{ "data": { "a": { "slug": "mimo-v2-6-flash-rl", "name": "MiMo-V2.6-Flash-RL", "vendor": "Xiaomi (MiMo team)", "vendorUrl": "https://mimo.xiaomi.com", "kind": "weights", "category": "open-weights", "summary": "MiMo-V2.6-Flash-RL is Xiaomi's open-weight agent model, a mixture of experts of 309B parameters with 15B active, taking text, images, video and audio with a 1M context. MIT. The weights are 178 GB at 4 bits, so it needs a server.", "url": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl", "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json", "dns": "mimo-v2-6-flash-rl.grade.anchorterminal.com", "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "license": "MIT (metadata only, no LICENSE file)", "transports": [], "packages": [ { "registry": "huggingface", "name": "XiaomiMiMo/MiMo-V2.6-Flash-RL" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under MIT, per the card's metadata. Running it yourself needs a multi-GPU server: the files are 177.7 GB with most weights at 4 bits. Xiaomi's API platform and Novita serve it for those without the hardware (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/XiaomiMiMo/MiMo-V2.6-Flash-RL", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "audio", "video", "reasoning", "tool-calling", "agentic", "long-context", "mit", "safetensors", "fp4", "custom-code", "self-hosted" ], "lastRelease": "2026-10-28", "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": 59, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 45, "upkeep": 59 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.", "bestFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "strengths": [ "MIT, ungated, with hosted options", "Four released runtimes carry it, with tool and reasoning parsers in vLLM and SGLang", "A 1M context and four input types", "Trained with reinforcement learning across agent harnesses", "A GGUF from the llama.cpp project's own organisation" ], "weaknesses": [ "178 GB at 4 bits, so it needs about 200 GB of memory", "The card's serve commands pass trust-remote-code", "A pickle file sits in the draft model's folder", "No safety results, limitations or data sources on the card", "Xiaomi replied in a few of the discussions only" ], "agentNotes": [ "Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita", "On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`", "Use temperature 1.0 and top_p 0.95, as the card recommends", "The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in", "Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle", "The Use Restrictions section asks for human oversight of high-risk actions" ], "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": 65, "risk": 45, "upkeep": 59 }, "provenanceScore": 58, "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": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo\n# the card's full command adds data-parallel attention and speculative decoding flags", "http": "# SGLang and vLLM serve an OpenAI-compatible API. The card recommends temperature 1.0 and top_p 0.95,\n# and names the mimo tool-call and reasoning parsers for both" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/mimo-v2-6-flash-rl" }, "sameCompany": [ "mimo-v2-6-distill-qwen-9b" ], "weights": { "trainedBy": "Xiaomi (MiMo team)", "params": "309B", "active": "15B", "context": 1048576, "goodFor": "Long agent runs over code, tools, screens and audio on a multi-GPU server, or through a provider.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "MiMo-V2.6-Flash-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL", "who": "publisher", "precision": "fp4", "sizeGB": 177.74, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build Xiaomi publishes, with most weights at 4 bits" }, { "name": "ggml-org MiMo-V2.6-Flash-RL-GGUF (MXFP4)", "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Flash-RL-GGUF", "who": "llama.cpp", "precision": "mxfp4", "sizeGB": 168.93, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 167.37 GB of MXFP4 weights plus the 1.56 GB Q8_0 projector, from the Hub's model information on 10 November 2026" } ], "run": "sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --tp 8 --reasoning-parser mimo --tool-call-parser mimo", "runBuild": "MiMo-V2.6-Flash-RL", "checked": "2026-11-10", "minMemoryGB": 256 }, "family": { "lab": "Xiaomi (MiMo)", "family": "MiMo-V2 (V2, V2.5, V2.6)", "url": "https://www.anchorterminal.com/open-weights/#xiaomi", "others": [ { "name": "MiMo-V2.6-Distill-Qwen-9B", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B", "params": "9B (Hub safetensors count 9.41B)", "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b" }, { "name": "MiMo-V2.6-Pro-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Flash-MOPD", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.6-Pro-RL", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL", "params": "1024.22B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Pro", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro", "params": "1023.18B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5", "params": "310.76B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2.5-ASR", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR", "params": "7.62B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash-Base", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base", "params": "309.77B (Hub safetensors count; the name states none)" }, { "name": "MiMo-V2-Flash", "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash", "params": "309.77B (Hub safetensors count; the name states none)" } ], "olderCount": 12 }, "area": "models", "provenance": { "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)", "domain": "mimo.xiaomi.