{ "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": "WeVisOne-VL-2B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "b": { "slug": "wevisone-vl-2b", "name": "WeVisOne-VL-2B", "vendor": "Tencent (WeChat Vision)", "vendorUrl": "https://www.tencent.com", "kind": "weights", "category": "open-weights", "summary": "WeVisOne-VL-2B is an open-weight perception model of 2.4B parameters from Tencent's WeChat Vision team, built on Qwen3-VL. Name something in an image and it returns boxes, points, counts, text, masks or alpha mattes. Apache 2.0.", "url": "https://www.anchorterminal.com/tools/wevisone-vl-2b", "markdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.json", "dns": "wevisone-vl-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/WeVisOne-VL-2B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/WeVisOne-VL-2B" } ], "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 under Apache 2.0. Running it yourself costs the hardware, 4.88 GB in BF16 (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://github.com/Tencent/WeVisOne-VL", "capabilities": [ "model.open-weights", "docs.ocr" ], "tags": [ "open-weights", "model", "vision", "grounding", "object-detection", "segmentation", "matting", "ocr", "counting", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 65, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.", "bestFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "strengths": [ "Seven perception tasks in one model, from boxes to alpha mattes", "Returns None for a target that isn't in the image", "Apache 2.0, ungated", "Each task's input and output is documented", "4.88 GB in BF16, so it fits a 8 GB machine" ], "weaknesses": [ "The pixel decoder ships as a pickle file", "Masks and mattes need Tencent's package from GitHub", "No quantised build from Tencent", "One image and one turn at a time, no video", "No reply from Tencent on the repository's discussions", "No context length, languages, safety results or data sources on the card" ], "agentNotes": [ "Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`", "Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting", "A None answer means the target isn't in the image. Treat it as a result, not an error", "mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's", "Send one image a turn. Video grids are rejected by the decoder", "The card says it isn't for identifying individuals or for high-impact decisions on its own" ], "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": 61 } ], "editorialScores": { "interface": 70, "reach": 70, "risk": 55, "upkeep": 48 }, "provenanceScore": 68, "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": "git clone https://github.com/Tencent/WeVisOne-VL.git \u0026\u0026 cd WeVisOne-VL\npip install -r requirements.txt \u0026\u0026 pip install -e .\n# then, in Python: WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"transformers\") or backend=\"vllm\"", "http": "# No HTTP API of its own. Tencent's package calls model.predict(image, task=\"detection\", query=[...]).\n# For boxes and points only, vLLM serves the backbone at http://localhost:8000/v1 with: vllm serve tencent/WeVisOne-VL-2B" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/wevisone-vl-2b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy-memory-30b-a3b", "hy3", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent WeChat Vision", "params": "2.4B", "goodFor": "Finding, counting, reading and cutting out what you name in an image, on a 8 GB machine.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "WeVisOne-VL-2B (BF16 safetensors)", "url": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 4.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The only build. Masks and mattes also need mask_decoder.pt and Tencent's package" } ], "run": "vllm serve tencent/WeVisOne-VL-2B", "runBuild": "WeVisOne-VL-2B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "WeVisOne-VL", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "WeVisOne-VL-4B", "url": "https://huggingface.co/tencent/WeVisOne-VL-4B", "params": "4.8B (4,830,581,248 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/wevisone-vl-4b" } ], "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/WeVisOne-VL-2B/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/WeVisOne-VL-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The card names WeChat Vision, Tencent Inc. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/wevisone-vl-2b.json" }, "edges": [ { "by": 26, "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 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48." }, "scores": { "a": 74, "b": 48 } }, { "by": 20, "category": "reach", "leads": "wevisone-vl-2b", "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 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70." }, "scores": { "a": 50, "b": 70 } }, { "by": 10, "category": "interface", "leads": "wevisone-vl-2b", "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 (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70." }, "scores": { "a": 60, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "AT\u0026T (Open Telco AI)", "b": "Tencent (WeChat Vision)", "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": "2026-10-28", "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": "WeVisOne-VL-2B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "question": "Which is better for AI agents, OTel-2.0-LLM-31B-IT or WeVisOne-VL-2B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 74 against 48" ], "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": [ "Reach, 70 against 50", "Agent interface, 70 against 60" ], "also": null, "goodFor": "Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence", "slug": "wevisone-vl-2b", "watchFor": "The pixel decoder ships as a pickle