "Devstral Small 2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling.json", "title": "DiffusionGemma 26B A4B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b.json", "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-inkling.json", "title": "EXAONE 4.5 33B vs Inkling", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling.json", "title": "Gemma 4 12B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.json", "title": "Gemma 4 26B A4B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-122b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.json", "title": "Gemma 4 31B IT vs Inkling", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-122b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-122b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-audio-2b.json", "title": "Inkling vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-omni-2b.json", "title": "Inkling vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.json", "title": "Inkling vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.json", "title": "Inkling vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-1-flash-1019.json", "title": "Inkling vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/inkling-vs-ling-3-1-flash-1019" }, { "json": 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"url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-122B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-122B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-122B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-122B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-122B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json", "title": "Ornith-1.5-122B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-122B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json", "title": "Ornith-1.5-122B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json", "title": "Ornith-1.5-122B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-122B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json", "title": "Ornith-1.5-122B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-122B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.json", "title": "Ornith-1.5-122B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "ornith-1-5-122b", "inkling": 50, "key": "reach", "name": "Reach", "ornith-1-5-122b": 75, "weight": 25 }, { "by": 20, "edge": "inkling", "inkling": 70, "key": "risk", "name": "Risk", "ornith-1-5-122b": 50, "weight": 20 }, { "by": 0, "edge": "", "inkling": 85, "key": "interface", "name": "Agent interface", "ornith-1-5-122b": 85, "weight": 10 }, { "by": 51, "edge": "inkling", "inkling": 87, "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-122b": 36, "weight": 10 } ], "summary": "Inkling scores 64.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "inkling": "Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.", "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b", "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.min.md" }, "markdown": "Inkling scores 64.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach. Both do open-weight models you run yourself.\n\n- Inkling: grade B, 64.7/100, rank #19 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling.json\n- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.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### Inkling (B)\n\nGood for: Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves.\n\nAhead on:\n- Risk, 70 against 50\n- Documentation and upkeep, 87 against 36\n\nWatch for: The smallest build Thinking Machines publishes needs about 710 GB, beyond any single workstation\n\n### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 75 against 50\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Documentation and upkeep: Inkling +51\n\n- Inkling (87): Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the weights files last changed on 23 July 2026, and Thinking Machines' Inkling renderer in tinker-cookbook changed on 30 October 2026, 3 days ago. Responsiveness 20, Thinking Machines answered 8 of the 12 questions and reports in its discussions. Version policy 5, a first release. File health 10, the files agree and the reports were answered.\n- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n\n### Reach: Ornith-1.5-122B +25\n\n- Inkling (50): Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Together AI, Fireworks and DeepInfra are live on Hugging Face, and Thinking Machines' Tinker serves it too.\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n\n### Risk: Inkling +20\n\n- Inkling (70): File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and commissioned external testers who published nothing. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n## Score by category\n\n| Category | Weight | Inkling | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 75 | Ornith-1.5-122B +25 |\n| Risk | 20% (20 this run) | 70 | 50 | Inkling +20 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 87 | 36 | Inkling +51 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.7 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Inkling | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | Ornith |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-23 | 2026-10-24 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Inkling\n\n- +0.5 on 2 November 2026: product\n\n### Ornith-1.5-122B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Inkling.** Thinking Machines' large multimodal generalist, stronger than Inkling-Small on factual recall by its own numbers. It needs about 710 GB of datacentre GPU memory even in NVFP4, so most agents will use a host such as Together AI or Tinker. The main risks are a use policy that binds on download and can change, and no outside safety results.