{ "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", "dns": "inkling-small.grade.anchorterminal.com", "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": "2026-11-01" }, "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": 67, "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": 1350, "categoryRank": 10, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 87 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "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", "Thinking Machines updated its Inkling renderer in tinker-cookbook on 30 October 2026" ], "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67 } ], "editorialScores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 87 }, "provenanceScore": 72, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "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": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-small.json", "live": { "slug": "inkling-small", "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-11-04T15:53:57.15782482Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-11-01T14:19:04.486860036Z" }, "pages": [ { "url": "https://huggingface.co/thinkingmachines/Inkling-Small/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-04T08:26:49.619943303Z", "changedAt": "2026-11-04T08:26:49.619943303Z", "fingerprint": "6de2b1ecc68e" } ], "updatedAt": "2026-11-04T15:53:57.15782482Z" } }, "answer": "Inkling-Small scores 67 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.", "b": { "slug": "ripple-65b-preview", "name": "Ripple-65B Preview", "vendor": "Ricursive Intelligence", "vendorUrl": "https://www.ricursive.com/ripple", "kind": "weights", "category": "open-weights", "summary": "Ricursive Intelligence's 65B dense model, pretrained from scratch and post-trained for agentic coding, on Hugging Face in BF16 and NVFP4 under Apache 2.0. Its custom architecture needs Ricursive's own model code to run.", "url": "https://www.anchorterminal.com/tools/ripple-65b-preview", "markdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json", "dns": "ripple-65b-preview.grade.anchorterminal.com", "repo": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview" }, { "registry": "huggingface", "name": "ricursiveAI/Ripple-65B-Preview-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the NVFP4 repository is gated (checked 2026-11-04).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 129.6 GB for the BF16 files and 57.2 GB for the NVFP4 build by the Hub's file pages. Ricursive's launch post names Together AI, GMI Cloud and OpenRouter as hosts, but Together's model page says coming soon (checked 2026-11-04).", "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 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-04" }, "docsUrl": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "reasoning", "tool-calling", "coding", "apache-2.0", "safetensors", "remote-code", "self-hosted", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 53.3, "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": 1350, "categoryRank": 78, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.", "bestFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "strengths": [ "Apache 2.0 with nothing added, ungated, from the organisation Ricursive's site links to", "A tools-aware chat template with several calls a turn, and a server that returns reasoning, content and tool calls separately", "Trained for long context at 2M-token sequences, with 1M stated", "Base model and a speculative-decoding draft published beside the instruct model", "Safetensors only, with a checksum on every file" ], "weaknesses": [ "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code", "The smallest build is 57.2 GB NVFP4, for NVIDIA Blackwell only, and there is no GGUF", "No independent benchmark yet, and the top-ranking claim rests on Ricursive's own Terminal-Bench 2.1 run", "Thinking can't be turned off or budgeted", "No safety guidance for agent use, no security contact and no knowledge cut-off" ], "agentNotes": [ "Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default", "The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags", "For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields", "Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on", "Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support" ], "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": 53.3 } ], "editorialScores": { "interface": 75, "reach": 45, "risk": 55, "upkeep": 49 }, "provenanceScore": 36, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download ricursiveAI/Ripple-65B-Preview --local-dir ./ripple-65b\nhf download ricursiveAI/Ripple-65B-Preview-DFlash2 --local-dir ./ripple-dflash2\ncd ripple-65b/inference/server\nCUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py \\\n --model-path ../.. \\\n --speculative-draft-model-path ../../../ripple-dflash2", "http": "curl http://127.0.0.1:30000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ripple\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ripple-65b-preview" }, "weights": { "trainedBy": "Ricursive Intelligence", "params": "65B", "context": 1048576, "goodFor": "A dense 65B model post-trained for agentic coding and terminal work, with several tool calls a turn and a long context, for teams with Blackwell GPUs or a DGX Spark.", "runtimes": [], "builds": [ { "name": "Ripple-65B-Preview-NVFP4", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 57.2, "memoryGB": 69, "format": "safetensors", "note": "NVFP4 MLP weights and activations with BF16 attention, 6 shards summed from the Hub's file page. For NVIDIA Blackwell, with Ricursive's DGX Spark loader. The checkpoint was replaced in place on release day" }, { "name": "Ripple-65B-Preview", "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview", "who": "publisher", "precision": "bf16", "sizeGB": 129.6, "memoryGB": 160, "format": "safetensors", "note": "16 shards summed from the Hub's file page. Served with Ricursive's SGLang 0.5.19 plugin on two B200 GPUs" } ], "run": "CUDA_VISIBLE_DEVICES=0,1 uv run --script serve.py --model-path /path/to/Ripple-65B-Preview --speculative-draft-model-path /path/to/Ripple-65B-Preview-DFlash2", "runBuild": "Ripple-65B-Preview", "checked": "2026-11-04", "minMemoryGB": 69 }, "area": "models", "provenance": { "legalEntity": "Ricursive Intelligence, Inc.", "domain": "ricursive.