file" } ], "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", "title": "Agents-A1-4B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.json", "title": "Agents-A1-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b" }, { "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-wevisone-vl-2b.json", "title": "Apertus v1.5 8B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.json", "title": "Apodex-1.1-mini vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.json", "title": "Arete-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b" }, { "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-wevisone-vl-2b.json", "title": "BigBang-v1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b.json", "title": "Carbon-X vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.json", "title": "Cola2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json", "title": "Cola2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b" }, { "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", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.json", "title": "Cosmos-Reason2-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.json", "title": "Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.json", "title": "Darwin-36B-Opus vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.json", "title": "DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.json", "title": "DeepSeek-V3.2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it.json", "title": "DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.json", "title": "DeepSeek V4.1 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it.json", "title": "DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b.json", "title": "DeepSeek-V4-Flash-0731 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-otel-2-0-llm-31b-it.json", "title": "Devstral Small 2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.json", "title": "Devstral Small 2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.json", "title": "DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b.json", "title": "DiffusionGemma 26B A4B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.json", "title": "EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.json", "title": "EXAONE 4.5 33B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-otel-2-0-llm-31b-it.json", "title": "Gemma 4 12B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 12B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-otel-2-0-llm-31b-it.json", "title": "Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 26B A4B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-otel-2-0-llm-31b-it.json", "title": "Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 31B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-otel-2-0-llm-31b-it.json", "title": "Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 E2B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-otel-2-0-llm-31b-it.json", "title": "Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b.json", "title": "Gemma 4 E4B IT vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it.json", "title": "GLM-4.5-Air vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-2b.json", "title": "GLM-4.5-Air vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it.json", "title": "GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b.json", "title": "GLM-4.7-Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-otel-2-0-llm-31b-it.json", "title": "GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b.json", "title": "GLM-5.3-Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-otel-2-0-llm-31b-it.json", "title": "GLM-5.3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b.json", "title": "GLM-5.3 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-otel-2-0-llm-31b-it.json", "title": "gpt-oss-120b vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b.json", "title": "gpt-oss-120b vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.json", "title": "gpt-oss-20b vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-2b.json", "title": "gpt-oss-20b vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 30B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b.json", "title": "Granite 4.2 30B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b.json", "title": "Granite 4.2 3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.json", "title": "Granite 4.2 8B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b.json", "title": "Granite 4.2 8B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.json", "title": "Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it.json", "title": "Hy3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.json", "title": "Hy3 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it.json", "title": "Inkling-Small vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.json", "title": "Inkling-Small vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.json", "title": "Inkling vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.json", "title": "Inkling vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.json", "title": "Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.json", "title": "Kimi K3.1 Pro vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.json", "title": "Kimi K3 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.json", "title": "Kimi K3 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.json", "title": "Kolibri-1 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json", "title": "Kolibri-1 vs WeVisOne-VL-2B", "url": 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{ "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it.json", "title": "Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b.json", "title": "Olmo 3 7B Think vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-122B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b" }, { "json": 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"https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b.json", "title": "Qwen3.