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Xiaomi's legal entity, domain and security facts are carried from our MiMo-V2.6 Distill listing.", "Nothing is hosted under this listing, so MIT stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 13 commits from 21 September to 28 October 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.json" }, "answer": "MiMo-V2.6-Flash-RL and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach and risk.", "b": { "slug": "ornith-1-5-35b-a3b", "name": "Ornith-1.5-35B-A3B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-35B-A3B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 35B parameters with 3B active per token 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-1-5-35b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json", "dns": "ornith-1-5-35b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit" }, { "registry": "ollama", "name": "ornith-1.5:35b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all eight of Ornith's build repositories for this size are not gated (checked 2026-10-21).", "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 72 GB for the BF16 files, 19.5 GB for Ornith's MLX 4-bit build or 21.7 GB for its Q4_K_M GGUF (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://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "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": 40, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 90, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 36 reports.", "bestFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required", "strengths": [ "Fits in about 24 GB from Ornith's MLX 4-bit build and 32 GB from its Q4_K_M GGUF, NVFP4 or Ollama's build, with only about 3B parameters active per token", "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", "A weight shard, every GGUF, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August 2026 with no note", "Ornith answered none of the 36 discussions", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it", "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 general tasks and temperature 1.0 to reproduce the card's benchmarks", "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "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": 90, "risk": 50, "upkeep": 36 }, "provenanceScore": 19, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:35b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --tensor-parallel-size 2 --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-1.5:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-35b-a3b" }, "sameCompany": [ "ornith-1-5-122b", "ornith-1-5-397b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-35B-A3B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-35B-A3B (Apache 2.0)", "params": "35B (3B active)", "context": 262144, "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, fast for its size with about 3B active parameters.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Ornith-1.5-35B-A3B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.5, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-35B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 25.3, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.7, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input" }, { "name": "ollama ornith-1.5:35b", "url": "https://ollama.com/library/ornith-1.5:35b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 23, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (22 GB) with a BF16 vision projector (903 MB), linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "Ornith-1.5-35B-A3B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 39.4, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Ornith-1.5-35B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 37.8, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 29.2, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-A3B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 36.9, "memoryGB": 48, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B-MLX-6bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-6bit", "who": "publisher", "precision": "6bit", "sizeGB": 28.2, "memoryGB": 48, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Ornith-1.5-35B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 71.1, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-35B-A3B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 69.3, "memoryGB": 96, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:35b", "runBuild": "ollama ornith-1.5:35b", "checked": "2026-11-03", "minMemoryGB": 24 }, "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-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-122b" }, { "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)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.", "The card's blog badge links deep-reinforce.com/ornith.html, which redirects to Ornith's 1.0 blog post on ornith.ai.", "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. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and 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-1-5-35b-a3b.json", "live": { "slug": "ornith-1-5-35b-a3b", "securityTxt": { "url": "https://ornith.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:27.802812649Z" }, "domain": { "domain": "ornith.ai", "registered": "2026-06-16", "source": "https://rdap.identitydigital.services/rdap/domain/ornith.ai", "checkedAt": "2026-11-08T14:47:46.372430234Z" }, "updatedAt": "2026-11-10T16:07:27.802812649Z" } }, "edges": [ { "by": 25, "category": "reach", "leads": "ornith-1-5-35b-a3b", "name": "Reach", "reasons": { "a": "Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.", "b": "Memory 25, since Ornith's own MLX 4-bit build is 19.5 GB and needs about 23.4 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, NVFP4 build and Ollama's build each need 32 GB. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, 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 on 21 October 2026, 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. 