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Every page on www.ricursive.com ends with \"© 2026 Ricursive Intelligence, Inc.\". The site links the Hugging Face organisation ricursiveAI (fullname Ricursive Intelligence), which publishes the four Ripple repositories. RDAP wasn't queried, by the run's rules.", "The Apache 2.0 LICENSE in the repository governs the weights and stands in for terms of service. The site's own legal pages (/legal and /legal/terms, titled Model Attribution Terms) render their text by script only and couldn't be read.", "Nothing is hosted by Ricursive, so no privacy policy or status page is scored.", "No changelog. The repositories' commit messages are uploads and one in-place swap of the NVFP4 checkpoint on release day.", "www.ricursive.com/.well-known/security.txt is 404 on 4 November 2026, and the only contact on the card and site is a general one." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ripple-65b-preview.json" }, "edges": [ { "by": 38, "category": "upkeep", "leads": "inkling-small", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the files last changed on 31 July 2026, but Thinking Machines' Inkling renderer in tinker-cookbook changed in its 30 October 2026 release. Responsiveness 20, Thinking Machines answered 8 of the 9 questions in its discussions. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17+15+20+20+5+10 = 87.", "b": "Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M." }, "scores": { "a": 87, "b": 49 } }, { "by": 15, "category": "risk", "leads": "inkling-small", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and described external red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.", "b": "File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw." }, "scores": { "a": 70, "b": 55 } }, { "by": 10, "category": "reach", "leads": "inkling-small", "name": "Reach", "reasons": { "a": "Memory 5 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 170.8 GB, about 205 GB with a fifth added, so it needs 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and DeepInfra is live on Hugging Face.", "b": "Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read." }, "scores": { "a": 55, "b": 45 } }, { "by": 10, "category": "interface", "leads": "inkling-small", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to the maximum, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens.", "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M." }, "scores": { "a": 85, "b": 75 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Thinking Machines Lab", "b": "Ricursive Intelligence", "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-07-31", "b": "2026-11-04", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling-Small scores 67 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.", "question": "Which is better for AI agents, Inkling-Small or Ripple-65B Preview?" } ], "goodFor": [ { "aheadOn": [ "Reach, 55 against 45", "Risk, 70 against 55", "Agent interface, 85 against 75", "Documentation and upkeep, 87 against 49" ], "also": null, "goodFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "slug": "inkling-small", "watchFor": "The smallest build Thinking Machines publishes needs about 205 GB" }, { "aheadOn": null, "also": null, "goodFor": "Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.", "slug": "ripple-65b-preview", "watchFor": "No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code" } ], "history": { "inkling-small": [ { "causes": [ "product" ], "date": "2026-11-01", "delta": 0.5, "from": 66.5, "fromGrade": "B", "to": 67, "toGrade": "B" } ], "ripple-65b-preview": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.json", "title": "Agents-A1-4B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.json", "title": "Agents-A1-4B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json", "title": "Apertus v1.5 8B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.json", "title": "Apertus v1.5 8B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview" }, { "json": "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-ripple-65b-preview.json", "title": "Apodex-1.1-mini vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview" }, { "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-ripple-65b-preview.json", "title": "BigBang-v1 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small.json", "title": "Carbon-X vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.json", "title": "Carbon-X vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling-small.json", "title": "Cosmos-Reason2-2B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.json", "title": "Cosmos-Reason2-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling-small.json", "title": "Darwin-36B-Opus vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.json", "title": "Darwin-36B-Opus vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small.json", "title": "DeepSeek-V3.2 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview.json", "title": "DeepSeek-V3.2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.json", "title": "DeepSeek V4.1 Flash vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview.json", "title": "DeepSeek V4.1 Flash vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling-small.json", "title": "DeepSeek-V4-Flash-0731 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview.json", "title": "DeepSeek-V4-Flash-0731 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.json", "title": "DiffusionGemma 26B A4B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview.json", "title": "DiffusionGemma 26B A4B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small.json", "title": "Gemma 4 12B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 12B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.json", "title": "Gemma 4 26B A4B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 26B A4B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.json", "title": "Gemma 4 31B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 31B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small.json", "title": "Gemma 4 E2B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 E2B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling-small.json", "title": "Gemma 4 E4B IT vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ripple-65b-preview.json", "title": "Gemma 4 E4B IT vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ripple-65b-preview" }, { "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-ripple-65b-preview.json", "title": "GLM-4.5-Air vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ripple-65b-preview" }, { "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-ripple-65b-preview.json", "title": "GLM-4.7-Flash vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.json", "title": "GLM-5.3-Flash vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.json", "title": "GLM-5.3-Flash vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-inkling-small.json", "title": "GLM-5.3 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview.json", "title": "GLM-5.3 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview" }, { "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-ripple-65b-preview.json", "title": "gpt-oss-120b vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ripple-65b-preview" }, { "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-ripple-65b-preview.json", "title": "gpt-oss-20b vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling-small.json", "title": "Granite 4.2 3B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.json", "title": "Granite 4.2 3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small.json", "title": "Hy3 vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/hy3-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ripple-65b-preview.json", "title": "Hy3 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/hy3-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Inkling-Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3.json", "title": "Inkling-Small vs Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1.json", "title": "Inkling-Small vs Kolibri-1", "url": 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LBS-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json", "title": "Inkling-Small vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.json", "title": "Inkling-Small vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Inkling-Small vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.json", "title": "Inkling-Small vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm5-2b.json", "title": "Inkling-Small vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.json", "title": "Inkling-Small vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.json", "title": "Inkling-Small vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m3-1-flash.json", "title": "Inkling-Small vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.json", "title": "Inkling-Small vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b.json", "title": "Inkling-Small vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.json", "title": "Inkling-Small vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling-Small vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-30b-a3b.json", "title": "Inkling-Small vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-4b.json", "title": "Inkling-Small vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Inkling-Small vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-super-120b-a12b.json", "title": "Inkling-Small vs Nemotron 3 Super 120B 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Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-32B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-4B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.json", "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.json", "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json", "title": "Recursive Flint vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json", "title": "Ripple-65B Preview vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b.json", "title": "Ripple-65B Preview vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ripple-65B Preview vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b.json", "title": "Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json", "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "edge": "inkling-small", "inkling-small": 55, "key": "reach", "name": "Reach", "ripple-65b-preview": 45, "weight": 25 }, { "by": 15, "edge": "inkling-small", "inkling-small": 70, "key": "risk", "name": "Risk", "ripple-65b-preview": 55, "weight": 20 }, { "by": 10, "edge": "inkling-small", "inkling-small": 85, "key": "interface", "name": "Agent interface", "ripple-65b-preview": 75, "weight": 10 }, { "by": 38, "edge": "inkling-small", "inkling-small": 87, "key": "upkeep", "name": "Documentation and upkeep", "ripple-65b-preview": 49, "weight": 10 } ], "summary": "Inkling-Small scores 67 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "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.", "ripple-65b-preview": "A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview", "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.min.md" }, "markdown": "Inkling-Small scores 67 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Inkling-Small: grade B, 67/100, rank #13 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #103 of 121 open-weight models. Markdown https://www.anchorterminal.com/tools/ripple-65b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.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 3674 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- Reach, 55 against 45\n- Risk, 70 against 55\n- Agent interface, 85 against 75\n- Documentation and upkeep, 87 against 49\n\nWatch for: The smallest build Thinking Machines publishes needs about 205 GB\n\n### Ripple-65B Preview (D)\n\nGood for: Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it.\n\nWatch for: No released runtime runs the architecture, so running it means Ricursive's Python in SGLang 0.5.19 or Transformers' remote code\n\n\n## Why the edge\n\n### Documentation and upkeep: Inkling-Small +38\n\n- Inkling-Small (87): Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page. Training data 15, an EU training-content summary with sources, mix, a July 2026 collection cut-off and filtering. Last change 20, the files last changed on 31 July 2026, but Thinking Machines' Inkling renderer in tinker-cookbook changed in its 30 October 2026 release. Responsiveness 20, Thinking Machines answered 8 of the 9 questions in its discussions. Version policy 5, a first release. File health 10, the files agree and no broken-file report waits. 17+15+20+20+5+10 = 87.\n- Ripple-65B Preview (49): Model card 14 of 20, no limitations and no knowledge cut-off. Training data 5, a general description with token counts and no cut-off. Last change 20, released today. Responsiveness 0, no discussions yet, read as none answered. Version policy 5, a first release, with the NVFP4 checkpoint replaced in place on release day under a commit note. File health 5, tokenizer_config.json carries a second, tool-less chat template and a 131,072-token limit against the card's 1M.\n\n### Risk: Inkling-Small +15\n\n- Inkling-Small (70): File safety 25, safetensors only and no repository code to run. Provenance 15, the files are in the organisation Thinking Machines links to, with a SHA-256 on every LFS file. Safety evaluation 10, Thinking Machines' own StrongREJECT and FORTRESS results and described external red-teaming, but nothing an outside party published or a harness to rerun. Agent guidance 10, the card says what its refusals miss and which safeguards to add, with nothing on prompt injection. Licence risk 5, 10 off for a use policy that reaches beyond the law and that can change at any time. Security programme 5, a security.txt and a disclosure policy, with public advisories unchecked and scored as absent.\n- Ripple-65B Preview (55): File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python. Provenance 15, the files are in the organisation ricursive.com links to, with a checksum on every file. Safety evaluation 10, Ricursive's own FORTRESS and StrongREJECT results. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, Apache 2.0. Security programme 0, no way to report a flaw.\n\n### Reach: Inkling-Small +10\n\n- Inkling-Small (55): Memory 5 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 170.8 GB, about 205 GB with a fifth added, so it needs 256 GB. Runtimes 15, released Transformers, vLLM and SGLang run it, llama.cpp, Ollama and LM Studio don't. Builds 10, one quantised build (NVFP4). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and DeepInfra is live on Hugging Face.\n- Ripple-65B Preview (45): Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine. Runtimes 0, no released runtime runs the ricursive architecture with its own code; Ricursive's SGLang plugin and Transformers' remote code don't count. Builds 10, one quantised precision (NVFP4) from Ricursive. Terms 15, Apache 2.0 with nothing added. Download 10, not gated. Hosted 0, Ricursive serves no API, Hugging Face maps no provider, Together AI's page says coming soon, and GMI Cloud's and OpenRouter's pages weren't read.\n\n### Agent interface: Inkling-Small +10\n\n- Inkling-Small (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to the maximum, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens.\n- Ripple-65B Preview (75): Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, Ricursive's server README says how tools are declared and calls come back, with the ripple parser in SGLang 0.5.19. Trained tool calling 20, one or more calls a turn and agentic-coding RL. Structured output 0, not documented. Reasoning control 0, the template has no thinking switch or effort levels. Context 10, long-context training at 2M-token sequences for a stated 1M.