\n\n**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\n\n## Before you call either\n\n### Inkling\n\n1. Unless you have a multi-GPU Blackwell or H200 node, call it through Together AI, Fireworks, DeepInfra or Tinker rather than starting it\n2. On 8 B200 GPUs, SGLang's recipe is `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-NVFP4 --tp 8 --quantization modelopt_fp4 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`\n3. Use the inkling tool parser, not Hermes, and the inkling reasoning parser\n4. 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 list xhigh\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Audio should be 16 kHz WAV, ideally under 20 minutes, and images 40 to 4096 px a side, per the card\n7. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Inkling or Ornith-1.5-122B?\n\nInkling scores 64.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling\", \"b\": \"ornith-1-5-122b\"}`. From a terminal: `anchor compare inkling ornith-1-5-122b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n\n## Other comparisons with Inkling or Ornith-1.5-122B\n\n- [Agents-A1-4B vs Inkling](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.md)\n- [Agents-A1-4B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.md)\n- [Apertus v1.5 8B vs Inkling](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.md)\n- [Apertus v1.5 8B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.md)\n- [Apodex-1.1-mini vs Inkling](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling.md)\n- [Apodex-1.1-mini vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b.md)\n- [Arete-27B vs Inkling](https://www.anchorterminal.com/compare/arete-27b-vs-inkling.md)\n- [Arete-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.md)\n- [BigBang-v1 vs Inkling](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling.md)\n- [BigBang-v1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.md)\n- [Carbon-X vs Inkling](https://www.anchorterminal.com/compare/carbon-x-vs-inkling.md)\n- [Carbon-X vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b.md)\n- [Cola2 vs Inkling](https://www.anchorterminal.com/compare/cola2-vs-inkling.md)\n- [Cola2 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.md)\n- [Cosmos-Reason2-2B vs Inkling](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b.md)\n- [Darwin-36B-Opus vs Inkling](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling.md)\n- [Darwin-36B-Opus vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b.md)\n- [DeepSeek-V3.2 vs Inkling](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b.md)\n- [DeepSeek V4.1 Flash vs Inkling](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b.md)\n- [DeepSeek-V4-Flash-0731 vs Inkling](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-122b.md)\n- [Devstral Small 2 vs Inkling](https://www.anchorterminal.com/compare/devstral-small-2-vs-inkling.md)\n- [Devstral Small 2 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b.md)\n- [DiffusionGemma 26B A4B IT vs Inkling](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-122b.md)\n- [EXAONE 4.5 33B vs Inkling](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-inkling.md)\n- [EXAONE 4.5 33B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-122b.md)\n- [Gemma 4 12B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling.md)\n- [Gemma 4 12B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-122b.md)\n- [Gemma 4 26B A4B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-122b.md)\n- [Gemma 4 31B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling.md)\n- [Gemma 4 31B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-122b.md)\n- [Gemma 4 E2B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-122b.md)\n- [Gemma 4 E4B IT vs Inkling](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-122b.md)\n- [GLM-4.5-Air vs Inkling](https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling.md)\n- [GLM-4.5-Air vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-122b.md)\n- [GLM-4.7-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling.md)\n- [GLM-4.7-Flash vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.md)\n- [GLM-5.3-Flash vs Inkling](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling.md)\n- [GLM-5.3-Flash vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-122b.md)\n- [GLM-5.3 vs Inkling](https://www.anchorterminal.com/compare/glm-5-3-vs-inkling.md)\n- [GLM-5.3 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-122b.md)\n- [gpt-oss-120b vs Inkling](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-inkling.md)\n- [gpt-oss-120b vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-122b.md)\n- [gpt-oss-20b vs Inkling](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling.md)\n- [gpt-oss-20b vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b.md)\n- [Granite 4.2 30B vs Inkling](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-inkling.md)\n- [Granite 4.2 30B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b.md)\n- [Granite 4.2 3B vs Inkling](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling.md)\n- [Granite 4.2 3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b.md)\n- [Granite 4.2 8B vs Inkling](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-inkling.md)\n- [Granite 4.2 8B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.md)\n- [Hy-Memory-30B-A3B vs Inkling](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.md)\n- [Hy3 vs Inkling](https://www.anchorterminal.com/compare/hy3-vs-inkling.md)\n- [Hy3 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-122b.md)\n- [Inkling-Small vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-122b.md)\n- [Inkling vs Inkling-Small](https://www.anchorterminal.com/compare/inkling-vs-inkling-small.md)\n- [Inkling vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling vs Kimi K3](https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.md)\n- [Inkling vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/inkling-vs-kimi-k3-1-pro.md)\n- [Inkling vs Kolibri-1](https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.md)\n- [Inkling vs Kronos](https://www.anchorterminal.com/compare/inkling-vs-kronos.md)\n- [Inkling vs Laguna M.