{ "data": { "a": { "slug": "minimax-m3-1-flash", "name": "MiniMax-M3.1-Flash", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "weights", "category": "open-weights", "summary": "MiniMax-M3.1-Flash is MiniMax's open-weight mixture-of-experts model for coding, agent work, computer use and cybersecurity, about 428B parameters with 23B active, taking text, images and video with up to 1M tokens of context.", "url": "https://www.anchorterminal.com/tools/minimax-m3-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json", "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "license": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "MiniMaxAI/MiniMax-M3.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for MiniMaxAI/MiniMax-M3.1-Flash (checked 2026-10-24).", "pricing": "free", "pricingNotes": "Free to download. The MiniMax Community License allows commercial use if the product shows \"Built with MiniMax M3.1-Flash\" and MiniMax gets a one-time notice by email; products earning more than $20M a year need MiniMax's prior written authorisation. Running it yourself costs the hardware, 250.1 GB of NVFP4 and MXFP8 weights plus a 2.3 GB DSpark draft. MiniMax's API sells the same-named model at $0.15 in and $0.60 out per million tokens (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": "2026-10-24" }, "docsUrl": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "video", "reasoning", "tool-calling", "coding", "nvfp4", "mxfp8", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-23", "graded": true, "anchor": { "graded": true, "score": 50.5, "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": 1210, "categoryRank": 77, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 64, "interface": 70, "reach": 40, "risk": 35, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-24" }, "negative": 0, "verdict": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.", "bestFor": "Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.", "strengths": [ "Quantisation-aware NVFP4 and MXFP8 release, about 250 GB for 428B parameters, with a DSpark draft included", "1,048,576 tokens of context in the config, with sparse attention in every layer", "Several tool calls in one block, and reasoning_effort from low to max per request", "Text, image and video input in one model", "No gate or account, files in MiniMax's own organisation with a SHA-256 for every shard" ], "weaknesses": [ "No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did", "About 300 GB with a fifth added, more than any 128 GB or 256 GB machine", "Commercial use needs a Built with line and a notice to MiniMax, and authorisation above $20M yearly revenue", "No safety evaluation, agent guidance or security contact, alongside strong claimed offensive security capability", "The card omits limitations, languages, training data and a knowledge cut-off, and no discussion has a MiniMax reply" ], "agentNotes": [ "Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue", "On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144", "Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code", "--block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128", "Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95", "On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off", "For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage", "Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "capability": 64, "interface": 70, "reach": 40, "risk": 35, "upkeep": 41 }, "provenanceScore": 55, "checked": { "categories": { "capability": "2026-10-24", "interface": "2026-10-24", "reach": "2026-10-24", "risk": "2026-10-24", "upkeep": "2026-10-24" }, "last": "2026-10-24", "oldest": "2026-10-24", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM's recipe, 8 x H100 or H200 (nightly image; no pip release supports it on 24 October 2026)\ndocker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash \\\n --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager \\\n --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin \\\n --max-model-len 262144 --reasoning-parser minimax_m3 \\\n --enable-auto-tool-choice --tool-call-parser minimax_m3", "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"model\": \"MiniMaxAI/MiniMax-M3.1-Flash\", \"temperature\": 1.0, \"top_p\": 0.95, \"chat_template_kwargs\": {\"reasoning_effort\": \"medium\"}, \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/minimax-m3-1-flash" }, "sameCompany": [ "minimax-api", "minimax-video", "minimax-voice-cloning", "minimax-code", "minimax-m2-5", "minimax-m2-7" ], "weights": { "trainedBy": "MiniMax", "builtOn": "MiniMaxAI/MiniMax-M3 (the card says M3.1-Flash builds on M3 and keeps its mixture-of-experts architecture; not declared as base_model in the metadata; M3 carries the same MiniMax Community License)", "params": "428B", "context": 1048576, "goodFor": "Mixture-of-experts model for coding, agent harnesses, computer use and security work, with images and video in, about 1M tokens of context and parallel tool calls, on a 4 to 8 GPU server.", "runtimes": [ "llama.cpp" ], "builds": [ { "name": "MiniMax-M3.1-Flash", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash", "who": "publisher", "precision": "nvfp4", "sizeGB": 250.12, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Quantisation-aware trained release, routed experts in NVFP4 (group 16) and dense and attention layers in MXFP8, 250,116,419,880 bytes in 48 shards summed from the Hub's file list, plus a 2.30 GB DSpark draft. The only build MiniMax publishes. vLLM and SGLang ran it only from pre-release images on 24 October 2026" } ], "run": "docker run --gpus all --ipc=host -p 8000:8000 -v ~/.cache/huggingface:/root/.cache/huggingface vllm/vllm-openai:minimax-m31-flash MiniMaxAI/MiniMax-M3.1-Flash --tensor-parallel-size 8 --block-size 128 --disable-hybrid-kv-cache-manager --kv-cache-dtype fp8 --attention-config '{\"indexer_kv_dtype\": \"fp8\"}' --moe-backend marlin --max-model-len 262144 --reasoning-parser minimax_m3 --enable-auto-tool-choice --tool-call-parser minimax_m3", "runBuild": "MiniMax-M3.1-Flash", "checked": "2026-10-24", "minMemoryGB": 512 }, "family": { "lab": "MiniMax", "family": "MiniMax-M3", "url": "https://www.anchorterminal.com/open-weights/#minimax", "others": [ { "name": "MiniMax-M3", "url": "https://huggingface.co/MiniMaxAI/MiniMax-M3", "params": "~428B total, ~23B activated" } ], "olderCount": 7 }, "area": "models", "provenance": { "legalEntity": "MiniMax (named in the licence's copyright line; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)", "domain": "minimax.io", "domainRegistered": "2021-03-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-24", "notes": [ "The licence reads Copyright (c) 2026 MiniMax. The platform terms' legal entity and the domain's registration date are as recorded on the minimax-api and minimax-m2-7 listings.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository's history is one commit, \"Init\", on 23 October 2026.", "www.minimax.io/.well-known/security.txt redirects to the home page on 24 October 2026, and MiniMax has no public GitHub repository for M3.1 Flash, so there is no security.txt or SECURITY.md.", "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI, and the card links MiniMax's site, API and GitHub organisation, so the repository is the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m3-1-flash.json", "live": { "slug": "minimax-m3-1-flash", "securityTxt": { "url": "https://minimax.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:28:08.450653621Z" }, "updatedAt": "2026-10-25T15:28:08.450653621Z" } }, "answer": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "thinkingcap-qwen3-8-27b", "name": "ThinkingCap (Qwen3.8 27B)", "vendor": "BottleCap AI", "vendorUrl": "https://bottlecapai.com", "kind": "weights", "category": "open-weights", "summary": "ThinkingCap-Qwen3.8-27B is BottleCap AI's fine-tune of Alibaba's Qwen3.8-27B that shortens its reasoning traces, a 27B dense vision-language model on Hugging Face under PolyForm Small Business 1.0.0, with a gated main repository.", "url": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json", "repo": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "license": "PolyForm-Small-Business-1.0.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-FP8" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw" } ], "auth": "none", "authNotes": "The BF16 and FP8 repositories are gated (gated auto). A Hugging Face account must accept the card's conditions and give a name, a company and an email before the files open, with automatic approval. The GGUF, NVFP4, NVFP4A4-AWQ, MLX and RCO GGUF repositories were not gated on 21 October 2026, though their cards carry the same request form in their metadata (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free for personal non-commercial use and for companies with fewer than 100 people and under 1,000,000 USD (2019, inflation-adjusted) revenue, under PolyForm Small Business 1.0.0 plus BottleCap's personal-use grant. Larger organisations need a separate commercial licence from BottleCap AI. Running it costs the hardware, 55.6 GB for BF16 and 15.5 GB for the smallest 4-bit GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "gated", "source-available", "safetensors", "gguf", "fp8", "nvfp4", "mlx", "self-hosted" ], "lastRelease": "2026-10-06", "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": 1210, "categoryRank": 46, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.", "bestFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "strengths": [ "Same architecture and template as Qwen3.8-27B, so released vLLM, SGLang, Transformers and llama.cpp run it", "BottleCap publishes FP8, NVFP4, MLX and GGUF builds, the IQ4_XS needing about 19 GB", "Per-request reasoning effort, several tool calls a turn and 262,144 tokens of context", "The card documents its own evaluation method with seeds, intervals and paired quantisation checks", "Safetensors only, in a verified organisation" ], "weaknesses": [ "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue", "The BF16 and FP8 repositories are gated behind