\n\n## Score by category\n\n| Category | Weight | Inkling-Small | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 45 | Inkling-Small +10 |\n| Risk | 20% (20 this run) | 70 | 55 | Inkling-Small +15 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Inkling-Small +10 |\n| Documentation and upkeep | 10% (10 this run) | 87 | 49 | Inkling-Small +38 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **67 · B** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | Inkling-Small | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | Ricursive Intelligence |\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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-31 | 2026-11-04 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.5/5 (4) | none |\n\n## How the scores have moved\n\n### Inkling-Small\n\n- +0.5 on 1 November 2026: product\n\n### Ripple-65B Preview\n\nNo score change since it was listed.\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**Ripple-65B Preview.** A dense 65B coding model from a new lab, pretrained from scratch and released today under Apache 2.0, that Ricursive says leads U.S. open-weight models on Terminal-Bench 2.1. Nobody has checked that yet, and the custom architecture runs only through Ricursive's own SGLang plugin on Blackwell GPUs, with a 57 GB NVFP4 build as the smallest.\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### Ripple-65B Preview\n\n1. Run Ricursive's launcher from inference/server with `uv run --script serve.py` on two B200 GPUs; it listens on 127.0.0.1:30000 with a 16K context by default\n2. The launcher turns on `--reasoning-parser ripple` and `--tool-call-parser ripple`; without them tool calls come back as raw text inside tool_call tags\n3. For `tool_choice: \"required\"` set `strict: true` and `additionalProperties: false` on each function, which the README says stopped extra argument fields\n4. Leave room in `max_tokens` for reasoning as well as the answer, since thinking is always on\n5. Use chat_template.jinja, not the template inside tokenizer_config.json, which has no tool support\n\n## Questions\n\n### Which is better for AI agents, Inkling-Small or Ripple-65B Preview?\n\nInkling-Small scores 67 (B) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-small-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling-small\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare inkling-small ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-small.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with Inkling-Small or Ripple-65B Preview\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 Ripple-65B Preview](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ripple-65b-preview.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 Ripple-65B Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ripple-65b-preview.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 Ripple-65B Preview](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ripple-65b-preview.md)\n- [BigBang-v1 vs Inkling-Small](https://www.anchorterminal.com/compare/bigbang-v1-vs-inkling-small.md)\n- [BigBang-v1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/bigbang-v1-vs-ripple-65b-preview.md)\n- [Carbon-X vs Inkling-Small](https://www.anchorterminal.com/compare/carbon-x-vs-inkling-small.md)\n- [Carbon-X vs Ripple-65B Preview](https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview.md)\n- [Cosmos-Reason2-2B vs Inkling-Small](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-inkling-small.md)\n- [Cosmos-Reason2-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ripple-65b-preview.md)\n- [Darwin-36B-Opus vs Inkling-Small](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-inkling-small.md)\n- [Darwin-36B-Opus vs Ripple-65B Preview](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview.md)\n- [DeepSeek-V3.2 vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-inkling-small.md)\n- [DeepSeek-V3.2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ripple-65b-preview.md)\n- [DeepSeek V4.1 Flash vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.md)\n- [DeepSeek V4.1 Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ripple-65b-preview.md)\n- [DeepSeek-V4-Flash-0731 vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-inkling-small.md)\n- [DeepSeek-V4-Flash-0731 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ripple-65b-preview.md)\n- [DiffusionGemma 26B A4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-inkling-small.md)\n- [DiffusionGemma 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 12B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-inkling-small.md)\n- [Gemma 4 12B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 26B A4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-inkling-small.md)\n- [Gemma 4 26B A4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 31B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-inkling-small.md)\n- [Gemma 4 31B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 E2B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-inkling-small.md)\n- [Gemma 4 E2B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ripple-65b-preview.md)\n- [Gemma 4 E4B IT vs Inkling-Small](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-inkling-small.md)\n- [Gemma 4 E4B IT vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ripple-65b-preview.md)\n- [GLM-4.5-Air vs Inkling-Small](https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small.md)\n- [GLM-4.5-Air vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ripple-65b-preview.md)\n- [GLM-4.7-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-inkling-small.md)\n- [GLM-4.7-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ripple-65b-preview.md)\n- [GLM-5.3-Flash vs Inkling-Small](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-inkling-small.md)\n- [GLM-5.3-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ripple-65b-preview.md)\n- [GLM-5.3 vs Inkling-Small](https://www.anchorterminal.com/compare/glm-5-3-vs-inkling-small.md)\n- [GLM-5.3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview.md)\n- [gpt-oss-120b vs Inkling-Small](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-inkling-small.md)\n- [gpt-oss-120b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ripple-65b-preview.md)\n- [gpt-oss-20b vs Inkling-Small](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-inkling-small.md)\n- [gpt-oss-20b vs Ripple-65B Preview](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ripple-65b-preview.md)\n- [Granite 4.2 3B vs Inkling-Small](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-inkling-small.md)\n- [Granite 4.2 3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ripple-65b-preview.md)\n- [Hy3 vs Inkling-Small](https://www.anchorterminal.com/compare/hy3-vs-inkling-small.md)\n- [Hy3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/hy3-vs-ripple-65b-preview.md)\n- [Inkling-Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/inkling-small-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Inkling-Small vs Kimi K3](https://www.anchorterminal.com/compare/inkling-small-vs-kimi-k3.md)\n- [Inkling-Small vs Kolibri-1](https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1.md)\n- [Inkling-Small vs Kronos](https://www.anchorterminal.com/compare/inkling-small-vs-kronos.md)\n- [Inkling-Small vs Laguna M.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-m-1.md)\n- [Inkling-Small vs Laguna S 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-s-2-1.md)\n- [Inkling-Small vs Laguna XS 2.1](https://www.anchorterminal.com/compare/inkling-small-vs-laguna-xs-2-1.md)\n- [Inkling-Small vs LBS-27B](https://www.anchorterminal.com/compare/inkling-small-vs-lbs-27b.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling-Small vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/inkling-small-vs-ling-3-0-tiny.md)\n- [Inkling-Small vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/inkling-small-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Inkling-Small vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm-o-4-5.md)\n- [Inkling-Small vs MiniCPM5-2B](https://www.anchorterminal.com/compare/inkling-small-vs-minicpm5-2b.md)\n- [Inkling-Small vs MiniMax-M2.5](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.md)\n- [Inkling-Small vs MiniMax-M2.7](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-7.md)\n- [Inkling-Small vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m3-1-flash.md)\n- [Inkling-Small vs Mistral Large 4](https://www.anchorterminal.com/compare/inkling-small-vs-mistral-large-4.md)\n- [Inkling-Small vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/inkling-small-vs-muse-glimmer-30b.md)\n- [Inkling-Small vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.md)\n- [Inkling-Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Inkling-Small vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Inkling-Small vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-30b-a3b.md)\n- [Inkling-Small vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-4b.md)\n- [Inkling-Small vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Inkling-Small vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-super-120b-a12b.md)\n- [Inkling-Small vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-ultra-550b-a55b.md)\n- [Inkling-Small vs occamy-1.0](https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.md)\n- [Inkling-Small vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/inkling-small-vs-olmo-3-7b-think.md)\n- [Inkling-Small vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-35b-a3b.md)\n- [Inkling-Small vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b.md)\n- [Inkling-Small vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b.md)\n- [Inkling-Small vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Inkling-Small vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-4b-instruct-2507.md)\n- [Inkling-Small vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-0-8b.md)\n- [Inkling-Small vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-122b-a10b.md)\n- [Inkling-Small vs Qwen3.5-2B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-2b.md)\n- [Inkling-Small vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-397b-a17b.md)\n- [Inkling-Small vs Qwen3.5-4B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-4b.md)\n- [Inkling-Small vs Qwen3.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-5-9b.md)\n- [Inkling-Small vs 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Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Inkling-Small vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-32b-instruct.md)\n- [Inkling-Small vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.md)\n- [Inkling-Small vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct.md)\n- [Inkling-Small vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.md)\n- [Inkling-Small vs Recursive Flint](https://www.anchorterminal.com/compare/inkling-small-vs-recursive-flint.md)\n- [Inkling-Small vs SmolLM3-3B](https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.md)\n- [Inkling-Small vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.md)\n- [Inkling-Small vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.md)\n- [Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Inkling-Small vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.md)\n- [Inkling vs Inkling-Small](https://www.anchorterminal.com/compare/inkling-vs-inkling-small.md)\n- [Inkling vs Ripple-65B Preview](https://www.anchorterminal.com/compare/inkling-vs-ripple-65b-preview.