Head to head · Open-weight models you run yourself · October 2026 research run
OTel-2.0-LLM-31B-IT vs Qwen3-Coder-30B-A3B-Instruct
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach and agent interface. 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 31
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Qwen3-Coder-30B-A3B-Instruct C
Best for Coding agents and local coding tools that want an open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.
Ahead on
- Reach, 90 against 50
- Agent interface, 75 against 60
Watch for
No safety results, prompt-injection guidance or security contact, though it's meant to run commands
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.
Documentation and upkeep OTel-2.0-LLM-31B-IT +43
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-Coder-30B-A3B-Instruct 31
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, the blog describes the mix in general terms without a cut-off. Last change 5, config.json was corrected on 3 December 2025, 336 days before the 4 November 2026 check. Responsiveness 0, counted by script on 4 November, none of 26 discussions has a reply from Alibaba's Qwen staff, and the one Hub-flagged member who replied is the independent quantiser bartowski. Version policy 5, a first release. File health 5, the files now agree with the card, but reports about the parser and an attention error wait without a reply. 11 + 5 + 5 + 0 + 5 + 5 = 31.
Reach Qwen3-Coder-30B-A3B-Instruct +40
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-Coder-30B-A3B-Instruct 90
Memory 25 of 30, LM Studio's MLX 4-bit build is 17.2 GB and Ollama's 4-bit build 19 GB, 21 to 23 GB with a fifth added, which fits 24 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3_moe architecture, vLLM and SGLang carry a qwen3_coder parser, Ollama's released build pins a llama.cpp that has qwen3moe, and LM Studio's catalogue carries it. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, Featherless AI on Hugging Face (live on 4 November 2026) plus SiliconFlow and Amazon Bedrock on OpenRouter (per 21 October, not re-read) serve it. 25 + 25 + 10 + 15 + 10 + 5 = 90.
Agent interface Qwen3-Coder-30B-A3B-Instruct +15
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.
Qwen3-Coder-30B-A3B-Instruct 75
Chat template 20, chat_template.jinja in the repository. Tool-calling format 25, the card shows how tools are declared, the README names the parser for SGLang and vLLM, and the repository ships it. Trained tool calling 20, the template renders several calls a turn and the training used agent reinforcement learning. Structured output 0, not documented. Reasoning control 0, the model has no thinking. Context 10, 262,144 tokens native, with the YaRN extension not counted. Template, parser and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75.
Score by category
| Category | Weight this run | OTel-2.0-LLM-31B-IT | Qwen3-Coder-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 90 | Qwen3-Coder-30B-A3B-Instruct +40 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |
| Documentation and upkeep | 10%10 | 74 | 31 | OTel-2.0-LLM-31B-IT +43 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 55.7/100 |
Facts side by side
| Fact | OTel-2.0-LLM-31B-IT | Qwen3-Coder-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 | 2025-12-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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-Coder-30B-A3B-Instruct
A coding model with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits under Apache 2.0, and several providers host it. It runs commands for agents with no safety results or prompt-injection guidance, and Alibaba hasn't answered its discussions.
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-Coder-30B-A3B-Instruct
- Quickest start is
ollama run qwen3-coder:30b, 19 GB - On our reference machine use BF16 with
vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder, 61 GB of weights fits the GPU share - Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON
- Pass tool-call arguments in the history as objects, the template iterates them as a mapping
- Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens
- For code completion use the fill-in-the-middle tokens
<|fim_prefix|>,<|fim_suffix|>and<|fim_middle|>from Qwen's README - Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection
Questions
Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3-Coder-30B-A3B-Instruct?
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen3-Coder-30B-A3B-Instruct's 55.7 (C), and leads in 1 of 4 scored categories. Qwen3-Coder-30B-A3B-Instruct leads on reach and agent interface.
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- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3-4B-Instruct-2507 vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-0.8B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-122B-A10B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-2B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-397B-A17B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-4B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-9B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Coder-Next
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint
- Qwen3-Coder-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Coder-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Coder-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2
- Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Coder-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Coder-30B-A3B-Instruct vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/otel-2-0-llm-31b-it-vs-qwen3-coder-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-coder-30b-a3b-instruct.json - From a terminal
anchor compare otel-2-0-llm-31b-it qwen3-coder-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "otel-2-0-llm-31b-it", "b": "qwen3-coder-30b-a3b-instruct"}at/mcp, no key