contact details", "No safety results, agent guidance, security contact or training-data description", "No hosted API or inference provider", "Every accuracy and token-saving figure comes from BottleCap's own harness" ], "agentNotes": [ "Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap", "For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB", "On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`", "Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error", "Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop", "The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "provenanceScore": 47, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja\n# or the gated BF16 weights, after accepting the card's conditions:\nvllm serve bottlecapai/ThinkingCap-Qwen3.8-27B --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/thinkingcap-qwen3-8-27b" }, "weights": { "trainedBy": "BottleCap AI", "builtOn": "Qwen/Qwen3.8-27B (Apache 2.0), declared in the card's metadata (base_model, base_model_relation finetune) and in NOTICE as the base checkpoint. ThinkingCap's own weights are under PolyForm Small Business 1.0.0 with a personal-use grant, and NOTICE keeps Apache 2.0 for the upstream Qwen materials", "params": "27B", "context": 262144, "goodFor": "Qwen3.8-27B with shorter reasoning traces, for agent and reasoning work where thinking tokens cost time, with images and video in.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ThinkingCap-Qwen3.8-27B-IQ4_XS", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Image input needs the 0.93 GB mmproj file from the same repository" }, { "name": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-RCO-4.2bpw", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw", "who": "publisher", "precision": "mixed-4.2bpw", "sizeGB": 14.3, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Mixed precision averaging 4.19 bits per weight, with the feed-forward weights at 3.8 to 3.9 bits, so it doesn't count as a 4-bit build for the memory line" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 20.6, "memoryGB": 32, "format": "safetensors", "note": "Not gated. Weight-only NVFP4 for vLLM on Hopper (Marlin) and Blackwell" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.4, "memoryGB": 32, "format": "safetensors", "note": "Not gated. NVFP4 weights and activations, Blackwell only" }, { "name": "ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 22.8, "memoryGB": 32, "format": "mlx", "runsOn": [ "mlx-vlm" ], "note": "Not gated. Mixed 4 and 8-bit DWQ with the vision tower and MTP head (22.0 GB without the MTP files). The card says to use mlx-vlm or oMLX" }, { "name": "ThinkingCap-Qwen3.8-27B-Q6_K", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 23.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-FP8", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Gated like the main repository. Block-wise FP8 for vLLM on Hopper and Blackwell, per the card" }, { "name": "ThinkingCap-Qwen3.8-27B-Q8_0", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "Gated (auto approval after accepting the card's conditions)" }, { "name": "ThinkingCap-Qwen3.8-27B-f16", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 54.7, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja", "runBuild": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "BottleCap AI (bottlecapai)", "family": "ThinkingCap", "url": "https://www.anchorterminal.com/open-weights/#bottlecap", "others": [ { "name": "ThinkingCap-Qwen3.6-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B", "params": "27B (27,356,728,560 by the Hub's safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "BottleCap AI (named in the licence's copyright line)", "domain": "bottlecapai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://bottlecapai.com/privacy-policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation bottlecapai is verified (team plan, 15 members). BottleCap's blog post for this model links the repository.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "bottlecapai.com/.well-known/security.txt returns 404. 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}, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "MiniMax", "b": "BottleCap 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": "MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue)", "b": "PolyForm-Small-Business-1.0.