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.json", "title": "Qwen3.6-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b.json", "title": "Qwen3.6-35B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "Qwen3.8-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b.json", "title": "Qwen3.8-Flash-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b.json", "title": "Qwen3-Coder-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-2B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-4B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b.json", "title": "Qwen3-VL-8B-Instruct vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b.json", "title": "Qwopus3.8-27B-Flash-V2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json", "title": "Recursive Flint vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b.json", "title": "Ripple-65B Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b.json", "title": "SenseNova-Loop-9B-Preview vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b.json", "title": "SmolLM3-3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b.json", "title": "Spark-X2.6-4B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json", "title": "Step 3.7 Flash vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "Swift 1.5 Qwen3.8-27B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b.json", "title": "ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b.json", "title": "WeVisOne-VL-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.json", "title": "WeVisOne-VL-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b.json", "title": "Baidu Unlimited-OCR-2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-wevisone-vl-2b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "wevisone-vl-2b", "key": "reach", "name": "Reach", "otel-2-0-llm-31b-it": 50, "weight": 25, "wevisone-vl-2b": 70 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "otel-2-0-llm-31b-it": 55, "weight": 20, "wevisone-vl-2b": 55 }, { "by": 10, "edge": "wevisone-vl-2b", "key": "interface", "name": "Agent interface", "otel-2-0-llm-31b-it": 60, "weight": 10, "wevisone-vl-2b": 70 }, { "by": 26, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "otel-2-0-llm-31b-it": 74, "weight": 10, "wevisone-vl-2b": 48 } ], "summary": "WeVisOne-VL-2B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. 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.", "wevisone-vl-2b": "A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b", "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.min.md" }, "markdown": "WeVisOne-VL-2B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep. 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- WeVisOne-VL-2B: grade C, 61/100, rank #83 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/wevisone-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/wevisone-vl-2b.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- Documentation and upkeep, 74 against 48\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n### WeVisOne-VL-2B (C)\n\nGood for: Agents that need to locate, count or cut out objects in images on a small GPU, under a permissive licence\n\nAhead on:\n- Reach, 70 against 50\n- Agent interface, 70 against 60\n\nWatch for: The pixel decoder ships as a pickle file\n\n\n## Why the edge\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +26\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- WeVisOne-VL-2B (48): Model card 8, Intended use, limitations and the architecture are on the card. Training data 5, A general description in the README of a data engine and over 100 million annotated images, with no sources named and no cut-off. Last change 20, the last commit, on 28 October 2026, put the weights back in BF16, 13 days before this check. Responsiveness 0, the one discussion that isn't a pull request (23 October 2026) has no reply from a tencent member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release. File health 10, Config, template and card agree on the Qwen3-VL classes, and the card names mask_decoder.pt as required and it's there. 8 + 5 + 20 + 0 + 5 + 10 = 48.\n\n### Reach: WeVisOne-VL-2B +20\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- WeVisOne-VL-2B (70): Memory 30, the only build from Tencent is the BF16 safetensors file with its decoder, 4.92 GB, and 4.92 GB × 1.2 = 5.9 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3VLForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Tencent publishes the BF16 weights only. Terms 15, Apache 2.0 in the card's metadata with the LICENSE file in the repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 15 + 10 + 0 = 70.\n\n### Agent interface: WeVisOne-VL-2B +10\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- WeVisOne-VL-2B (70): Chat template 20, chat_template.jinja in the repository (5,292 characters), Qwen3-VL's template. Tool-calling format 25, Read for a perception model as what it takes and returns. Trained tool calling 10, Read for a perception model as the inputs it was trained to use. Structured output 15, Read for a perception model as typed outputs. Reasoning control 0, the card says it writes structured output directly with no chain-of-thought reasoning. Context 0, Tencent states no context length for it. 20 + 25 + 10 + 15 + 0 + 0 = 70.\n\n## Score by category\n\n| Category | Weight | OTel-2.0-LLM-31B-IT | WeVisOne-VL-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 70 | WeVisOne-VL-2B +20 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | WeVisOne-VL-2B +10 |\n| Documentation and upkeep | 10% (10 this run) | 74 | 48 | OTel-2.0-LLM-31B-IT +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | OTel-2.0-LLM-31B-IT | WeVisOne-VL-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | AT\u0026T (Open Telco AI) | Tencent (WeChat Vision) |\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 | 2026-10-28 |\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**WeVisOne-VL-2B.