{ "data": { "a": { "slug": "kimi-k3", "name": "Kimi K3", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3 is Moonshot AI's open-weight multimodal mixture-of-experts model, 2.8T parameters with 104B active and a 1M-token context, trained in MXFP4 and published on Hugging Face under the modified-MIT Kimi K3 Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3.json", "dns": "kimi-k3.grade.anchorterminal.com", "repo": "https://huggingface.co/moonshotai/Kimi-K3", "license": "Kimi K3 License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Kimi K3 Licence. A company running a model-as-a-service business with more than $20 million in revenue over 12 months needs a separate agreement with Moonshot AI, at a price not published. Running it yourself costs the hardware, 1,561 GB of files (checked 2026-10-24).", "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 2026-10-24).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-02" }, "docsUrl": "https://huggingface.co/moonshotai/Kimi-K3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-09-02", "graded": true, "anchor": { "graded": true, "score": 62.4, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 23, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "bestFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "strengths": [ "1,048,576 tokens of context trained in, with indexed parallel tool calls in one turn", "Model code in released vLLM, SGLang and llama.cpp, with recipes linked from the card", "A third-party cyber evaluation by the UK AI Security Institute and CAISI", "Safetensors only, with a SHA-256 for every shard, downloadable with no account", "Served by Moonshot's API and five inference providers for agents without the hardware" ], "weaknesses": [ "1,561 GB of files at its smallest publisher build, far beyond any single machine", "No Jinja template, so prompts, tokeniser and model load through Moonshot's Python with trust_remote_code", "No Moonshot reply in the 40 most recent discussions, and a tokeniser report waits unanswered", "No disclosure route for flaws and no prompt-injection guidance", "No training sources, sizes or knowledge cut-off, and the card leaves out limitations and languages" ], "agentNotes": [ "Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect", "Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable", "Thinking is always on; set reasoning_effort to low or high when max is too slow", "Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones", "Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear", "Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.4 } ], "editorialScores": { "capability": 75, "interface": 65, "reach": 50, "risk": 65, "upkeep": 41 }, "provenanceScore": 70, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# SGLang, per its Kimi-K3 cookbook linked from the card (one node of 8 B300 GPUs)\nsglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 \\\n --context-length 65536 --mem-fraction-static 0.82 \\\n --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n# or, with llama.cpp and Unsloth's GGUF, a third party's conversion:\nllama-server -hf unsloth/Kimi-K3-GGUF:UD-Q4_K_XL --jinja", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3" }, "sameCompany": [ "kimi-k3-1-pro" ], "weights": { "trainedBy": "Moonshot AI", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding and long tool-calling sessions, text and images in, with always-on reasoning and JSON Schema output.", "runtimes": [ "llama.cpp", "vLLM", "SGLang" ], "builds": [ { "name": "Kimi-K3", "url": "https://huggingface.co/moonshotai/Kimi-K3", "who": "publisher", "precision": "mxfp4", "sizeGB": 1560.9, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4; the only build Moonshot publishes" }, { "name": "unsloth Kimi-K3-GGUF (UD-Q4_K_XL)", "url": "https://huggingface.co/unsloth/Kimi-K3-GGUF", "who": "unsloth", "precision": "q4_k_xl", "sizeGB": 1509.57, "memoryGB": 2048, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not Moonshot's. 1508.67 GB of UD-Q4_K_XL weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" } ], "run": "sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000", "runBuild": "Kimi-K3", "checked": "2026-10-24", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Moonshot AI (the licence names Moonshot AI; the Kimi Platform terms name Moonshot AI PTE. LTD.)", "domain": "moonshot.ai", "domainRegistered": "2017-12-15", "domainNote": "The .ai registry's RDAP server (Identity Digital) gives moonshot.ai a registration date of 15 December 2017, read 24 October 2026; rdap.org didn't answer on 21 October.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "securityTxt": "none", "checked": "2026-10-24", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms (platform.kimi.ai/docs/agreement/modeluse) name Moonshot AI PTE. LTD. as the party for its API.", "Nothing is hosted, so the Kimi K3 Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Its messages mostly say \"Update README.md\" or \"Update encoding_k3.py\" without saying what changed; the last change, on 2 September 2026, added an evaluation result.", "moonshot.ai, www.moonshot.ai, kimi.com and www.kimi.com answered /.well-known/security.txt with an HTML page and platform.kimi.ai with 404 on 21 and 24 October 2026, so no security.txt was found. The Kimi-K3 GitHub repository has no SECURITY.md." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3.json", "live": { "slug": "kimi-k3", "securityTxt": { "url": "https://moonshot.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:03.282831769Z" }, "domain": { "domain": "moonshot.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/moonshot.ai", "checkedAt": "2026-11-08T14:52:29.297019771Z" }, "pages": [ { "url": "https://huggingface.co/moonshotai/Kimi-K3/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:14.587006924Z", "changedAt": "2026-11-10T16:23:14.587006924Z", "fingerprint": "f16ede6bc278" } ], "updatedAt": "2026-11-10T16:23:14.587006924Z" } }, "answer": "Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep.", "b": { "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" }, "edges": [ { "by": 20, "category": "risk", "leads": "kimi-k3", "name": "Risk", "reasons": { "a": "File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.", "b": "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." }, "scores": { "a": 65, "b": 45 } }, { "by": 20, "category": "interface", "leads": "mimo-v2-6-flash-rl", "name": "Agent interface", "reasons": { "a": "The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.", "b": "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." }, "scores": { "a": 65, "b": 85 } }, { "by": 18, "category": "upkeep", "leads": "mimo-v2-6-flash-rl", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits without a reply.", "b": "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." }, "scores": { "a": 41, "b": 59 } }, { "by": 15, "category": "reach", "leads": "mimo-v2-6-flash-rl", "name": "Reach", "reasons": { "a": "Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.", "b": "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." }, "scores": { "a": 50, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Moonshot AI", "b": "Xiaomi (MiMo team)", "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": "Kimi K3 License (modified MIT)", "b": "MIT (metadata only, no LICENSE file)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-09-02", "b": "2026-10-28", "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": "2.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Kimi K3 or MiMo-V2.6-Flash-RL?" } ], "goodFor": [ { "aheadOn": [ "Risk, 65 against 45" ], "also": null, "goodFor": "Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work", "slug": "kimi-k3", "watchFor": "1,561 GB of files at its smallest publisher build, far beyond any single machine" }, { "aheadOn": [ "Reach, 65 against 50", "Agent interface, 85 against 65", "Documentation and upkeep, 59 against 41" ], "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" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"MiMo-V2.6-Flash-RL vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-27b.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.json", "title": "MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2.json", "title": "MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.json", "title": "MiMo-V2.6-Flash-RL vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview.json", "title": "MiMo-V2.6-Flash-RL vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.json", "title": "MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b.json", "title": "MiMo-V2.6-Flash-RL vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.json", "title": "MiMo-V2.6-Flash-RL vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.json", "title": "MiMo-V2.6-Flash-RL vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b.json", "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.json", "title": "MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b.json", "title": "MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.json", "title": "MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.json", "title": "MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.json", "title": "MiMo-V2.6-Flash-RL vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "mimo-v2-6-flash-rl", "key": "reach", "kimi-k3": 50, "mimo-v2-6-flash-rl": 65, "name": "Reach", "weight": 25 }, { "by": 20, "edge": "kimi-k3", "key": "risk", "kimi-k3": 65, "mimo-v2-6-flash-rl": 45, "name": "Risk", "weight": 20 }, { "by": 20, "edge": "mimo-v2-6-flash-rl", "key": "interface", "kimi-k3": 65, "mimo-v2-6-flash-rl": 85, "name": "Agent interface", "weight": 10 }, { "by": 18, "edge": "mimo-v2-6-flash-rl", "key": "upkeep", "kimi-k3": 41, "mimo-v2-6-flash-rl": 59, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "kimi-k3": "Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl", "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.min.md" }, "markdown": "Kimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Kimi K3: grade B, 62.4/100, rank #30 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3.json\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- 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### Kimi K3 (B)\n\nGood for: Teams with a multi-node GPU cluster who want an open 1M-context agent model for long coding and research work\n\nAhead on:\n- Risk, 65 against 45\n\nWatch for: 1,561 GB