{
  "data": {
    "a": {
      "slug": "otel-2-0-llm-31b-it",
      "name": "OTel-2.0-LLM-31B-IT",
      "vendor": "AT\u0026T (Open Telco AI)",
      "vendorUrl": "https://github.com/farbodtavakkoli/OTel",
      "kind": "weights",
      "category": "open-weights",
      "summary": "OTel-2.0-LLM-31B-IT is AT\u0026T's open-weight telecoms model, Gemma 4 31B IT post-trained on about 440 billion telecom tokens from GSMA's corpus of standards, for RAG and question answering over 3GPP, ETSI and other specifications, published in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json",
      "dns": "otel-2-0-llm-31b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "farbodtavakkoli/OTel-2.0-LLM-31B-IT"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata gives it, with Google's Gemma 4 licence page as the link. Running it yourself costs the hardware, 62.6 GB in BF16, which the card puts on two 80 GB GPUs or one of 192 GB (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "fine-tune",
        "telecom",
        "domain-specific",
        "rag",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-11-01",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 83,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.",
        "bestFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "strengths": [
          "The card states what the training covered and left out, and what hasn't been validated",
          "Training sources and sizes are named, with GSMA as the provider of the corpus",
          "Apache 2.0, ungated, safetensors only, no code to run",
          "A full vLLM serving guide with sizing, stop tokens and troubleshooting",
          "The owner answers questions on the repository"
        ],
        "weaknesses": [
          "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked",
          "The files are in a personal account, not an AT\u0026T or GSMA organisation",
          "Weights replaced in place three times, and an old shard set left in the repository",
          "No tool-call format, structured output or thinking",
          "Post-trained at 4,096 tokens, so long-context use is unproven",
          "No safety results and no security contact"
        ],
        "agentNotes": [
          "Download the 15 shards the index names, not the whole repository, which is twice the size",
          "Pin a revision. The card says the weights may change under the same name",
          "Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's",
          "Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel",
          "Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review",
          "Don't use it to run network tools unattended. The card says that hasn't been evaluated"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.8
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# with vLLM 0.19.0 or later, on two 80 GB GPUs, per the card:\nvllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --served-model-name OTel-2.0-LLM-31B-IT \\\n  --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16 --port 8000",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"OTel-2.0-LLM-31B-IT\", \"messages\": [{\"role\": \"user\", \"content\": \"What model is this?\"}], \"temperature\": 0}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/otel-2-0-llm-31b-it"
      },
      "weights": {
        "trainedBy": "AT\u0026T Chief Data Office, with GSMA, Red Hat, Microsoft, AMD, Dell, MLCommons and Pleias named as contributors",
        "builtOn": "google/gemma-4-31b-it (post-trained with OSFT; named on the card and in its metadata)",
        "params": "31B",
        "context": 262144,
        "goodFor": "Telecoms question answering and RAG over 3GPP, ETSI, GSMA, CAMARA, ITU, O-RAN and TM Forum material, in English, as the generation stage with retrieval and human review around it.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "OTel-2.0-LLM-31B-IT",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.55,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "The 15 shards the index names. The repository also holds an earlier 14-shard set of 64.21 GB, so download by the index and not the whole repository. No quantised build from the publisher or the usual converters"
          }
        ],
        "run": "vllm serve farbodtavakkoli/OTel-2.0-LLM-31B-IT --tensor-parallel-size 2 --max-model-len 8192 --dtype bfloat16",
        "runBuild": "OTel-2.0-LLM-31B-IT",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "AT\u0026T (Open Telco AI)",
        "family": "OTel 2.0",
        "url": "https://www.anchorterminal.com/open-weights/#att-open-telco",
        "others": [
          {
            "name": "OTel-2.0-LLM-31B-IT-QLoRA",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT-QLoRA",
            "params": "31B"
          },
          {
            "name": "OTel-2.0-Decision-26B-A4B-4bit",
            "url": "https://huggingface.co/farbodtavakkoli/OTel-2.0-Decision-26B-A4B-4bit",
            "params": "26B total, 4B active (per name; card not read)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "AT\u0026T (the card and GSMA's newsroom name AT\u0026T as the releaser, and the model answers that AT\u0026T Chief Data Office trained it). Published from a contributor's personal accounts on Hugging Face and GitHub",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/farbodtavakkoli/OTel-2.0-LLM-31B-IT/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No vendor domain is recorded. The project's pages are a personal GitHub repository and Hugging Face account, and open-telco.ai, the site GSMA's article links, answered 403 on 10 November 2026.",
          "GSMA's newsroom article of 24 July 2026 names AT\u0026T as the releaser and GSMA as the source of the corpus. It doesn't link the Hugging Face repository.",
