Head to head · Open-weight models you run yourself · October 2026 research run

OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B

OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach. 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

OTel-2.0-LLM-31B-IT C

Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware

Ahead on

  • Agent interface, 60 against 50
  • Documentation and upkeep, 74 against 41

Watch for

BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked

Qwen-AgentWorld-35B-A3B D

Best for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine

Ahead on

  • Reach, 55 against 50

Watch for

Not a general assistant, and the card doesn't describe using it to call tools

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 +33

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.

Qwen-AgentWorld-35B-A3B 41

Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length. Training data 5, A general description without a cut-off, the three training stages and a statement that no outputs from external API services were included. Last change 10, the last commit was on 25 June 2026, an update to the card, 138 days before this check, past 90 days and within 180. Responsiveness 0, 10 discussions that aren't pull requests, opened between 24 June and 5 July 2026. Version policy 5, A first release, with nothing replaced. File health 10, Config, both copies of the template and the card agree on the architecture and the context. 11 + 5 + 10 + 0 + 5 + 10 = 41.

Agent interface OTel-2.0-LLM-31B-IT +10

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.

Qwen-AgentWorld-35B-A3B 50

Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 10, the template renders several tool_call blocks in one turn, but the card presents the model as a simulator of what tools and environments return, not as a tool caller. Structured output 0, no JSON or JSON Schema output documented for the model. Reasoning control 0, the card says thinking is on by default and names the qwen3 reasoning parser, but doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens native per the card and the text config. 20 + 10 + 10 + 0 + 0 + 10 = 50.

Reach Qwen-AgentWorld-35B-A3B +5

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.

Qwen-AgentWorld-35B-A3B 55

Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share. Runtimes 20, Four released runtimes run the architecture, vLLM 0.33.0 (Qwen3_5MoeForConditionalGeneration), SGLang 0.5.23, Transformers 5.22.2 (qwen3_5_moe) and llama.cpp b12242 (its Qwen conversion code). Builds 0, Alibaba publishes the BF16 weights only. Terms 15, Apache 2.0, an OSI licence, in the metadata and as a LICENSE file, with no use policy or other terms added. Download 10, the repository isn't gated, so the files download with no account and no terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026). 10 + 20 + 0 + 15 + 10 + 0 = 55.

Score by category

CategoryWeight this runOTel-2.0-LLM-31B-ITQwen-AgentWorld-35B-A3BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255055Qwen-AgentWorld-35B-A3B +5
Risk20%205555even
Agent interface10%106050OTel-2.0-LLM-31B-IT +10
Documentation and upkeep10%107441OTel-2.0-LLM-31B-IT +33
Negative events≤1500
TotalC 56.8/100D 52.1/100

Facts side by side

FactOTel-2.0-LLM-31B-ITQwen-AgentWorld-35B-A3B
KindOpen-weight modelOpen-weight model
VendorAT&T (Open Telco AI)Alibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-012026-06-25
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked

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.

Qwen-AgentWorld-35B-A3B

A specialist, not a chat model. It plays the environment an agent acts in, across seven domains, so you can train or test an agent without the real thing. Alibaba ships BF16 only, which fits a 128 GB machine, and hasn't answered a discussion on it or changed the files since June.

Before you call either

OTel-2.0-LLM-31B-IT

  1. Download the 15 shards the index names, not the whole repository, which is twice the size
  2. Pin a revision. The card says the weights may change under the same name
  3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's
  4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel
  5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review
  6. Don't use it to run network tools unattended. The card says that hasn't been evaluated

Qwen-AgentWorld-35B-A3B

  1. Use it as the environment, not the agent. Give it the domain's system prompt from Alibaba's GitHub repository, the history and the agent's action, and it returns the predicted observation
  2. With vLLM pass --language-model-only, or startup fails on the missing vision weights
  3. Use temperature 0.6, top_p 0.95 and top_k 20, and allow up to 32,768 output tokens, as the card recommends
  4. Keep at least 128K of context, since the simulation depends on the history
  5. Thinking is on by default and comes back through the qwen3 reasoning parser
  6. A simulated observation is a prediction. Check anything that matters against the real environment

Questions

Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen-AgentWorld-35B-A3B?

OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen-AgentWorld-35B-A3B's 52.1 (D), and leads in 2 of 4 scored categories. Qwen-AgentWorld-35B-A3B leads on reach.

Other comparisons with OTel-2.0-LLM-31B-IT or Qwen-AgentWorld-35B-A3B

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