{ "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 occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.", "b": { "slug": "occamy-1-0", "name": "occamy-1.0", "vendor": "Accio (Accio-Lab)", "vendorUrl": "https://accio-lab.github.io/occamy/", "kind": "weights", "category": "open-weights", "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/occamy-1-0", "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json", "dns": "occamy-1-0.grade.anchorterminal.com", "repo": "https://huggingface.co/Accio-Lab/occamy-1.0", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-FP8" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-NVFP4" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-APEX-GGUF" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-4bit" }, { "registry": "huggingface", "name": "Accio-Lab/occamy-1.0-MLX-mxfp4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (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/Accio-Lab/occamy-1.0", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agent", "coding", "derivative", "apache-2.0", "safetensors", "gguf", "mlx", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-10-02", "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": 49, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.", "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "strengths": [ "Apache 2.0 like its base, with nothing gated", "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB", "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama, vLLM, SGLang and Transformers run it", "A technical report, the supervised data mix with token counts and a published data subset", "MD5 and SHA-256 checksums for every file" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact", "No hosted API or inference provider", "Tool-calling format and structured output aren't documented beyond the template and parser name", "Languages and a knowledge cut-off aren't stated", "The MLX builds await Apple Metal acceptance, per Accio's own cards" ], "agentNotes": [ "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later", "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism", "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says", "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5", "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata", "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head", "Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 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": 70, "reach": 90, "risk": 55, "upkeep": 74 }, "provenanceScore": 24, "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": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/occamy-1-0" }, "weights": { "trainedBy": "Accio (Accio-Lab)", "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "occamy-1.0-MLX-mxfp4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4", "who": "publisher", "precision": "mxfp4", "sizeGB": 18.4, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-MLX-4bit", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 19.5, "memoryGB": 24, "format": "mlx", "runsOn": [ "MLX" ], "note": "Text only. Apple Metal acceptance pending, per the card" }, { "name": "occamy-1.0-IQ4_XS", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 18.7, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card" }, { "name": "occamy-1.0-APEX-Compact", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf", "who": "publisher", "precision": "mixed-q3-q4", "sizeGB": 16.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line" }, { "name": "occamy-1.0-NVFP4", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.9, "memoryGB": 32, "format": "safetensors", "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card" }, { "name": "occamy-1.0-Q4_K_M", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 21.2, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0-FP8", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 36.6, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "occamy-1.0-Q8_0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 36.9, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "occamy-1.0", "url": "https://huggingface.co/Accio-Lab/occamy-1.0", "who": "publisher", "precision": "bf16", "sizeGB": 70.2, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "occamy-1.0", "checked": "2026-11-03", "minMemoryGB": 24 }, "family": { "lab": "Accio (Accio-Lab)", "family": "occamy", "url": "https://www.anchorterminal.com/open-weights/#accio-lab", "others": [ { "name": "occamy-1.0-MTP", "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP", "params": "not stated (draft head only)" } ], "olderCount": 4, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "accio-lab.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.", "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues." ], "score": 24 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json", "live": { "slug": "occamy-1-0", "securityTxt": { "url": "https://accio-lab.github.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:29.220890734Z" }, "domain": { "domain": "github.io", "checkedAt": "2026-11-08T15:08:12.013832199Z" }, "updatedAt": "2026-11-10T16:08:29.220890734Z" } }, "edges": [ { "by": 25, "category": "reach", "leads": "occamy-1-0", "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 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90." }, "scores": { "a": 65, "b": 90 } }, { "by": 15, "category": "interface", "leads": "mimo-v2-6-flash-rl", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens." }, "scores": { "a": 85, "b": 70 } }, { "by": 15, "category": "upkeep", "leads": "occamy-1-0", "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 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74." }, "scores": { "a": 59, "b": 74 } }, { "by": 10, "category": "risk", "leads": "occamy-1-0", "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 only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact." }, "scores": { "a": 45, "b": 55 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Xiaomi (MiMo team)", "b": "Accio (Accio-Lab)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT (metadata only, no LICENSE file)", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-28", "b": "2026-10-02", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "MiMo-V2.6-Flash-RL and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.", "question": "Which is better for AI agents, MiMo-V2.6-Flash-RL or occamy-1.0?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 85 against 70" ], "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, 55 against 45", "Documentation and upkeep, 74 against 59" ], "also": null, "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.", "slug": "occamy-1-0", "watchFor": "No safety results, prompt-injection guidance or security contact" } ], "history": { "mimo-v2-6-flash-rl": null, "occamy-1-0": [ { "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-occamy-1-0.json", "title": "Agents-A1-4B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0" }, { "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": 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"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-occamy-1-0.