          "The Hub lists the publishing account as a member of the AT\u0026T and GSMA organisations.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The GitHub repository has no SECURITY.md (404 on 10 November 2026)."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.json"
    },
    "answer": "OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.",
    "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": 1518,
        "categoryRank": 100,
        "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",
      "live": {
        "slug": "ripple-65b-preview",
        "securityTxt": {
          "url": "https://ricursive.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:39.085200806Z"
        },
        "domain": {
          "domain": "ricursive.com",
          "registered": "2025-10-05",
          "source": "https://rdap.verisign.com/com/v1/domain/ricursive.com",
          "checkedAt": "2026-11-08T14:52:57.113403323Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:44.447363623Z",
            "changedAt": "2026-11-10T16:23:44.447363623Z",
            "fingerprint": "ceb3545d1daa"
          }
        ],
        "updatedAt": "2026-11-10T16:23:44.447363623Z"
      }
    },
    "edges": [
      {
        "by": 25,
        "category": "upkeep",
        "leads": "otel-2-0-llm-31b-it",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.",
          "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": 74,
          "b": 49
        }
      },
      {
        "by": 15,
        "category": "interface",
        "leads": "ripple-65b-preview",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "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": 60,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "otel-2-0-llm-31b-it",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.",
          "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": 50,
          "b": 45
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "AT\u0026T (Open Telco AI)",
        "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": "2026-11-01",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.",
        "question": "Which is better for AI agents, OTel-2.0-LLM-31B-IT or Ripple-65B Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 50 against 45",
          "Documentation and upkeep, 74 against 49"
        ],
        "also": null,
        "goodFor": "Telecoms teams that want an open model for standards questions and RAG on their own hardware",
        "slug": "otel-2-0-llm-31b-it",
        "watchFor": "BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked"
      },
      {
        "aheadOn": [
          "Agent interface, 75 against 60"
        ],
        "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": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it"
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      {
        "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-otel-2-0-llm-31b-it.json",
        "title": "Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it"
      },
      {
        "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-otel-2-0-llm-31b-it.json",
        "title": "Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it"
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      {
        "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/arete-27b-vs-otel-2-0-llm-31b-it.json",
        "title": "Arete-27B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.json",
        "title": "Arete-27B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it.json",
        "title": "BigBang-v1 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it"
      },
      {
        "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-otel-2-0-llm-31b-it.json",
        "title": "Carbon-X vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-otel-2-0-llm-31b-it"
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      {
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.json",
        "title": "Cola2 vs OTel-2.0-LLM-31B-IT",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.json",
        "title": "Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ripple-65b-preview"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it.json",
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      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ripple-65b-preview"
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.json",
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        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-ripple-65b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-ripple-65b-preview"
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        "title": "Qwen3-VL-8B-Instruct vs Ripple-65B Preview",
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        "title": "Qwopus3.8-27B-Flash-V2 vs Ripple-65B Preview",
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        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json",
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        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-27b"
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-swift-1-5-qwen3-8-flash-next"
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        "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-thinkingcap-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-unlimited-ocr-2.json",
        "title": "Ripple-65B Preview vs Baidu Unlimited-OCR-2",
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-2B",
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-4b.json",
        "title": "Ripple-65B Preview vs WeVisOne-VL-4B",