of files at its smallest publisher build, far beyond any single machine\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- Reach, 65 against 50\n- Agent interface, 85 against 65\n- Documentation and upkeep, 59 against 41\n\nWatch for: 178 GB at 4 bits, so it needs about 200 GB of memory\n\n\n## Why the edge\n\n### Risk: Kimi K3 +20\n\n- Kimi K3 (65): File safety earns 15, safetensors only with no pickle files, less 10 because the documented loading path runs Moonshot's Python with trust_remote_code and builds prompts with encoding_k3.py. Provenance earns the full 15, files in the organisation Moonshot links to with a SHA-256 for every shard. Safety evaluation earns 20 for the cyber assessment the UK AI Security Institute and CAISI ran and published, which found its safeguards didn't stop exploit development. Agent guidance earns 5 for the blog's general cautions about proactiveness and passing back thinking. Licence risk earns 10, losing 5 for the model-as-a-service revenue threshold and nothing else. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or public advisories found.\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\n### Agent interface: MiMo-V2.6-Flash-RL +20\n\n- Kimi K3 (65): The chat template earns 10, because there is no Jinja template and the prompt format exists only as encoding_k3.py, code you run. The tool-calling format earns 10, since Moonshot documents the tools channel but the parser name comes only from vLLM's and SGLang's own recipes. Trained tool calling earns 20 for indexed parallel calls in one turn. Structured output earns 5, as the card names it among its linked guides without saying how to ask the weights for it, and the how sits in hosted-API docs. Reasoning control earns 10, with per-request effort documented on the card and reasoning returned as reasoning_content. Context earns 10 for 1,048,576 tokens trained in.\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\n### Documentation and upkeep: MiMo-V2.6-Flash-RL +18\n\n- Kimi K3 (41): The model card earns 11, covering intended use, architecture, parameters and context, but not limitations, languages or a knowledge cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 15, an encoding_k3.py update on 20 August, 74 days before 2 November and within 90; the GitHub repository's last commit is 6 August. Responsiveness earns nothing, with no Moonshot reply in the 40 most recent discussions. Version policy earns 5 for a first release. File health earns 5, because a report that the tokeniser goes past the declared vocabulary waits without a reply.\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\n### Reach: MiMo-V2.6-Flash-RL +15\n\n- Kimi K3 (50): Memory earns nothing, because the only build of 4-bit precision or better from Moonshot or a runtime's own library is Moonshot's MXFP4 release of 1,561 GB, about 1,873 GB with the fifth added, far above 256 GB. Ollama and LM Studio list it only as a cloud model. Runtimes earn 15, for model code in released vLLM, SGLang and llama.cpp; Transformers has no kimi_k3 code of its own and loads Moonshot's remote code, so it doesn't count. Builds earn 10 for one publisher precision, the MXFP4 files the model was trained for. Terms earn 10, a custom MIT-based licence that is free for commercial use below a revenue threshold. Download earns 10, with no gate and no account. Hosted earns 5, since Moonshot's API serves it and five inference providers are live for it on Hugging Face.\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\n## Score by category\n\n| Category | Weight | Kimi K3 | MiMo-V2.6-Flash-RL | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 65 | MiMo-V2.6-Flash-RL +15 |\n| Risk | 20% (20 this run) | 65 | 45 | Kimi K3 +20 |\n| Agent interface | 10% (10 this run) | 65 | 85 | MiMo-V2.6-Flash-RL +20 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 59 | MiMo-V2.6-Flash-RL +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.4 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kimi K3 | MiMo-V2.6-Flash-RL |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Moonshot AI | Xiaomi (MiMo team) |\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 | Kimi K3 License (modified MIT) | MIT (metadata only, no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-02 | 2026-10-28 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## Verdicts\n\n**Kimi K3.** Kimi K3 is a 2.8T multimodal agent model with a 1M-token context, parallel tool calls and per-request effort, for long coding and research sessions. Its 1,561 GB of files need a multi-node GPU cluster, so most agents will call a hosted copy. The main risks are Moonshot's Python running at load and prompt time, and no answers in its repository.