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0" }, { "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-occamy-1-0.json", "title": "Kimi K3.1 Pro vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0" }, { "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-occamy-1-0.json", "title": "Kimi K3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0" }, { "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-occamy-1-0.json", "title": "Kolibri-1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.json", "title": "Kronos vs MiMo-V2.6-Flash-RL", "url": 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2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl.json", "title": "Laguna XS 2.1 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.json", "title": "Laguna XS 2.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl.json", "title": "LBS-27B vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json", "title": "LBS-27B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0" }, { "json": 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Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.json", "title": "occamy-1.0 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.json", "title": "occamy-1.0 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b.json", "title": "occamy-1.0 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b.json", "title": "occamy-1.0 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.json", "title": "occamy-1.0 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.json", "title": "occamy-1.0 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json", "title": "occamy-1.0 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json", "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json", "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json", "title": "occamy-1.0 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.json", "title": "occamy-1.0 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview.json", "title": "occamy-1.0 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json", "title": "occamy-1.0 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json", "title": "occamy-1.0 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash.json", "title": "occamy-1.0 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json", "title": "occamy-1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json", "title": "occamy-1.0 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.json", "title": "occamy-1.0 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b.json", "title": "occamy-1.0 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "occamy-1-0", "key": "reach", "mimo-v2-6-flash-rl": 65, "name": "Reach", "occamy-1-0": 90, "weight": 25 }, { "by": 10, "edge": "occamy-1-0", "key": "risk", "mimo-v2-6-flash-rl": 45, "name": "Risk", "occamy-1-0": 55, "weight": 20 }, { "by": 15, "edge": "mimo-v2-6-flash-rl", "key": "interface", "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "occamy-1-0": 70, "weight": 10 }, { "by": 15, "edge": "occamy-1-0", "key": "upkeep", "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "occamy-1-0": 74, "weight": 10 } ], "summary": "MiMo-V2.6-Flash-RL and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep. 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.", "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own." } }, "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-occamy-1-0", "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.min.md" }, "markdown": "MiMo-V2.6-Flash-RL and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep. 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- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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- Agent interface, 85 against 70\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 90 against 65\n- Risk, 55 against 45\n- Documentation and upkeep, 74 against 59\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Why the edge\n\n### Reach: occamy-1.0 +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- occamy-1.0 (90): Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Agent interface: MiMo-V2.6-Flash-RL +15\n\n- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n- occamy-1.0 (70): Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens.\n\n### Documentation and upkeep: occamy-1.0 +15\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- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.\n\n### Risk: occamy-1.0 +10\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- occamy-1.0 (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact.\n\n## Score by category\n\n| Category | Weight | MiMo-V2.6-Flash-RL | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 90 | occamy-1.0 +25 |\n| Risk | 20% (20 this run) | 45 | 55 | occamy-1.0 +10 |\n| Agent interface | 10% (10 this run) | 85 | 70 | MiMo-V2.6-Flash-RL +15 |\n| Documentation and upkeep | 10% (10 this run) | 59 | 74 | occamy-1.0 +15 |\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 | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Xiaomi (MiMo team) | Accio (Accio-Lab) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT (metadata only, no LICENSE file) | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-10-02 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3/5 (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### occamy-1.0\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**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\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### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n7. Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture\n\n## Questions\n\n### Which is better for AI agents, MiMo-V2.6-Flash-RL or occamy-1.0?\n\nMiMo-V2.6-Flash-RL and occamy-1.0 score within a point of each other on agent readiness, 61 (C) and 61 (C). occamy-1.0 leads on reach, risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mimo-v2-6-flash-rl\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl occamy-1-0`\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/occamy-1-0.json\n\n## Other comparisons with MiMo-V2.6-Flash-RL or occamy-1.0\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 occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [Arete-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [BigBang-v1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/bigbang-v1-vs-mimo-v2-6-flash-rl.md)\n- [BigBang-v1 vs occamy-1.0](https://www.anchorterminal.com/compare/bigbang-v1-vs-occamy-1-0.md)\n- [Carbon-X vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/carbon-x-vs-mimo-v2-6-flash-rl.md)\n- [Carbon-X vs occamy-1.0](https://www.anchorterminal.com/compare/carbon-x-vs-occamy-1-0.md)\n- [Cola2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.md)\n- [Cola2 vs occamy-1.0](https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.md)\n- [Cosmos-Reason2-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-mimo-v2-6-flash-rl.md)\n- [Cosmos-Reason2-2B vs occamy-1.0](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-occamy-1-0.md)\n- [Darwin-36B-Opus vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-mimo-v2-6-flash-rl.md)\n- [Darwin-36B-Opus vs occamy-1.0](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-occamy-1-0.md)\n- [DeepSeek-V3.2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek-V3.2 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek-V4-Flash-0731 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-mimo-v2-6-flash-rl.md)\n- [DeepSeek-V4-Flash-0731 vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-occamy-1-0.md)\n- [Devstral Small 