{ "data": { "a": { "slug": "inkling-small", "name": "Inkling-Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling-Small is Thinking Machines Lab's open-weight mixture-of-experts model, 276B total and 12B active parameters, taking text, images and audio and returning text, with adjustable thinking effort, published in BF16 and NVFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling-small", "markdownUrl": "https://www.anchorterminal.com/tools/inkling-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-small.json", "repo": "https://huggingface.co/thinkingmachines/Inkling-Small", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 531.9 GB for the BF16 files and 170.8 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens and $1.20 per million output 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 (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-small/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 66.5, "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": 1197, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.", "bestFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "strengths": [ "Thinking Machines answered 8 of the 9 questions in its discussions", "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request from none to the maximum, several tool calls a turn and a 1M-token context", "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers", "A training-content summary with sources, mix and a July 2026 collection cut-off", "Thinking Machines' own Tinker hosts it as beta serverless inference" ], "weaknesses": [ "The smallest build Thinking Machines publishes needs about 205 GB", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "No release of ggml-org's llama.cpp, Ollama or LM Studio runs it", "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance", "Structured output isn't documented, and the card gives no dated knowledge cut-off" ], "agentNotes": [ "Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4", "Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh", "Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card", "Use guided decoding or validate JSON yourself, since structured output isn't documented", "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.5 } ], "editorialScores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "provenanceScore": 67, "checked": { "categories": { "capability": "2026-10-21", "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": "# per SGLang's cookbook, NVFP4 on 8 Blackwell GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 \\\n --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-Small-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling-small" }, "sameCompany": [ "inkling" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "276B", "active": "12B", "context": 1048576, "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images and audio in, and thinking effort set per request.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Inkling-Small-NVFP4", "url": "https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 170.8, "memoryGB": 256, "format": "safetensors", "note": "NVFP4 experts with FP8 and BF16 parts. Thinking Machines says W4A4 needs NVIDIA SM100 or newer, and W4A16 runs on H200" }, { "name": "Inkling-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-Small", "who": "publisher", "precision": "bf16", "sizeGB": 531.9, "memoryGB": 1024, "format": "safetensors" } ], "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "runBuild": "Inkling-Small-NVFP4", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines", "others": [ { "name": "Inkling", "url": "https://huggingface.co/thinkingmachines/Inkling", "params": "952.38B (Hub safetensors count; the name states none)", "listing": "https://www.anchorterminal.com/tools/inkling" } ], "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "thinkingmachines.ai's registration date wasn't read on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling-Small/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Thinking Machines' model card page and launch post link the thinkingmachines organisation on Hugging Face, which isn't verified by Hugging Face.", "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 12 commits from 27 to 31 July 2026, an upload, card updates and community evaluation results.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, and the site publishes a coordinated vulnerability disclosure policy that leaves model safety to a separate incident contact." ], "score": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-small.json" }, "answer": "Inkling-Small scores 66.5 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach.", "b": { "slug": "qwopus3-8-27b-flash-v2", "name": "Qwopus3.8-27B-Flash-V2", "vendor": "Jackrong", "vendorUrl": "https://huggingface.co/Jackrong", "kind": "weights", "category": "open-weights", "summary": "Qwopus3.8-27B-Flash-V2 is Jackrong's open-weight 27B fine-tune of Qwen3.8-27B, by way of his Qwopus3.8-27B-Flash, trained for shorter reasoning in agent loops, published in BF16 and GGUF with a multi-token prediction head under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2", "markdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json", "repo": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "license": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2" }, { "registry": "huggingface", "name": "Jackrong/Qwopus3.8-27B-Flash-V2-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, the same licence as the Qwen3.8-27B base. Nothing is hosted. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 15.4 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/Jackrong/Qwopus3.8-27B-Flash-V2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "fine-tune", "apache-2.0", "safetensors", "gguf", "mtp", "self-hosted" ], "lastRelease": "2026-09-21", "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": 1197, "categoryRank": 39, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.", "bestFor": "Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.", "strengths": [ "Jackrong's 4-bit GGUFs fit 24 GB, with many precisions up to BF16", "The base's architecture, so released vLLM, SGLang, Transformers and llama.cpp run it", "Apache 2.0 through the whole chain, with no gate or account", "Safetensors and GGUF only, with a checksum on every file", "Bundled MTP head for self-speculative decoding, and reasoning effort set per request", "A card that states its limitations and how its own tests were run" ], "weaknesses": [ "Published from a personal account, with no organisation, security contact or safety evaluation", "Training data includes traces derived from Claude and GPT, by the card's own account", "Tool calls switch to a Hermes-style JSON format that the card doesn't document", "Reasoning output location, context and cut-off aren't documented", "Reports of looping and a grammar failure wait without a reply" ], "agentNotes": [ "Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images", "The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp", "The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder", "For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0", "reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error", "Earlier reasoning stays in the context unless preserve_thinking is false", "Cap max_tokens, since a report says it loops more than V1" ], "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": 60, "reach": 85, "risk": 50, "upkeep": 56 }, "provenanceScore": 40, "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 Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja -c 65536\n# or, with vLLM:\nvllm serve Jackrong/Qwopus3.8-27B-Flash-V2 --max-model-len 131072 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser hermes", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.3, \"chat_template_kwargs\": {\"enable_thinking\": true, \"reasoning_effort\": \"xhigh\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwopus3-8-27b-flash-v2" }, "weights": { "trainedBy": "Jackrong", "builtOn": "Jackrong/Qwopus3.8-27B-Flash (fine-tune, Apache 2.0), itself a fine-tune of Qwen/Qwen3.8-27B (Apache 2.0); declared in the card's metadata", "params": "27.8B", "context": 262144, "goodFor": "A 27B vision-language fine-tune for long agent loops and coding, trained to reason less per turn, with an MTP head for speculative decoding.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.42, "memoryGB": 24, "format": "gguf", "note": "Vision needs mmproj-F32.gguf as well (1.84 GB)" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_S.gguf", "who": "publisher", "precision": "q4_k_s", "sizeGB": 15.83, "memoryGB": 24, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 16.81, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 19.54, "memoryGB": 24, "format": "gguf", "note": "The quantisation the card's own tests used, with --spec-type draft-mtp" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 22.43, "memoryGB": 32, "format": "gguf" }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29.05, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Qwopus3.8-27B-Flash-V2", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2", "who": "publisher", "precision": "bf16", "sizeGB": 55.58, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash-V2-GGUF/blob/main/Qwopus3.8-27B-Flash-V2-MTP-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 54.66, "memoryGB": 96, "format": "gguf", "note": "Jackrong also publishes Q3_K_L, Q3_K_M, Q3_K_S and Q2_K files (10.9 to 14.6 GB), below 4-bit precision" } ], "run": "llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Qwopus3.8-27B-Flash-V2-MTP-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Jackrong", "family": "Qwopus3", "url": "https://www.anchorterminal.com/open-weights/#jackrong", "others": [ { "name": "Qwopus3.5-9B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-v3", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-0.8B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-0.8B-v3", "params": "0.8B (873,438,784 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-27B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-27B-v3.5", "params": "27B (27,356,728,560 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-2B-v3", "url": "https://huggingface.co/Jackrong/Qwopus3.5-2B-v3", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-v3.5", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-v3.5", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus-GLM-18B-Healed", "url": "https://huggingface.co/Jackrong/Qwopus-GLM-18B-Healed", "params": "18B (16,572,666,096 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v1-preview", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v1-preview", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-v1", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-v1", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-v2", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-v2", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-9B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Qwopus3.5-4B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.5-4B-Coder", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-35B-A3B-Coder", "url": "https://huggingface.co/Jackrong/Qwopus3.6-35B-A3B-Coder", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Qwopus3.6-27B-Fusion-BF16", "url": "https://huggingface.co/Jackrong/Qwopus3.6-27B-Fusion-BF16", "params": "27B (27,320,697,856 by the Hub's GGUF count)" }, { "name": "Qwopus3.8-27B-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3.8-27B-Flash", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Qwopus3-5-9B-Coder-Flash", "url": "https://huggingface.co/Jackrong/Qwopus3-5-9B-Coder-Flash", "params": "9B (9,653,104,368 by the Hub's safetensors count)" } ], "olderCount": 53, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Jackrong, an individual Hugging Face account", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Jackrong is a personal account (158 models, 5,852 followers), not an organisation, and no website or GitHub link is on the card. The card sends questions to a tester's X account.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The repository has one commit (21 September 2026); the card describes what changed from the first Flash release.", "No security contact or disclosure policy was found." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Thinking Machines Lab", "b": "Jackrong", "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": "Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-09-21", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.5/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling-Small scores 66.5 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. 