Head to head · Open-weight models you run yourself · October 2026 research run
OTel-2.0-LLM-31B-IT vs Qwen3-VL-30B-A3B-Instruct
Qwen3-VL-30B-A3B-Instruct scores 58 (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.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware
Ahead on
- Documentation and upkeep, 74 against 36
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Best for An agent with 24 GB or more to spare that wants a local reader of images, documents and video with 262,144 tokens of context
Ahead on
- Reach, 95 against 50
Watch for
No safety evaluation, agent-use guidance or security contact
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Qwen3-VL-30B-A3B-Instruct +45
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.
Qwen3-VL-30B-A3B-Instruct 95
Alibaba's own Q4_K_M GGUF is 18.6 GB, so with a fifth added it needs about 22 GB, or 23 GB with the vision file, and fits 24 GB (25). Transformers 4.57.0, vLLM 0.11.0, SGLang 0.5.4, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). Novita, Featherless AI and DeepInfra serve it on Hugging Face, and SiliconFlow too on OpenRouter (5). 25 + 25 + 15 + 15 + 10 + 5 = 95.
Documentation and upkeep OTel-2.0-LLM-31B-IT +38
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.
Qwen3-VL-30B-A3B-Instruct 36
The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The newest Hub commit is the library-tag commit of 26 November 2025 and the weights haven't changed since September 2025, so the qwen-vl-utils fix of 4 January 2026 in the code that runs it sets the line, within a year (5). Qwen answered none of the eight Hugging Face discussions that aren't pull requests (0), and GitHub issues weren't counted. Earlier versions kept in their own repositories without notes (5). Config, template and card agree and no broken-file report waits (10). 11 + 5 + 5 + 0 + 5 + 10 = 36.
Score by category
| Category | Weight this run | OTel-2.0-LLM-31B-IT | Qwen3-VL-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 95 | Qwen3-VL-30B-A3B-Instruct +45 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 74 | 36 | OTel-2.0-LLM-31B-IT +38 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 58/100 |
Facts side by side
| Fact | OTel-2.0-LLM-31B-IT | Qwen3-VL-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | AT&T (Open Telco AI) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-01 | 2025-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
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.
Qwen3-VL-30B-A3B-Instruct
A vision-language mixture of experts for screenshots, documents, charts and video, with about 3B of its 31B parameters active. Alibaba's Q4_K_M GGUF with its vision file needs about 23 GB, it loads in Transformers, vLLM, SGLang, llama.cpp and Ollama under Apache 2.0, and three providers host it. No safety work is published, so add guardrails before it drives a GUI.
Before you call either
OTel-2.0-LLM-31B-IT
- 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
Qwen3-VL-30B-A3B-Instruct
- Start it with
llama-server -hf Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF:Q4_K_M --jinja -c 32768, orollama run qwen3-vl:30b-a3b-instruct - In Ollama the plain
qwen3-vl:30btag is the Thinking edition, so name30b-a3b-instruct - With the GGUF, load the mmproj vision file too, or images won't be read
- Parse tool calls as JSON inside
<tool_call>tags, or use SGLang's--tool-call-parser qwen - Use the shipped sampling, temperature 0.7, top_p 0.8 and top_k 20, from generation_config.json
- Validate JSON output yourself, since structured output isn't documented
- vLLM needs 0.11.0 or later, Transformers 4.57.0 or later and llama.cpp b6907 or later
Questions
Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3-VL-30B-A3B-Instruct?
Qwen3-VL-30B-A3B-Instruct scores 58 (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.
Other comparisons with OTel-2.0-LLM-31B-IT or Qwen3-VL-30B-A3B-Instruct
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- Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT
- Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct
- Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT
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- Qwen3.5-4B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-2B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-30B-A3B-Instruct vs Recursive Flint
- Qwen3-VL-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-VL-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2
- Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-VL-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-VL-30B-A3B-Instruct vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/otel-2-0-llm-31b-it.json·/api/v1/tools/qwen3-vl-30b-a3b-instruct.json - From a terminal
anchor compare otel-2-0-llm-31b-it qwen3-vl-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "otel-2-0-llm-31b-it", "b": "qwen3-vl-30b-a3b-instruct"}at/mcp, no key