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      {
        "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "otel-2-0-llm-31b-it",
        "key": "reach",
        "name": "Reach",
        "otel-2-0-llm-31b-it": 50,
        "ripple-65b-preview": 45,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "otel-2-0-llm-31b-it": 55,
        "ripple-65b-preview": 55,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "ripple-65b-preview",
        "key": "interface",
        "name": "Agent interface",
        "otel-2-0-llm-31b-it": 60,
        "ripple-65b-preview": 75,
        "weight": 10
      },
      {
        "by": 25,
        "edge": "otel-2-0-llm-31b-it",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "otel-2-0-llm-31b-it": 74,
        "ripple-65b-preview": 49,
        "weight": 10
      }
    ],
    "summary": "OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "otel-2-0-llm-31b-it": "A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.",
      "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": {
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    "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface. Both do open-weight models you run yourself.\n\n- OTel-2.0-LLM-31B-IT: grade C, 56.8/100, rank #111 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json\n- Ripple-65B Preview: grade D, 53.3/100, rank #135 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### OTel-2.0-LLM-31B-IT (C)\n\nGood for: Telecoms teams that want an open model for standards questions and RAG on their own hardware\n\nAhead on:\n- Reach, 50 against 45\n- Documentation and upkeep, 74 against 49\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\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\nAhead on:\n- Agent interface, 75 against 60\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: OTel-2.0-LLM-31B-IT +25\n\n- OTel-2.0-LLM-31B-IT (74): Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.\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### Agent interface: Ripple-65B Preview +15\n\n- OTel-2.0-LLM-31B-IT (60): Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.\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### Reach: OTel-2.0-LLM-31B-IT +5\n\n- OTel-2.0-LLM-31B-IT (50): Memory 10, the publisher's only build is the BF16 safetensors, 62.55 GB for the 15 shards the index names, and 62.55 GB × 1.2 = 75.1 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 15, the architecture is Gemma4ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors files, three at 5 each. Builds 0, the publisher ships the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with nothing added on the card. Download 10, gated is false on 10 November 2026, so the files download with no account or terms. Hosted 0, Hugging Face maps one inference provider, Featherless AI (live), short of the two the line needs. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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## Score by category\n\n| Category | Weight | OTel-2.0-LLM-31B-IT | Ripple-65B Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 45 | OTel-2.0-LLM-31B-IT +5 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 75 | Ripple-65B Preview +15 |\n| Documentation and upkeep | 10% (10 this run) | 74 | 49 | OTel-2.0-LLM-31B-IT +25 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **53.3 · D** | |\n\n## Facts side by side\n\n| Fact | OTel-2.0-LLM-31B-IT | Ripple-65B Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | AT\u0026T (Open Telco AI) | 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 | 2026-11-01 | 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\n## Verdicts\n\n**OTel-2.0-LLM-31B-IT.** A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.\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### OTel-2.0-LLM-31B-IT\n\n1. Download the 15 shards the index names, not the whole repository, which is twice the size\n2. Pin a revision. The card says the weights may change under the same name\n3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's\n4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel\n5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review\n6. Don't use it to run network tools unattended. The card says that hasn't been evaluated\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, OTel-2.0-LLM-31B-IT or Ripple-65B Preview?\n\nOTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-ripple-65b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"otel-2-0-llm-31b-it\", \"b\": \"ripple-65b-preview\"}`. From a terminal: `anchor compare otel-2-0-llm-31b-it ripple-65b-preview`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json and https://www.anchorterminal.com/api/v1/tools/ripple-65b-preview.json\n\n## Other comparisons with OTel-2.0-LLM-31B-IT or Ripple-65B Preview\n\n- [Agents-A1-4B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it.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 OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-otel-2-0-llm-31b-it.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 OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-otel-2-0-llm-31b-it.md)\n- [Apodex-1.1-mini vs Ripple-65B 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Flash](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-step-3-7-flash.md)\n- [OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-27b.md)\n- [OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-swift-1-5-qwen3-8-flash-next.md)\n- [OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-unlimited-ocr-2.md)\n- [OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.md)\n- [OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-4b.md)\n- [OTel-2.0-LLM-31B-IT vs 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