25 + 25 + 15 + 15 + 10 + 0 = 90." }, "scores": { "a": 65, "b": 90 } }, { "by": 23, "category": "upkeep", "leads": "mimo-v2-6-flash-rl", "name": "Documentation and upkeep", "reasons": { "a": "Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.", "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, Ornith deleted configuration.json and a marker file from its FP8 build on 20 October 2026, 14 days ago, which this run counts as a change to the files. Responsiveness 0, Ornith answered none of the 36 questions and reports. Version policy 0, a weight shard, every GGUF file, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August with no note. File health 0, the card links a missing LICENSE, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36." }, "scores": { "a": 59, "b": 36 } }, { "by": 5, "category": "risk", "leads": "ornith-1-5-35b-a3b", "name": "Risk", "reasons": { "a": "File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.", "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": 45, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Xiaomi (MiMo team)", "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": "MIT (metadata only, no LICENSE file)", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-28", "b": "2026-08-23", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiMo-V2.6-Flash-RL and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach and risk.", "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or Ornith-1.5-35B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 59 against 36" ], "also": null, "goodFor": "Teams with a GPU server, or a provider account, that want an open agent model with a very long context", "slug": "mimo-v2-6-flash-rl", "watchFor": "178 GB at 4 bits, so it needs about 200 GB of memory" }, { "aheadOn": [ "Reach, 90 against 65", "Risk, 50 against 45" ], "also": null, "goodFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-35b-a3b", "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": { "mimo-v2-6-flash-rl": null, "ornith-1-5-35b-a3b": [ { "causes": [ "correction" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.json", "title": "Agents-A1-4B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json", "title": "Agents-A1-4B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.json", "title": "Apertus v1.5 8B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.json", "title": "Apodex-1.1-mini vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.json", "title": "Arete-27B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b.json", "title": "Arete-27B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.json", "title": "BigBang-v1 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.json", "title": "BigBang-v1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl.json", "title": "Carbon-X vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-35b-a3b.json", "title": "Carbon-X vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json", "title": "Cola2 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-35b-a3b.json", "title": "Cola2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl.json", "title": "Cosmos-Reason2-2B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl.json", "title": "Darwin-36B-Opus vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl.json", "title": "DeepSeek-V3.2 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.json", "title": "DeepSeek V4.1 Flash vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.json", "title": "DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.json", "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl.json", "title": "Devstral Small 2 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-35b-a3b.json", "title": "Devstral Small 2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.json", "title": "DiffusionGemma 26B A4B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.json", "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.json", "title": "EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-35b-a3b.json", "title": "EXAONE 4.5 33B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.json", "title": "Gemma 4 12B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 12B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl.json", "title": "Gemma 4 26B A4B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl.json", "title": "Gemma 4 31B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl.json", "title": "Gemma 4 E2B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 E2B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl.json", "title": "Gemma 4 E4B IT vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.json", "title": "Gemma 4 E4B IT vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl.json", "title": "GLM-4.5-Air vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.json", "title": "GLM-4.5-Air vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.json", "title": "GLM-4.7-Flash vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b.json", "title": "GLM-4.7-Flash vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl.json", "title": "GLM-5.3-Flash vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-35b-a3b.json", "title": "GLM-5.3-Flash vs Ornith-1.5-35B-A3B", "url": 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"https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.json", "title": "gpt-oss-20b vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b.json", "title": "gpt-oss-20b vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.json", "title": "Granite 4.2 30B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b.json", "title": "Granite 4.2 30B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.json", "title": "Granite 4.2 3B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b.json", "title": "Granite 4.2 3B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.json", "title": "Granite 4.2 8B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-35b-a3b.json", "title": "Granite 4.2 8B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.json", "title": "Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL", "url": 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"https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-35b-a3b.json", "title": "Inkling-Small vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.json", "title": "Inkling vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b.json", "title": "Inkling vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.json", "title": "Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b.json", "title": "Kimi K3.1 Pro vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json", "title": "Kimi K3 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.json", "title": "Kimi K3 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.json", "title": "Kolibri-1 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.json", "title": "Kolibri-1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.json", "title": "Kronos vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-35b-a3b.json", "title": "Kronos vs Ornith-1.5-35B-A3B", "url": 