\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## Before you call either\n\n### Kimi K3\n\n1. Start it with SGLang on a node of 8 B300s, sglang serve --trust-remote-code --model-path moonshotai/Kimi-K3 --tp-size 8 --context-length 65536 --mem-fraction-static 0.82 --reasoning-parser kimi_k3 --tool-call-parser kimi_k3 --port 30000\n2. Use the kimi_k3 tool-call and reasoning parsers in vLLM or SGLang, and validate tool-call arguments against the schema, since vLLM's recipe says K3 sometimes emits a format its parser doesn't expect\n3. Pass the whole assistant message back every turn, reasoning_content and tool_calls included, or output becomes unstable\n4. Thinking is always on; set reasoning_effort to low or high when max is too slow\n5. Sampling per the card is temperature 1.0, with top-p 0.95 for single-step tasks and 1.0 for agentic ones\n6. Don't hand an ongoing session from another model to K3, and state boundaries in the system prompt, since Moonshot warns it can act on its own when intent is unclear\n7. Without the hardware, call it through Moonshot's API or an inference provider listed on its Hugging Face page\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## Questions\n\n### Which is better for AI agents, Kimi K3 or MiMo-V2.6-Flash-RL?\n\nKimi K3 scores 62.4 (B) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 1 of 4 scored categories. MiMo-V2.6-Flash-RL leads on reach, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-flash-rl.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kimi-k3\", \"b\": \"mimo-v2-6-flash-rl\"}`. From a terminal: `anchor compare kimi-k3 mimo-v2-6-flash-rl`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json\n\n## Other comparisons with Kimi K3 or MiMo-V2.6-Flash-RL\n\n- [Agents-A1-4B vs Kimi K3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.md)\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- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.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- [Apodex-1.1-mini vs Kimi K3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3.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- [Arete-27B vs Kimi K3](https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.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- [BigBang-v1 vs Kimi K3](https://www.anchorterminal.com/compare/bigbang-v1-vs-kimi-k3.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- [Carbon-X vs Kimi K3](https://www.anchorterminal.com/compare/carbon-x-vs-kimi-k3.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- [Cola2 vs Kimi K3](https://www.anchorterminal.com/compare/cola2-vs-kimi-k3.md)\n- [Cola2 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.md)\n- [Cosmos-Reason2-2B vs Kimi K3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kimi-k3.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- [Darwin-36B-Opus vs Kimi K3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kimi-k3.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- [DeepSeek-V3.2 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kimi-k3.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 V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.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-Flash-0731 vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kimi-k3.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- [Devstral Small 2 vs Kimi K3](https://www.anchorterminal.com/compare/devstral-small-2-vs-kimi-k3.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- [DiffusionGemma 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kimi-k3.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- [EXAONE 4.5 33B vs Kimi K3](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kimi-k3.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- [Gemma 4 12B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kimi-k3.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 26B A4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kimi-k3.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 31B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kimi-k3.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 E2B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kimi-k3.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 E4B IT vs Kimi K3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kimi-k3.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- [GLM-4.5-Air vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.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-4.7-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kimi-k3.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-5.3-Flash vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kimi-k3.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 vs Kimi K3](https://www.anchorterminal.com/compare/glm-5-3-vs-kimi-k3.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- [gpt-oss-120b vs Kimi K3](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kimi-k3.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-20b vs Kimi K3](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kimi-k3.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- [Granite 4.2 30B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kimi-k3.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 3B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kimi-k3.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 8B vs Kimi K3](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kimi-k3.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- [Hy-Memory-30B-A3B vs Kimi K3](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kimi-k3.