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

OTel-2.0-LLM-31B-IT vs Qwopus3.8-27B-Flash-V2

Qwopus3.8-27B-Flash-V2 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 risk and 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

OTel-2.0-LLM-31B-ITAT&T (Open Telco AI) · Open-weight model C56.8/100#111 of 160 open-weight models Qwopus3.8-27B-Flash-V2Jackrong · Open-weight model C61/100#53 of 160 open-weight models

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

  • Risk, 55 against 50
  • Documentation and upkeep, 74 against 51

Watch for

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

Qwopus3.8-27B-Flash-V2 C

Best for Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.

Ahead on

  • Reach, 85 against 50

Watch for

Published from a personal account, with no organisation, security contact or safety evaluation

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 Qwopus3.8-27B-Flash-V2 +35

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.

Qwopus3.8-27B-Flash-V2 85

Memory 25 of 30, Jackrong's IQ4_XS GGUF is 15.4 GB, about 18.5 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps Qwen3.8-27B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, while Ollama's entry is the base's, not this model's. Builds 15, GGUFs at many precisions from 4-bit to 8-bit and BF16. Terms 15, Apache 2.0 through the chain. Download 10, nothing is gated. Hosted 0, no provider serves it.

Documentation and upkeep OTel-2.0-LLM-31B-IT +23

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.

Qwopus3.8-27B-Flash-V2 51

Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure.

Risk OTel-2.0-LLM-31B-IT +5

OTel-2.0-LLM-31B-IT 55

File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT&T's or GSMA's site links to: GSMA's article names AT&T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55.

Qwopus3.8-27B-Flash-V2 50

File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy.

Score by category

CategoryWeight this runOTel-2.0-LLM-31B-ITQwopus3.8-27B-Flash-V2Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255085Qwopus3.8-27B-Flash-V2 +35
Risk20%205550OTel-2.0-LLM-31B-IT +5
Agent interface10%106060even
Documentation and upkeep10%107451OTel-2.0-LLM-31B-IT +23
Negative events≤1500
TotalC 56.8/100C 61/100

Facts side by side

FactOTel-2.0-LLM-31B-ITQwopus3.8-27B-Flash-V2
KindOpen-weight modelOpen-weight model
VendorAT&T (Open Telco AI)Jackrong
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0
Read-only variant documentednono
llms.txtnono
Last release2026-11-012026-09-21
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.5/5 (4)

How the scores have moved

OTel-2.0-LLM-31B-IT

No score change since it was listed.

Qwopus3.8-27B-Flash-V2

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.

Qwopus3.8-27B-Flash-V2

A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.

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

Qwopus3.8-27B-Flash-V2

  1. Quickest start is llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja, about 17 GB, and add mmproj-F32.gguf for images
  2. The card's tests used Q5_K_M with the bundled MTP head and --spec-type draft-mtp in llama.cpp
  3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
  4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
  5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
  6. Earlier reasoning stays in the context unless preserve_thinking is false
  7. Cap max_tokens, since a report says it loops more than V1

Questions

Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwopus3.8-27B-Flash-V2?

Qwopus3.8-27B-Flash-V2 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 risk and documentation and upkeep.

Other comparisons with OTel-2.0-LLM-31B-IT or Qwopus3.8-27B-Flash-V2

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