{ "data": { "a": { "slug": "apodex-1-1-mini", "name": "Apodex-1.1-mini", "vendor": "Apodex AI", "vendorUrl": "https://www.apodex.com", "kind": "weights", "category": "open-weights", "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini", "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json", "dns": "apodex-1-1-mini.grade.anchorterminal.com", "repo": "https://huggingface.co/apodex/Apodex-1.1-mini", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-FP8" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-NVFP4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GPTQ-Int4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol. Apodex's hosted API bills by account (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "gptq", "self-hosted" ], "lastRelease": "2026-08-25", "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": 31, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "strengths": [ "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls", "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE", "Apache 2.0 through the chain, with the base's licence file carried and no gate", "Apodex's own API serves the same model", "Safetensors and GGUF only, with checksums on every weight file" ], "weaknesses": [ "No safety results, and agent guidance only from the harness", "Apodex answered none of the four discussions", "The card states no parameters, architecture, limitations or knowledge cut-off", "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default", "Structured output isn't documented" ], "agentNotes": [ "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed", "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file", "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks", "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768", "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block", "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "provenanceScore": 47, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n --tool-call-parser qwen3_coder --reasoning-parser qwen3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apodex-1-1-mini" }, "weights": { "trainedBy": "Apodex AI", "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Apodex-1.1-mini-NVFP4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 24.3, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang" }, { "name": "Apodex-1.1-mini-GPTQ-Int4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4", "who": "publisher", "precision": "int4", "sizeGB": 24.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "The card serves it with SGLang" }, { "name": "apodex1.1-35b-Q4_K_M-MTP", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server" }, { "name": "Apodex-1.1-mini-FP8", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Apodex-1.1-mini", "url": "https://huggingface.co/apodex/Apodex-1.1-mini", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3", "runBuild": "Apodex-1.1-mini", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Apodex AI (apodex)", "family": "Apodex-1", "url": "https://www.anchorterminal.com/open-weights/#apodex", "others": [ { "name": "Apodex-1.0-mini", "url": "https://huggingface.co/apodex/Apodex-1.0-mini", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-4B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-2B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-0.8B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT", "params": "0.8B (873,438,784 by the Hub's safetensors count)" } ], "unrestrictedCopies": 2 }, "area": "models", "provenance": { "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)", "domain": "apodex.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.", "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.", "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.", "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.", "unchecked: the domain's registration date." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json", "live": { "slug": "apodex-1-1-mini", "securityTxt": { "url": "https://apodex.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:39.653671365Z" }, "domain": { "domain": "apodex.com", "registered": "2018-10-10", "source": "https://rdap.verisign.com/com/v1/domain/apodex.com", "checkedAt": "2026-11-08T14:48:24.412328563Z" }, "updatedAt": "2026-11-10T16:09:39.653671365Z" } }, "answer": "Apodex-1.1-mini scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.", "b": { "slug": "kimi-k3-1-pro", "name": "Kimi K3.1 Pro", "vendor": "Moonshot AI", "vendorUrl": "https://www.moonshot.ai", "kind": "weights", "category": "open-weights", "summary": "Kimi K3.1 Pro is Moonshot AI's open-weight multimodal mixture-of-experts model, post-trained from Kimi K3, with 2.8T parameters, 104B active and a 1M-token context, published in MXFP4 under the modified-MIT Kimi K3.1 Pro Licence.", "url": "https://www.anchorterminal.com/tools/kimi-k3-1-pro", "markdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json", "dns": "kimi-k3-1-pro.grade.anchorterminal.com", "repo": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro", "license": "Kimi K3.1 Pro License (modified MIT)", "transports": [], "packages": [ { "registry": "huggingface", "name": "moonshotai/Kimi-K3.1-Pro" } ], "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, commercial use included, under the Kimi K3.1 Pro 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 2026-11-10).", "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/moonshotai/Kimi-K3.1-Pro", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "modified-mit", "safetensors", "mxfp4", "self-hosted" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 48.6, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 109, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 40, "risk": 50, "upkeep": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.", "bestFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API", "strengths": [ "Published weights for a model Moonshot calls its most capable, with a 1M-token context