2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/devstral-small-2-vs-mimo-v2-6-flash-rl.md)\n- [Devstral Small 2 vs occamy-1.0](https://www.anchorterminal.com/compare/devstral-small-2-vs-occamy-1-0.md)\n- [DiffusionGemma 26B A4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.md)\n- [DiffusionGemma 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-occamy-1-0.md)\n- [EXAONE 4.5 33B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-mimo-v2-6-flash-rl.md)\n- [EXAONE 4.5 33B vs occamy-1.0](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-occamy-1-0.md)\n- [Gemma 4 12B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 12B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-occamy-1-0.md)\n- [Gemma 4 26B A4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 26B A4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-occamy-1-0.md)\n- [Gemma 4 31B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 31B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0.md)\n- [Gemma 4 E2B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 E2B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.md)\n- [Gemma 4 E4B IT vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mimo-v2-6-flash-rl.md)\n- [Gemma 4 E4B IT vs occamy-1.0](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.md)\n- [GLM-4.5-Air vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-flash-rl.md)\n- [GLM-4.5-Air vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.md)\n- [GLM-4.7-Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-mimo-v2-6-flash-rl.md)\n- [GLM-4.7-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.md)\n- [GLM-5.3-Flash vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mimo-v2-6-flash-rl.md)\n- [GLM-5.3-Flash vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.md)\n- [GLM-5.3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/glm-5-3-vs-mimo-v2-6-flash-rl.md)\n- [GLM-5.3 vs occamy-1.0](https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.md)\n- [gpt-oss-120b vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-mimo-v2-6-flash-rl.md)\n- [gpt-oss-120b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.md)\n- [gpt-oss-20b vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-mimo-v2-6-flash-rl.md)\n- [gpt-oss-20b vs occamy-1.0](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.md)\n- [Granite 4.2 30B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 30B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0.md)\n- [Granite 4.2 3B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 3B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.md)\n- [Granite 4.2 8B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-mimo-v2-6-flash-rl.md)\n- [Granite 4.2 8B vs occamy-1.0](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-occamy-1-0.md)\n- [Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.md)\n- [Hy-Memory-30B-A3B vs occamy-1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.md)\n- [Hy3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.md)\n- [Hy3 vs occamy-1.0](https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.md)\n- [Inkling-Small vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-flash-rl.md)\n- [Inkling-Small vs occamy-1.0](https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mimo-v2-6-flash-rl.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.md)\n- [Kimi K3.1 Pro vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mimo-v2-6-flash-rl.md)\n- [Kimi K3.1 Pro vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0.md)\n- [Kimi K3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.md)\n- [Kimi K3 vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.md)\n- [Kolibri-1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.md)\n- [Kolibri-1 vs occamy-1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.md)\n- [Kronos vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.md)\n- [Kronos vs occamy-1.0](https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0.md)\n- [Laguna M.1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-flash-rl.md)\n- [Laguna M.1 vs occamy-1.0](https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.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 occamy-1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.md)\n- [LFM3-1.7B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-1.7B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0.md)\n- [LFM3-Audio-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-Audio-2B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0.md)\n- [LFM3-Omni-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-Omni-2B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0.md)\n- [LFM3-VL-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.md)\n- [LFM3-VL-2B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.md)\n- [Ling-3.0-tiny vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.md)\n- [Ling-3.0-tiny vs occamy-1.0](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0.md)\n- [Ling-3.1-flash-1019 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-mimo-v2-6-flash-rl.md)\n- [Ling-3.1-flash-1019 vs occamy-1.0](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-occamy-1-0.md)\n- [LongCat-2.5 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.md)\n- [LongCat-2.5 vs occamy-1.0](https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0.md)\n- [MiMo-V2.6 Distill Qwen 9B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-mimo-v2-6-flash-rl.md)\n- [MiMo-V2.6 Distill Qwen 9B vs occamy-1.0](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-occamy-1-0.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-o-4-5.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs MiniCPM5-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm5-2b.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M2.5](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M2.7](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-7.md)\n- [MiMo-V2.6-Flash-RL vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m3-1-flash.md)\n- [MiMo-V2.6-Flash-RL vs Mistral Large 4](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-mistral-large-4.md)\n- [MiMo-V2.6-Flash-RL vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-muse-glimmer-30b.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.md)\n- [MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b.md)\n- [MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-30b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-4b.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-super-120b-a12b.md)\n- [MiMo-V2.6-Flash-RL vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.md)\n- [MiMo-V2.6-Flash-RL vs North Mini Code 1.0](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-north-mini-code-1-0.md)\n- [MiMo-V2.6-Flash-RL vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-olmo-3-7b-think.md)\n- [MiMo-V2.6-Flash-RL vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b.md)\n- [MiMo-V2.6-Flash-RL vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-397b.md)\n- [MiMo-V2.6-Flash-RL vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-9b.md)\n- [MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it.md)\n- [MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.md)\n- [MiMo-V2.6-Flash-RL vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-30b-a3b-instruct-2507.md)\n- [MiMo-V2.6-Flash-RL vs 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