{ "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": "ZAYA1-8B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on documentation and upkeep.", "b": { "slug": "zaya1-8b", "name": "ZAYA1-8B", "vendor": "Zyphra", "vendorUrl": "https://www.zyphra.com", "kind": "weights", "category": "open-weights", "summary": "ZAYA1-8B is Zyphra's open-weight reasoning model, a mixture of experts of 8.4B parameters with 760M active, trained from scratch for maths and coding. Apache 2.0. Zyphra's 4-bit build is 5.9 GB. Released SGLang and Transformers run it.", "url": "https://www.anchorterminal.com/tools/zaya1-8b", "markdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zaya1-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zaya1-8b.json", "dns": "zaya1-8b.grade.anchorterminal.com", "repo": "https://huggingface.co/Zyphra/ZAYA1-8B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B" }, { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B-FP8-Experts" }, { "registry": "huggingface", "name": "Zyphra/ZAYA1-8B-MXFP4-Experts" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0, per the main repository's metadata. Running it yourself costs little: 5.9 GB with the experts at 4 bits (MXFP4), 9.6 GB in FP8 or 17.7 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://huggingface.co/Zyphra/ZAYA1-8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "small", "reasoning", "maths", "coding", "tool-calling", "apache-2.0", "safetensors", "fp8", "mxfp4", "self-hosted" ], "lastRelease": "2026-07-02", "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": 67, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 80, "risk": 55, "upkeep": 50 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.", "bestFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling", "strengths": [ "Apache 2.0, ungated", "5.9 GB at 4 bits, 760M parameters active", "Released Transformers and SGLang carry the architecture", "Zyphra replied in most of the recent discussions", "The original checkpoint is kept beside the reshaped one" ], "weaknesses": [ "Tool calling needs a parser that only Zyphra's vLLM fork has", "Released vLLM and llama.cpp don't run it, and there's no GGUF", "The card's install steps use forks", "No limitations, context length, languages, data or safety results on the card", "Benchmarks on the card are all from Zyphra's own harness" ], "agentNotes": [ "Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork", "Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in", "Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work", "Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes", "The quantised repositories have no licence tag. The main one is Apache 2.0" ], "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": 60, "reach": 80, "risk": 55, "upkeep": 50 }, "provenanceScore": 56, "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3\n# the card's own route is vLLM from Zyphra's fork, with --tool-call-parser zaya_xml", "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. The card recommends temperature 1.0\n# and top_p 0.95 in general, and temperature 0.6 for agent and code work" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/zaya1-8b" }, "weights": { "trainedBy": "Zyphra", "builtOn": "ZAYA1-reasoning-base, Zyphra's own pre-trained model, per the card", "params": "8.4B", "active": "760M", "context": 131072, "goodFor": "Long step-by-step reasoning in maths and code on a small machine, and as the model inside a test-time compute harness.", "runtimes": [ "SGLang", "Transformers" ], "builds": [ { "name": "ZAYA1-8B-MXFP4-Experts", "url": "https://huggingface.co/Zyphra/ZAYA1-8B-MXFP4-Experts", "who": "publisher", "precision": "mxfp4", "sizeGB": 5.85, "memoryGB": 8, "format": "safetensors", "note": "Zyphra's build with the experts at 4 bits. Which released runtime loads it wasn't established" }, { "name": "ZAYA1-8B-FP8-Experts", "url": "https://huggingface.co/Zyphra/ZAYA1-8B-FP8-Experts", "who": "publisher", "precision": "fp8", "sizeGB": 9.64, "memoryGB": 12, "format": "safetensors", "note": "Zyphra's build with the experts in FP8" }, { "name": "ZAYA1-8B (BF16 safetensors)", "url": "https://huggingface.co/Zyphra/ZAYA1-8B", "who": "publisher", "precision": "bf16", "sizeGB": 17.68, "memoryGB": 24, "format": "safetensors", "runsOn": [ "SGLang", "Transformers" ], "note": "Four shards, reshaped for Transformers" } ], "run": "python -m sglang.launch_server --model-path Zyphra/ZAYA1-8B --reasoning-parser qwen3", "runBuild": "ZAYA1-8B (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Zyphra", "family": "ZAYA1", "url": "https://www.anchorterminal.com/open-weights/#zyphra", "others": [ { "name": "ZAYA1-VL-8B", "url": "https://huggingface.co/Zyphra/ZAYA1-VL-8B" }, { "name": "ZAYA1-74B-preview", "url": "https://huggingface.co/Zyphra/ZAYA1-74B-preview" } ] }, "area": "models", "provenance": { "legalEntity": "Zyphra, named on the card and its site. The legal entity wasn't looked up", "domain": "zyphra.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Zyphra/ZAYA1-8B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "www.zyphra.com's robots.txt allows reading outside /studio and /api. Its about page gives a San Francisco address and names no people in the text read.", "The domain's registration date wasn't looked up. www.zyphra.com has no security.txt (404 on 10 November 2026).", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. Zyphra's site lists a cloud product for its open models, which wasn't read.", "The changelog is the commit history, 41 commits from 4 May to 26 June 2026." ], "score": 56 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/zaya1-8b.json" }, "edges": [ { "by": 30, "category": "reach", "leads": "zaya1-8b", "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 smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80." }, "scores": { "a": 50, "b": 80 } }, { "by": 24, "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 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50." }, "scores": { "a": 74, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "AT\u0026T (Open Telco AI)", "b": "Zyphra", "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": "2026-07-02", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "ZAYA1-8B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 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 ZAYA1-8B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 74 against 50" ], "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, 80 against 50" ], "also": null, "goodFor": "People who want a small, fast reasoning model for maths and code and can live without tool calling", "slug": "zaya1-8b", "watchFor": "Tool calling needs a parser that only Zyphra's vLLM fork has" } ], "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-zaya1-8b.json", "title": "Agents-A1-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Apertus v1.5 8B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Apodex-1.1-mini vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Arete-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "BigBang-v1 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Carbon-X vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Cola2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/cola2-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Cosmos-Reason2-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Darwin-36B-Opus vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "DeepSeek-V3.2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "DeepSeek V4.1 Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "DeepSeek-V4-Flash-0731 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Devstral Small 2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "DiffusionGemma 26B A4B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "EXAONE 4.5 33B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Gemma 4 12B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Gemma 