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Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint.json", "title": "Ornith-1.5-35B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-35B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Ornith-1.5-35B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.json", "title": "Ornith-1.5-35B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-step-3-7-flash.json", "title": "Ornith-1.5-35B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-35B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-zaya1-8b.json", "title": "Ornith-1.5-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "ornith-1-5-35b-a3b", "key": "reach", "mimo-v2-6-flash-rl": 65, "name": "Reach", "ornith-1-5-35b-a3b": 90, "weight": 25 }, { "by": 5, "edge": "ornith-1-5-35b-a3b", "key": "risk", "mimo-v2-6-flash-rl": 45, "name": "Risk", "ornith-1-5-35b-a3b": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "ornith-1-5-35b-a3b": 85, "weight": 10 }, { "by": 23, "edge": "mimo-v2-6-flash-rl", "key": "upkeep", "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "ornith-1-5-35b-a3b": 36, "weight": 10 } ], "summary": "MiMo-V2.6-Flash-RL and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach and risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "mimo-v2-6-flash-rl": "A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.", "ornith-1-5-35b-a3b": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 36 reports." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b", "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.min.md" }, "markdown": "MiMo-V2.6-Flash-RL and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach and risk. Both do open-weight models you run yourself.\n\n- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n- Ornith-1.5-35B-A3B: grade C, 61/100, rank #50 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.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### MiMo-V2.6-Flash-RL (C)\n\nGood for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context\n\nAhead on:\n- Documentation and upkeep, 59 against 36\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### Ornith-1.5-35B-A3B (C)\n\nGood for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 90 against 65\n- Risk, 50 against 45\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### Reach: Ornith-1.5-35B-A3B +25\n\n- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.\n- Ornith-1.5-35B-A3B (90): Memory 25, since Ornith's own MLX 4-bit build is 19.5 GB and needs about 23.4 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, NVFP4 build and Ollama's build each need 32 GB. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, 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 on 21 October 2026, 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. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +23\n\n- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.\n- Ornith-1.5-35B-A3B (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, Ornith deleted configuration.json and a marker file from its FP8 build on 20 October 2026, 14 days ago, which this run counts as a change to the files. Responsiveness 0, Ornith answered none of the 36 questions and reports. Version policy 0, a weight shard, every GGUF file, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August with no note. File health 0, the card links a missing LICENSE, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n\n### Risk: Ornith-1.5-35B-A3B +5\n\n- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.\n- Ornith-1.5-35B-A3B (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 | MiMo-V2.6-Flash-RL | Ornith-1.5-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | Ornith-1.5-35B-A3B +25 |\n| Risk | 20% (20 this run) | 45 | 50 | Ornith-1.5-35B-A3B +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 59 | 36 | MiMo-V2.6-Flash-RL +23 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiMo-V2.6-Flash-RL | Ornith-1.5-35B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | 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 | MIT (metadata only, no LICENSE file) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.3/5 (4) |\n\n## How the scores have moved\n\n### MiMo-V2.6-Flash-RL\n\nNo score change since it was listed.\n\n### Ornith-1.5-35B-A3B\n\n- no change on 3 November 2026: correction\n\n## Verdicts\n\n**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.\n\n**Ornith-1.5-35B-A3B.** A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 36 reports.\n\n## Before you call either\n\n### MiMo-V2.6-Flash-RL\n\n1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita\n2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`\n3. Use temperature 1.0 and top_p 0.95, as the card recommends\n4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in\n5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle\n6. The Use Restrictions section asks for human oversight of high-risk actions\n\n### Ornith-1.5-35B-A3B\n\n1. Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it\n2. 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\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or Ornith-1.5-35B-A3B?\n\nMiMo-V2.6-Flash-RL and Ornith-1.5-35B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-35B-A3B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"ornith-1-5-35b-a3b\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl ornith-1-5-35b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or Ornith-1.5-35B-A3B\n\n- [Agents-A1-4B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-flash-rl.md)\n- [Agents-A1-4B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.md)\n- [Apertus v1.5 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-flash-rl.md)\n- [Apertus v1.5 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.md)\n- [Apodex-1.1-mini vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-mimo-v2-6-flash-rl.md)\n- [Apodex-1.1-mini vs 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vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna S 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna S 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-35b-a3b.md)\n- [Laguna XS 2.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna XS 2.1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-35b-a3b.md)\n- [LBS-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.md)\n- [LBS-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-flash-rl.md)\n- [LFM2.5-2.6B vs 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