** A small Apache 2.0 perception model that finds, counts, reads and cuts out what you name in an image, and says None when it isn't there. Boxes and points run in vLLM, SGLang or Transformers. Masks and mattes need Tencent's package and a pickle file. Two weeks old, with no questions answered yet and no safety results.\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### WeVisOne-VL-2B\n\n1. Use Tencent's package: `WeVisOneVL(\"tencent/WeVisOne-VL-2B\", backend=\"vllm\")` then `model.predict(image, task=..., query=...)`\n2. Tasks are detection, referring, point, count_box, count_point, ocr, ocr_grounding, segmentation and matting\n3. A None answer means the target isn't in the image. Treat it as a result, not an error\n4. mask_decoder.pt is a pickle. Load it only from the tencent repository, and check its SHA-256 against the Hub's\n5. Send one image a turn. Video grids are rejected by the decoder\n6. The card says it isn't for identifying individuals or for high-impact decisions on its own\n\n## Questions\n\n### Which is better for AI agents, OTel-2.0-LLM-31B-IT or WeVisOne-VL-2B?\n\nWeVisOne-VL-2B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-wevisone-vl-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"otel-2-0-llm-31b-it\", \"b\": \"wevisone-vl-2b\"}`. From a terminal: `anchor compare otel-2-0-llm-31b-it wevisone-vl-2b`\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/wevisone-vl-2b.json\n\n## Other comparisons with OTel-2.0-LLM-31B-IT or WeVisOne-VL-2B\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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-wevisone-vl-2b.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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-wevisone-vl-2b.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 WeVisOne-VL-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-wevisone-vl-2b.md)\n- [Arete-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md)\n- [Arete-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.md)\n- [BigBang-v1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/bigbang-v1-vs-otel-2-0-llm-31b-it.md)\n- [BigBang-v1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/bigbang-v1-vs-wevisone-vl-2b.md)\n- [Carbon-X vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/carbon-x-vs-otel-2-0-llm-31b-it.md)\n- [Carbon-X vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/carbon-x-vs-wevisone-vl-2b.md)\n- [Cola2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it.md)\n- [Cola2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.md)\n- [Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-otel-2-0-llm-31b-it.md)\n- [Cosmos-Reason2-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-wevisone-vl-2b.md)\n- [Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md)\n- [Darwin-36B-Opus vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-wevisone-vl-2b.md)\n- [DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V3.2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-wevisone-vl-2b.md)\n- [DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek V4.1 Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-wevisone-vl-2b.md)\n- [DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-otel-2-0-llm-31b-it.md)\n- [DeepSeek-V4-Flash-0731 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-wevisone-vl-2b.md)\n- [Devstral Small 2 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/devstral-small-2-vs-otel-2-0-llm-31b-it.md)\n- [Devstral Small 2 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.md)\n- [DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-otel-2-0-llm-31b-it.md)\n- [DiffusionGemma 26B A4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-wevisone-vl-2b.md)\n- [EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-otel-2-0-llm-31b-it.md)\n- [EXAONE 4.5 33B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-wevisone-vl-2b.md)\n- [Gemma 4 12B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 12B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 26B A4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 31B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 E2B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-wevisone-vl-2b.md)\n- [Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-otel-2-0-llm-31b-it.md)\n- [Gemma 4 E4B IT vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-wevisone-vl-2b.md)\n- [GLM-4.5-Air vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-4-5-air-vs-otel-2-0-llm-31b-it.md)\n- [GLM-4.5-Air vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-wevisone-vl-2b.md)\n- [GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-otel-2-0-llm-31b-it.md)\n- [GLM-4.7-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-wevisone-vl-2b.md)\n- [GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-otel-2-0-llm-31b-it.md)\n- [GLM-5.3-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-wevisone-vl-2b.md)\n- [GLM-5.3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/glm-5-3-vs-otel-2-0-llm-31b-it.md)\n- [GLM-5.3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-vs-wevisone-vl-2b.md)\n- [gpt-oss-120b vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-otel-2-0-llm-31b-it.md)\n- [gpt-oss-120b vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-wevisone-vl-2b.md)\n- [gpt-oss-20b vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-otel-2-0-llm-31b-it.md)\n- [gpt-oss-20b vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 30B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 