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"https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-small.json", "title": "Apodex-1.1-mini vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2.json", "title": "Apodex-1.1-mini vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small.json", "title": "BigBang-v1 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2.json", "title": "BigBang-v1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling-small.json", "title": 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"json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2.json", "title": "Gemma 4 E4B IT vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small.json", "title": "GLM-4.5-Air vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2.json", "title": "GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small.json", "title": "GLM-4.7-Flash vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwopus3-8-27b-flash-v2.json", "title": 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}, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-inkling-small.json", "title": "gpt-oss-120b vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2.json", "title": "gpt-oss-120b vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling-small.json", "title": "gpt-oss-20b vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwopus3-8-27b-flash-v2.json", "title": "gpt-oss-20b vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling-small.json", "title": "Granite 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"https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-27B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3.8-Flash-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b.json", "title": "Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next" }, { "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" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "qwopus3-8-27b-flash-v2", "inkling-small": 55, "key": "reach", "name": "Reach", "qwopus3-8-27b-flash-v2": 85, "weight": 25 }, { "by": 20, "edge": "inkling-small", "inkling-small": 70, "key": "risk", "name": "Risk", "qwopus3-8-27b-flash-v2": 50, "weight": 20 }, { "by": 25, "edge": "inkling-small", "inkling-small": 85, "key": "interface", "name": "Agent interface", "qwopus3-8-27b-flash-v2": 60, "weight": 10 }, { "by": 26, "edge": "inkling-small", "inkling-small": 82, "key": "upkeep", "name": "Documentation and upkeep", "qwopus3-8-27b-flash-v2": 56, "weight": 10 } ], "summary": "Inkling-Small scores 66.5 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "inkling-small": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.", "qwopus3-8-27b-flash-v2": "A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2", "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.min.md" }, "markdown": "Inkling-Small scores 66.5 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach. Both do open-weight models you run yourself.\n\n- Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.json\n- Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Inkling-Small (B)\n\nGood for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.\n\nAhead on:\n- Risk, 70 against 50\n- Agent interface, 85 against 60\n- Documentation and upkeep, 82 against 56\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.5 out of 5\n\nWatch for: The smallest build Thinking Machines publishes needs about 205 GB\n\n### Qwopus3.8-27B-Flash-V2 (C)\n\nGood for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU.\n\nAhead on:\n- Reach, 85 against 55\n\nWatch for: Published from a personal account, with no organisation, security contact or safety evaluation\n\n\n## Score by category\n\n| Category | Weight | Inkling-Small | Qwopus3.8-27B-Flash-V2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 85 | Qwopus3.8-27B-Flash-V2 +30 |\n| Risk | 20% (20 this run) | 70 | 50 | Inkling-Small +20 |\n| Agent interface | 10% (10 this run) | 85 | 60 | Inkling-Small +25 |\n| Documentation and upkeep | 10% (10 this run) | 82 | 56 | Inkling-Small +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **66.5 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Inkling-Small | Qwopus3.8-27B-Flash-V2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | Jackrong |\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 | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-09-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 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 | 3.5/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.\n\n**Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.\n\n## Before you call either\n\n### Inkling-Small\n\n1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4\n2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself\n3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh\n4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card\n7. Use guided decoding or validate JSON yourself, since structured output isn't documented\n8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n### Qwopus3.8-27B-Flash-V2\n\n1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images\n2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp\n3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder\n4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0\n5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error\n6. Earlier reasoning stays in the context unless preserve_thinking is false\n7. Cap max_tokens, since a report says it loops more than V1\n\n## Questions\n\n### Which is better for AI agents, Inkling-Small or Qwopus3.8-27B-Flash-V2?\n\nInkling-Small scores 66.5 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 3 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling-small\", \"b\": \"qwopus3-8-27b-flash-v2\"}`. From a terminal: `anchor compare inkling-small qwopus3-8-27b-flash-v2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-small.json and https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.json\n\n## Other comparisons with Inkling-Small or Qwopus3.8-27B-Flash-V2\n\n- [Agents-A1-4B vs Inkling-Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.md)\n- [Agents-A1-4B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apertus v1.5 8B vs Inkling-Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.md)\n- [Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apodex-1.1-mini vs Inkling-Small](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-small.md)\n- [Apodex-1.1-mini vs 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"https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Inkling-Small vs Qwopus3.8-27B-Flash-V2", "url": "" } ], "description": "Inkling-Small scores 66.5 (B) to Qwopus3.8-27B-Flash-V2's 61 (C) for open-weight models you run yourself. 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