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ripple-65B Preview](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ripple-65b-preview.md)\n- [Kimi K3 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kimi-k3-vs-ripple-65b-preview.md)\n- [Kolibri-1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kolibri-1-vs-ripple-65b-preview.md)\n- [Kronos vs Ripple-65B Preview](https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview.md)\n- [Laguna M.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.md)\n- [Laguna S 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ripple-65b-preview.md)\n- [Laguna XS 2.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ripple-65b-preview.md)\n- [LBS-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lbs-27b-vs-ripple-65b-preview.md)\n- [LFM2.5-2.6B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ripple-65b-preview.md)\n- [Ling-3.0-tiny vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ripple-65b-preview.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ripple-65b-preview.md)\n- [MiniCPM-o 4.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ripple-65b-preview.md)\n- [MiniCPM5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ripple-65b-preview.md)\n- [MiniMax-M2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.md)\n- [MiniMax-M2.7 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ripple-65b-preview.md)\n- [MiniMax-M3.1-Flash vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ripple-65b-preview.md)\n- [Mistral Large 4 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mistral-large-4-vs-ripple-65b-preview.md)\n- [Muse Glimmer 30B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ripple-65b-preview.md)\n- [Nanbeige4.3-33B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ripple-65b-preview.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ripple-65b-preview.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ripple-65b-preview.md)\n- [Nemotron 3 Nano 30B A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ripple-65b-preview.md)\n- [Nemotron 3 Nano 4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ripple-65b-preview.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ripple-65b-preview.md)\n- [Nemotron 3 Super 120B A12B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview.md)\n- [Nemotron 3 Ultra 550B A55B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ripple-65b-preview.md)\n- [occamy-1.0 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.md)\n- [Olmo 3 7B Think vs Ripple-65B Preview](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ripple-65b-preview.md)\n- [Ornith-1.5-35B-A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ripple-65b-preview.md)\n- [Ornith-1.5-397B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview.md)\n- [Ornith-1.5-9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-ripple-65b-preview.md)\n- [Qwen3-4B-Instruct-2507 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview.md)\n- [Qwen3.5-0.8B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-ripple-65b-preview.md)\n- [Qwen3.5-122B-A10B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-ripple-65b-preview.md)\n- [Qwen3.5-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-ripple-65b-preview.md)\n- [Qwen3.5-397B-A17B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-ripple-65b-preview.md)\n- [Qwen3.5-4B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-ripple-65b-preview.md)\n- [Qwen3.5-9B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-ripple-65b-preview.md)\n- [Qwen3.6-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.md)\n- [Qwen3.6-35B-A3B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-ripple-65b-preview.md)\n- [Qwen3.8-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.md)\n- [Qwen3.8-Flash-Next vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-ripple-65b-preview.md)\n- [Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-Coder-Next vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-ripple-65b-preview.md)\n- [Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-VL-2B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-VL-32B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-VL-4B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview.md)\n- [Qwen3-VL-8B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-ripple-65b-preview.md)\n- [Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-ripple-65b-preview.md)\n- [Recursive Flint vs Ripple-65B Preview](https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.md)\n- [Ripple-65B Preview vs SmolLM3-3B](https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.md)\n- [Ripple-65B Preview vs 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"CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "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 Ripple-65B Preview", "url": "" } ], "description": "Inkling-Small scores 67 (B) to Ripple-65B Preview's 53.3 (D) for open-weight models you run yourself. 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