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-m-1.md)\n- [Inkling vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-s-2-1.md)\n- [Inkling vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-vs-laguna-xs-2-1.md)\n- [Inkling vs LBS-27B](https://www.anchorterminal.com/compare/inkling-vs-lbs-27b.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [Inkling vs LFM3-1.7B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-1-7b.md)\n- [Inkling vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-audio-2b.md)\n- [Inkling vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-omni-2b.md)\n- [Inkling vs LFM3-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-lfm3-vl-2b.md)\n- [Inkling vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/inkling-vs-ling-3-0-tiny.md)\n- [Inkling vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/inkling-vs-ling-3-1-flash-1019.md)\n- [Inkling vs LongCat-2.5](https://www.anchorterminal.com/compare/inkling-vs-longcat-2-5.md)\n- [Inkling vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Inkling vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.md)\n- [Inkling vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-vs-minicpm-o-4-5.md)\n- [Inkling vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-1b.md)\n- [Inkling vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-minicpm-v-4-7-35b-a3b.md)\n- [Inkling vs MiniCPM5-2B](https://www.anchorterminal.com/compare/inkling-vs-minicpm5-2b.md)\n- [Inkling vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5.md)\n- [Inkling vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-7.md)\n- [Inkling vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/inkling-vs-minimax-m3-1-flash.md)\n- [Inkling vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-vs-mistral-large-4.md)\n- [Inkling vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-vs-muse-glimmer-30b.md)\n- [Inkling vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-2-3b.md)\n- [Inkling vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b.md)\n- [Inkling vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Inkling vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-30b-a3b.md)\n- [Inkling vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-4b.md)\n- [Inkling vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Inkling vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b.md)\n- [Inkling vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-ultra-550b-a55b.md)\n- [Inkling vs North Mini Code 1.0](https://www.anchorterminal.com/compare/inkling-vs-north-mini-code-1-0.md)\n- [Inkling vs occamy-1.0](https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.md)\n- [Inkling vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/inkling-vs-olmo-3-7b-think.md)\n- [Inkling vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.md)\n- [Inkling vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.md)\n- [Inkling vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.md)\n- [Inkling vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.md)\n- [Inkling vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-qwen-agentworld-35b-a3b.md)\n- [Inkling vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/inkling-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Inkling vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/inkling-vs-qwen3-4b-instruct-2507.md)\n- [Inkling vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-0-8b.md)\n- [Inkling vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-122b-a10b.md)\n- [Inkling vs Qwen3.5-2B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-2b.md)\n- [Inkling vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-397b-a17b.md)\n- [Inkling vs Qwen3.5-4B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-4b.md)\n- [Inkling vs Qwen3.5-9B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-5-9b.md)\n- [Inkling vs Qwen3.6-27B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-27b.md)\n- [Inkling vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-6-35b-a3b.md)\n- [Inkling vs Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-27b.md)\n- [Inkling vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-vs-qwen3-8-flash-next.md)\n- [Inkling vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Inkling vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/inkling-vs-qwen3-coder-next.md)\n- [Inkling vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Inkling vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-2b-instruct.md)\n- [Inkling vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Inkling vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-32b-instruct.md)\n- [Inkling vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-4b-instruct.md)\n- [Inkling vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/inkling-vs-qwen3-vl-8b-instruct.md)\n- [Inkling vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/inkling-vs-qwopus3-8-27b-flash-v2.md)\n- [Inkling vs Recursive Flint](https://www.anchorterminal.com/compare/inkling-vs-recursive-flint.md)\n- [Inkling vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-vs-ripple-65b-preview.md)\n- [Inkling vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/inkling-vs-sensenova-loop-9b-preview.md)\n- [Inkling vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-vs-smollm3-3b.md)\n- [Inkling vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.md)\n- [Inkling vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash.md)\n- [Inkling vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Inkling vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.md)\n- [Inkling vs 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