and images in", "Free commercial use under a modified MIT licence, ungated, with a hash on every file", "Moonshot's API and Together serve the same model for agents without the hardware", "reasoning_effort per request and several tool calls a turn", "vLLM and SGLang both publish serving recipes for it" ], "weaknesses": [ "1,561 GB of files, so it needs a node of eight large accelerators at least", "Only llama.cpp has the model code in a release, and no GGUF is published", "No chat template, and prompts are built by code from the repository", "No limitations, safety guidance or security contact on the card, and no independent safety evaluation yet", "None of the newest discussions has a reply from Moonshot" ], "agentNotes": [ "Use Moonshot's API or Together unless you have a node of eight B300-class accelerators", "With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`", "Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires", "Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off", "Kimi K3's model code doesn't load it, because the attention layers changed", "Validate JSON output yourself, since the card doesn't document structured output for the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 48.6 } ], "editorialScores": { "interface": 65, "reach": 40, "risk": 50, "upkeep": 51 }, "provenanceScore": 70, "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": "# vLLM, per its Kimi-K3.1-Pro recipe linked from the card (vLLM 0.34.0 or later, two GB300 trays)\nvllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code \\\n --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP \\\n --load-format fastsafetensors --enable-auto-tool-choice \\\n --tool-call-parser kimi_k3 --reasoning-parser kimi_k3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"moonshotai/Kimi-K3.1-Pro\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"high\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kimi-k3-1-pro" }, "sameCompany": [ "kimi-k3" ], "weights": { "trainedBy": "Moonshot AI", "builtOn": "Kimi K3, post-trained, per the card. Not declared in the metadata", "params": "2.8T", "active": "104B", "context": 1048576, "goodFor": "Large multimodal agent model for coding, research and long tool-calling sessions, text and images in, with always-on reasoning you can budget.", "runtimes": [ "llama.cpp" ], "builds": [ { "name": "Kimi-K3.1-Pro", "url": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro", "who": "publisher", "precision": "mxfp4", "sizeGB": 1561.3, "memoryGB": 2048, "format": "safetensors", "note": "Trained in MXFP4, the only build Moonshot publishes. vLLM's recipe needs 0.34.0 or later" } ], "run": "vllm serve moonshotai/Kimi-K3.1-Pro --trust-remote-code --tensor-parallel-size 8 --nnodes 2 --node-rank 0 --master-addr $HEAD_IP --load-format fastsafetensors --enable-auto-tool-choice --tool-call-parser kimi_k3 --reasoning-parser kimi_k3", "runBuild": "Kimi-K3.1-Pro", "checked": "2026-11-10", "minMemoryGB": 2048 }, "family": { "lab": "Moonshot AI (Kimi)", "family": "Kimi K3.1", "url": "https://www.anchorterminal.com/open-weights/#moonshot", "olderCount": 18 }, "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 gave moonshot.ai a registration date of 15 December 2017, read 24 October 2026 for the Kimi K3 listing.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/moonshotai/Kimi-K3.1-Pro/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Moonshot AI. The Kimi Platform's terms name Moonshot AI PTE. LTD. as the party for its API, as the Kimi K3 listing records.", "Nothing is hosted, so the Kimi K3.1 Pro Licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, two commits, the first on 2 November 2026 and an update to encoding_k3.py on 5 November.", "No security.txt was found on moonshot.ai or kimi.com for the Kimi K3 listing on 24 October 2026. unchecked on 10 November." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kimi-k3-1-pro.json" }, "edges": [ { "by": 45, "category": "reach", "leads": "apodex-1-1-mini", "name": "Reach", "reasons": { "a": "Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.", "b": "Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40." }, "scores": { "a": 85, "b": 40 } }, { "by": 20, "category": "interface", "leads": "apodex-1-1-mini", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.", "b": "Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65." }, "scores": { "a": 85, "b": 65 } }, { "by": 15, "category": "risk", "leads": "apodex-1-1-mini", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.", "b": "File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50." }, "scores": { "a": 65, "b": 50 } }, { "by": 13, "category": "upkeep", "leads": "kimi-k3-1-pro", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.", "b": "Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51." }, "scores": { "a": 38, "b": 51 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Apodex AI", "b": "Moonshot AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Kimi K3.1 Pro License (modified MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-25", "b": "2026-11-05", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Apodex-1.1-mini scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.", "question": "Which is better for AI agents, Apodex-1.1-mini or Kimi K3.1 Pro?