30B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-wevisone-vl-2b.md)\n- [Granite 4.2 8B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-otel-2-0-llm-31b-it.md)\n- [Granite 4.2 8B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-wevisone-vl-2b.md)\n- [Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [Hy-Memory-30B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.md)\n- [Hy3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy3-vs-otel-2-0-llm-31b-it.md)\n- [Hy3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/hy3-vs-wevisone-vl-2b.md)\n- [Inkling-Small vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-small-vs-otel-2-0-llm-31b-it.md)\n- [Inkling-Small vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/inkling-small-vs-wevisone-vl-2b.md)\n- [Inkling vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/inkling-vs-otel-2-0-llm-31b-it.md)\n- [Inkling vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/inkling-vs-wevisone-vl-2b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-otel-2-0-llm-31b-it.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-wevisone-vl-2b.md)\n- [Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3.1 Pro vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-wevisone-vl-2b.md)\n- [Kimi K3 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kimi-k3-vs-otel-2-0-llm-31b-it.md)\n- [Kimi K3 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kimi-k3-vs-wevisone-vl-2b.md)\n- [Kolibri-1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.md)\n- [Kolibri-1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.md)\n- [Kronos vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it.md)\n- [Kronos vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.md)\n- [Laguna M.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-m-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna M.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.md)\n- [Laguna S 2.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna S 2.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-wevisone-vl-2b.md)\n- [Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.md)\n- [Laguna XS 2.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-wevisone-vl-2b.md)\n- [LBS-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lbs-27b-vs-otel-2-0-llm-31b-it.md)\n- [LBS-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lbs-27b-vs-wevisone-vl-2b.md)\n- [LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-otel-2-0-llm-31b-it.md)\n- [LFM2.5-2.6B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-wevisone-vl-2b.md)\n- [LFM3-1.7B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-1.7B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-wevisone-vl-2b.md)\n- [LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Audio-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-wevisone-vl-2b.md)\n- [LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-Omni-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.md)\n- [LFM3-VL-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-otel-2-0-llm-31b-it.md)\n- [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md)\n- [Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-otel-2-0-llm-31b-it.md)\n- [Ling-3.0-tiny vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-wevisone-vl-2b.md)\n- [Ling-3.1-flash-1019 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-otel-2-0-llm-31b-it.md)\n- [Ling-3.1-flash-1019 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b.md)\n- [LongCat-2.5 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.md)\n- [LongCat-2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-otel-2-0-llm-31b-it.md)\n- [MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-2b.md)\n- [MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it.md)\n- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md)\n- [MiniCPM-o 4.5 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM-o 4.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM-V 4.7 1B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-wevisone-vl-2b.md)\n- [MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM-V 4.7 35B-A3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-wevisone-vl-2b.md)\n- [MiniCPM5-2B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minicpm5-2b-vs-otel-2-0-llm-31b-it.md)\n- [MiniCPM5-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.5 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minimax-m2-5-vs-otel-2-0-llm-31b-it.md)\n- [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md)\n- [MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minimax-m2-7-vs-otel-2-0-llm-31b-it.md)\n- [MiniMax-M2.7 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-wevisone-vl-2b.md)\n- [MiniMax-M3.1-Flash vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-otel-2-0-llm-31b-it.md)\n- [MiniMax-M3.1-Flash vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-wevisone-vl-2b.md)\n- [Mistral Large 4 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mistral-large-4-vs-otel-2-0-llm-31b-it.md)\n- [Mistral Large 4 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mistral-large-4-vs-wevisone-vl-2b.md)\n- [Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-otel-2-0-llm-31b-it.md)\n- [Muse Glimmer 30B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-wevisone-vl-2b.md)\n- [Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-otel-2-0-llm-31b-it.md)\n- [Nanbeige4.2-3B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-wevisone-vl-2b.md)\n- [Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-otel-2-0-llm-31b-it.md)\n- [Nanbeige4.3-33B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-wevisone-vl-2b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-otel-2-0-llm-31b-it.md)\n- [NVIDIA Nemotron 3.5 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