4 26B A4B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Gemma 4 31B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Gemma 4 E2B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Gemma 4 E4B IT vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "GLM-4.5-Air vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "GLM-4.7-Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "GLM-5.3-Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "GLM-5.3 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "gpt-oss-120b vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "gpt-oss-20b vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Granite 4.2 30B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Granite 4.2 3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Granite 4.2 8B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Hy-Memory-30B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Hy3 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/hy3-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Inkling-Small vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Inkling vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/inkling-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Kimi K3.1 Pro vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-zaya1-8b" }, { "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-zaya1-8b.json", "title": "Kimi K3 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-zaya1-8b" }, { "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": 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"url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b.json", "title": "Qwen3.5-9B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.json", "title": "Qwen3.6-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b.json", "title": "Qwen3.6-35B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b.json", "title": "Qwen3.8-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-zaya1-8b.json", "title": "Qwen3.8-Flash-Next vs ZAYA1-8B", "url": 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"https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-32B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-4B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b.json", "title": "Qwen3-VL-8B-Instruct vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.json", "title": "Recursive Flint vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b.json", "title": "Ripple-65B Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ripple-65b-preview-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b.json", "title": "SenseNova-Loop-9B-Preview vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/sensenova-loop-9b-preview-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b.json", "title": "SmolLM3-3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b.json", "title": "Spark-X2.6-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json", "title": "Step 3.7 Flash vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b.json", "title": "Swift 1.5 Qwen3.8-27B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.json", "title": "ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.json", "title": "Baidu Unlimited-OCR-2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b" }, { "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/wevisone-vl-4b-vs-zaya1-8b.json", "title": "WeVisOne-VL-4B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "zaya1-8b", "key": "reach", "name": "Reach", "otel-2-0-llm-31b-it": 50, "weight": 25, "zaya1-8b": 80 }, { "by": 0, "edge": "", "key": "risk", "name": "Risk", "otel-2-0-llm-31b-it": 55, "weight": 20, "zaya1-8b": 55 }, { "by": 0, "edge": "", "key": "interface", "name": "Agent interface", "otel-2-0-llm-31b-it": 60, "weight": 10, "zaya1-8b": 60 }, { "by": 24, "edge": "otel-2-0-llm-31b-it", "key": "upkeep", "name": "Documentation and upkeep", "otel-2-0-llm-31b-it": 74, "weight": 10, "zaya1-8b": 50 } ], "summary": "ZAYA1-8B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 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.", "zaya1-8b": "A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety." } }, "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-zaya1-8b", "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.min.md" }, "markdown": "ZAYA1-8B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 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- ZAYA1-8B: grade C, 61/100, rank #85 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/zaya1-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/zaya1-8b.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 50\n\nWatch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked\n\n### ZAYA1-8B (C)\n\nGood for: People who want a small, fast reasoning model for maths and code and can live without tool calling\n\nAhead on:\n- Reach, 80 against 50\n\nWatch for: Tool calling needs a parser that only Zyphra's vLLM fork has\n\n\n## Why the edge\n\n### Reach: ZAYA1-8B +30\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- ZAYA1-8B (80): Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.\n\n### Documentation and upkeep: OTel-2.0-LLM-31B-IT +24\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- ZAYA1-8B (50): Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.\n\n## Score by category\n\n| Category | Weight | OTel-2.0-LLM-31B-IT | ZAYA1-8B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 80 | ZAYA1-8B +30 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 74 | 50 | OTel-2.0-LLM-31B-IT +24 |\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 | ZAYA1-8B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | AT\u0026T (Open Telco AI) | Zyphra |\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 | 2026-07-02 |\n| Terms last updated | no document linked | no document linked |\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**ZAYA1-8B.** A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.\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### ZAYA1-8B\n\n1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork\n2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in\n3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work\n4. Download `Zyphra/ZAYA1-8B`, not the legacy repository, for the released runtimes\n5. The quantised repositories have no licence tag. The main one is Apache 2.0\n\n## Questions\n\n### Which is better for AI agents, OTel-2.0-LLM-31B-IT or ZAYA1-8B?\n\nZAYA1-8B scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 1 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-zaya1-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"otel-2-0-llm-31b-it\", \"b\": \"zaya1-8b\"}`. From a terminal: `anchor compare otel-2-0-llm-31b-it zaya1-8b`\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/zaya1-8b.json\n\n## Other comparisons with OTel-2.0-LLM-31B-IT or ZAYA1-8B\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 ZAYA1-8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-zaya1-8b.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 ZAYA1-8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-zaya1-8b.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 ZAYA1-8B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-zaya1-8b.md)\n- [Arete-27B vs 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ZAYA1-8B](https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-zaya1-8b.md)\n- [ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B](https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-zaya1-8b.md)\n- [Baidu Unlimited-OCR-2 vs ZAYA1-8B](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-zaya1-8b.md)\n- [WeVisOne-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-2b-vs-zaya1-8b.md)\n- [WeVisOne-VL-4B vs ZAYA1-8B](https://www.anchorterminal.com/compare/wevisone-vl-4b-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": 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