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-23", "b": "2026-10-06", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "name": "Privacy policy last 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"https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano 4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json", "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json", "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "thinkingcap-qwen3-8-27b", "key": "reach", "minimax-m3-1-flash": 40, "name": "Reach", "thinkingcap-qwen3-8-27b": 75, "weight": 25 }, { "by": 10, "edge": "thinkingcap-qwen3-8-27b", "key": "risk", "minimax-m3-1-flash": 35, "name": "Risk", "thinkingcap-qwen3-8-27b": 45, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "minimax-m3-1-flash": 70, "name": "Agent interface", "thinkingcap-qwen3-8-27b": 70, "weight": 10 }, { "by": 12, "edge": "thinkingcap-qwen3-8-27b", "key": "upkeep", "minimax-m3-1-flash": 41, "name": "Documentation and upkeep", "thinkingcap-qwen3-8-27b": 53, "weight": 10 } ], "summary": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "minimax-m3-1-flash": "A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.", "thinkingcap-qwen3-8-27b": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.min.md" }, "markdown": "ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- MiniMax-M3.1-Flash: grade D, 50.5/100, rank #99 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m3-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m3-1-flash.json\n- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #54 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### MiniMax-M3.1-Flash (D)\n\nGood for: Teams with a 4 to 8 GPU server who want an open multimodal agent and coding model with 1M tokens of context and can meet the licence's attribution and notice terms.\n\nWatch for: No released vLLM or SGLang ran it on 24 October 2026; only llama.cpp b11861 from that day did\n\n### ThinkingCap (Qwen3.8 27B) (C)\n\nGood for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.\n\nAhead on:\n- Reach, 75 against 40\n- Risk, 45 against 35\n- Documentation and upkeep, 53 against 41\n\nWatch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue\n\n\n## Score by category\n\n| Category | Weight | MiniMax-M3.1-Flash | ThinkingCap (Qwen3.8 27B) | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 75 | ThinkingCap (Qwen3.8 27B) +35 |\n| Risk | 20% (20 this run) | 35 | 45 | ThinkingCap (Qwen3.8 27B) +10 |\n| Agent interface | 10% (10 this run) | 70 | 70 | even |\n| Documentation and upkeep | 10% (10 this run) | 41 | 53 | ThinkingCap (Qwen3.8 27B) +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **50.5 · D** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | MiniMax-M3.1-Flash | ThinkingCap (Qwen3.8 27B) |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | MiniMax | BottleCap 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 | MiniMax Community License (custom; commercial use needs a \"Built with MiniMax M3.1-Flash\" line and a one-time notice to MiniMax, and MiniMax's written authorisation above $20M yearly revenue) | PolyForm-Small-Business-1.0.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-23 | 2026-10-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.3/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**MiniMax-M3.1-Flash.** A 428B mixture-of-experts model, 23B active, for agent work, coding and computer use, with image and video input, about 1M tokens of context and parallel tool calls. It needs a 4 to 8 GPU server for 250 GB of NVFP4 and MXFP8 weights, and vLLM and SGLang support was pre-release on 24 October 2026. Commercial use needs attribution and notice.\n\n**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.\n\n## Before you call either\n\n### MiniMax-M3.1-Flash\n\n1. Check the use case first; commercial products must show \"Built with MiniMax M3.1-Flash\" and notify MiniMax once, or get written authorisation above $20M yearly revenue\n2. On 8 H100 or H200 GPUs use vLLM's nightly image vllm/vllm-openai:minimax-m31-flash with --tensor-parallel-size 8 --block-size 128 --kv-cache-dtype fp8 --moe-backend marlin --max-model-len 262144\n3. Pass --reasoning-parser minimax_m3 --tool-call-parser minimax_m3 --enable-auto-tool-choice in vLLM, or minimax-m3 for both in SGLang with --trust-remote-code\n4. --block-size 128 is mandatory in vLLM, since MSA's sparse block size is 128\n5. Set effort per request with chat_template_kwargs reasoning_effort (low, medium, high, xhigh, max); sample at temperature 1.0 and top_p 0.95\n6. On Blackwell or MI355X, 4 GPUs serve it with the DSpark draft from dspark/; vLLM's Hopper command leaves DSpark off and turns video input off\n7. For GGUF builds by others, use llama.cpp b11861 or later; earlier builds mis-scale the NVFP4 experts and produce garbage\n8. Without the hardware, call MiniMax-M3.1-Flash on MiniMax's API (minimax-api), whose thinking defaults to max effort\n\n### ThinkingCap (Qwen3.8 27B)\n\n1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap\n2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB\n3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`\n4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error\n5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop\n6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor\n\n## Questions\n\n### Which is better for AI agents, MiniMax-M3.1-Flash or ThinkingCap (Qwen3.8 27B)?\n\nThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against MiniMax-M3.1-Flash's 50.5 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-thinkingcap-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-m3-1-flash\", \"b\": \"thinkingcap-qwen3-8-27b\"}`. 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