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- [Hy3 vs Kimi K3](https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.md)\n- [Hy3 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-flash-rl.md)\n- [Inkling-Small vs Kimi K3](https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3.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 vs Kimi K3](https://www.anchorterminal.com/compare/inkling-vs-kimi-k3.md)\n- [Inkling vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/inkling-vs-mimo-v2-6-flash-rl.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kimi K3](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kimi-k3.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- [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 vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.md)\n- [Kimi K3 vs Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.md)\n- [Kimi K3 vs Kronos](https://www.anchorterminal.com/compare/kimi-k3-vs-kronos.md)\n- [Kimi K3 vs Laguna M.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-m-1.md)\n- [Kimi K3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-s-2-1.md)\n- [Kimi K3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kimi-k3-vs-laguna-xs-2-1.md)\n- [Kimi K3 vs LBS-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-lbs-27b.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-1-7b.md)\n- [Kimi K3 vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-audio-2b.md)\n- [Kimi K3 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-omni-2b.md)\n- [Kimi K3 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm3-vl-2b.md)\n- [Kimi K3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-0-tiny.md)\n- [Kimi K3 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-1-flash-1019.md)\n- [Kimi K3 vs LongCat-2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-longcat-2-5.md)\n- [Kimi K3 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/kimi-k3-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kimi K3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-o-4-5.md)\n- [Kimi K3 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-1b.md)\n- [Kimi K3 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kimi K3 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-minicpm5-2b.md)\n- [Kimi K3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-5.md)\n- [Kimi K3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m2-7.md)\n- [Kimi K3 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kimi-k3-vs-minimax-m3-1-flash.md)\n- [Kimi K3 vs Mistral Large 4](https://www.anchorterminal.com/compare/kimi-k3-vs-mistral-large-4.md)\n- [Kimi K3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kimi-k3-vs-muse-glimmer-30b.md)\n- [Kimi K3 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-2-3b.md)\n- [Kimi K3 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kimi-k3-vs-nanbeige-4-3-33b.md)\n- [Kimi K3 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kimi K3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kimi K3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-30b-a3b.md)\n- [Kimi K3 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-4b.md)\n- [Kimi K3 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Kimi K3 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.md)\n- [Kimi K3 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-ultra-550b-a55b.md)\n- [Kimi K3 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-north-mini-code-1-0.md)\n- [Kimi K3 vs occamy-1.0](https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.md)\n- [Kimi K3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kimi-k3-vs-olmo-3-7b-think.md)\n- [Kimi K3 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-122b.md)\n- [Kimi K3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-35b-a3b.md)\n- [Kimi K3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-397b.md)\n- [Kimi K3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.md)\n- [Kimi K3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen-agentworld-35b-a3b.md)\n- [Kimi K3 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Kimi K3 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-4b-instruct-2507.md)\n- [Kimi K3 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-0-8b.md)\n- [Kimi K3 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-122b-a10b.md)\n- [Kimi K3 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-2b.md)\n- [Kimi K3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-397b-a17b.md)\n- [Kimi K3 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-4b.md)\n- [Kimi K3 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-5-9b.md)\n- [Kimi K3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-27b.md)\n- [Kimi K3 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-6-35b-a3b.md)\n- [Kimi K3 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-27b.md)\n- [Kimi K3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-8-flash-next.md)\n- [Kimi K3 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-coder-next.md)\n- [Kimi K3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-2b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Kimi K3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/kimi-k3-vs-qwen3-vl-32b-instruct.md)\n- 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