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 40", "Risk, 65 against 50", "Agent interface, 85 against 65" ], "also": null, "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "slug": "apodex-1-1-mini", "watchFor": "No safety results, and agent guidance only from the harness" }, { "aheadOn": [ "Documentation and upkeep, 51 against 38" ], "also": null, "goodFor": "Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API", "slug": "kimi-k3-1-pro", "watchFor": "1,561 GB of files, so it needs a node of eight large accelerators at least" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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{ "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-7.json", "title": "Kimi K3.1 Pro vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash.json", "title": "Kimi K3.1 Pro vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4.json", "title": "Kimi K3.1 Pro vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b.json", "title": "Kimi K3.1 Pro vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nanbeige-4-2-3b.json", "title": "Kimi K3.1 Pro vs 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"https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct.json", "title": "Kimi K3.1 Pro vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2.json", "title": "Kimi K3.1 Pro vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint.json", "title": "Kimi K3.1 Pro vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview.json", "title": "Kimi K3.1 Pro vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview.json", "title": "Kimi K3.1 Pro vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b.json", "title": "Kimi K3.1 Pro vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b.json", "title": "Kimi K3.1 Pro vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.json", "title": "Kimi K3.1 Pro vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b.json", "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kimi K3.1 Pro vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b.json", "title": "Kimi K3.1 Pro vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.json", "title": "Kimi K3.1 Pro vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b.json", "title": "Kimi K3.1 Pro vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro.json", "title": "Kimi K3 vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-kimi-k3-1-pro" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apodex-1-1-mini": 85, "by": 45, "edge": "apodex-1-1-mini", "key": "reach", "kimi-k3-1-pro": 40, "name": "Reach", "weight": 25 }, { "apodex-1-1-mini": 65, "by": 15, "edge": "apodex-1-1-mini", "key": "risk", "kimi-k3-1-pro": 50, "name": "Risk", "weight": 20 }, { "apodex-1-1-mini": 85, "by": 20, "edge": "apodex-1-1-mini", "key": "interface", "kimi-k3-1-pro": 65, "name": "Agent interface", "weight": 10 }, { "apodex-1-1-mini": 38, "by": 13, "edge": "kimi-k3-1-pro", "key": "upkeep", "kimi-k3-1-pro": 51, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Apodex-1.1-mini scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "kimi-k3-1-pro": "Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro", "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.min.md" }, "markdown": "Apodex-1.1-mini scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #41 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\n- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.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### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Reach, 85 against 40\n- Risk, 65 against 50\n- Agent interface, 85 against 65\n\nWatch for: No safety results, and agent guidance only from the harness\n\n### Kimi K3.1 Pro (D)\n\nGood for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API\n\nAhead on:\n- Documentation and upkeep, 51 against 38\n\nWatch for: 1,561 GB of files, so it needs a node of eight large accelerators at least\n\n\n## Why the edge\n\n### Reach: Apodex-1.1-mini +45\n\n- Apodex-1.1-mini (85): Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.\n- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.\n\n### Agent interface: Apodex-1.1-mini +20\n\n- Apodex-1.1-mini (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.\n- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.\n\n### Risk: Apodex-1.1-mini +15\n\n- Apodex-1.1-mini (65): File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.\n- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.\n\n### Documentation and upkeep: Kimi K3.1 Pro +13\n\n- Apodex-1.1-mini (38): Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.\n- Kimi K3.1 Pro (51): Model card 11, no limitations, languages or cut-off. Training data 5, one general sentence. Last change 20, a commit on 5 November 2026. Responsiveness 0, none of the 8 newest discussions has a reply from Moonshot. Version policy 5, Kimi K3 keeps its repository and no dated note was found. File health 10, the files agree. 11 + 5 + 20 + 0 + 5 + 10 = 51.\n\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Kimi K3.1 Pro | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 40 | Apodex-1.1-mini +45 |\n| Risk | 20% (20 this run) | 65 | 50 | Apodex-1.1-mini +15 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Apodex-1.1-mini +20 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 51 | Kimi K3.1 Pro +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Kimi K3.1 Pro |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | Moonshot AI |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Kimi K3.1 Pro License (modified MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 2026-11-05 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.3/5 (4) | none |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\n\n**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.\n\n## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\n\n### Kimi K3.1 Pro\n\n1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators\n2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`\n3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires\n4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off\n5. Kimi K3's model code doesn't load it, because the attention layers changed\n6. Validate JSON output yourself, since the card doesn't document structured output for the weights\n\n## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Kimi K3.1 Pro?\n\nApodex-1.1-mini scores 61 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 3 of 4 scored categories. Kimi K3.1 Pro leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kimi-k3-1-pro.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"kimi-k3-1-pro\"}`. 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