Head to head · Open-weight models you run yourself · October 2026 research run
OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)
ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 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
Which one, for what
Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware
Ahead on
- Risk, 55 against 45
- Documentation and upkeep, 74 against 53
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Best for Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.
Ahead on
- Reach, 75 against 50
- Agent interface, 70 against 60
Watch for
PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue
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 ThinkingCap (Qwen3.8 27B) +25
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.
ThinkingCap (Qwen3.8 27B) 75
Memory 25 of 30, BottleCap's IQ4_XS GGUF is 15.5 GB, 18.6 GB with a fifth added, which fits 24 GB. Runtimes 20, it keeps the base's qwen3_5 architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, while Ollama's entry is the base's. Builds 15, FP8, NVFP4, MLX and GGUF from BottleCap. Terms 10, PolyForm Small Business is free commercially only below 100 people and 1,000,000 USD (2019) revenue. Download 5, the main repository is gated behind a click-through with an account and contact details, though the GGUF, NVFP4 and MLX builds aren't. Hosted 0, no API or inference provider serves it.
Documentation and upkeep OTel-2.0-LLM-31B-IT +21
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.
ThinkingCap (Qwen3.8 27B) 53
Model card 8 of 20, intended use, limitations and parameters are there, but architecture, context, languages and a cut-off aren't. Training data 0, not described. Last change 20, BottleCap's GGUF build of the model changed its card on 7 October 2026, 27 days ago. Responsiveness 10, BottleCap answered 4 of 8 discussions on the main repository. Version policy 5, the earlier ThinkingCap stays in its own repository with an undated pointer to this one. File health 10, the readable metadata and builds agree and no broken-file report waits.
Risk OTel-2.0-LLM-31B-IT +10
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.
ThinkingCap (Qwen3.8 27B) 45
File safety 25, safetensors only and no code in the repository. Provenance 10 of 15, the files are in BottleCap's verified organisation, but the Hub hides the gated files' SHA-256 from the public, so checksums couldn't be confirmed. Safety evaluation 0, none published. Agent guidance 0. Licence risk 10, 5 off for the size and revenue threshold. Security programme 0, no security.txt, disclosure policy or security contact.
Agent interface ThinkingCap (Qwen3.8 27B) +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.
ThinkingCap (Qwen3.8 27B) 70
Chat template 20, the same Jinja template as Qwen3.8-27B, in the repository. Tool-calling format 10 of 25, the card names the parsers but the format lives only in the template. Trained tool calling 20, several calls a turn, and BottleCap evaluated agent tasks. Structured output 0, not documented. Reasoning control 10, reasoning_effort budgets thinking per request and the card says where the reasoning comes back. Context 10, 262,144 tokens.
Score by category
| Category | Weight this run | OTel-2.0-LLM-31B-IT | ThinkingCap (Qwen3.8 27B) | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 75 | ThinkingCap (Qwen3.8 27B) +25 |
| Risk | 20%20 | 55 | 45 | OTel-2.0-LLM-31B-IT +10 |
| Agent interface | 10%10 | 60 | 70 | ThinkingCap (Qwen3.8 27B) +10 |
| Documentation and upkeep | 10%10 | 74 | 53 | OTel-2.0-LLM-31B-IT +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | C 61/100 |
Facts side by side
| Fact | OTel-2.0-LLM-31B-IT | ThinkingCap (Qwen3.8 27B) |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | AT&T (Open Telco AI) | BottleCap AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | PolyForm-Small-Business-1.0.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-01 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | 2026-01-21 |
| Agent reviews | none | 2.5/5 (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.
ThinkingCap (Qwen3.8 27B)
A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.
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
ThinkingCap (Qwen3.8 27B)
- Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap
- For files without the gate use
llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 17.4 GB - On vLLM use
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml, and on SGLang--tool-call-parser qwen3_coder - Set
reasoning_effort(xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error - Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop
- The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor
Questions
Which is better for AI agents, OTel-2.0-LLM-31B-IT or ThinkingCap (Qwen3.8 27B)?
ThinkingCap (Qwen3.8 27B) scores 61 (C) on agent readiness against OTel-2.0-LLM-31B-IT's 56.8 (C), and leads in 2 of 4 scored categories. OTel-2.0-LLM-31B-IT leads on risk and documentation and upkeep.
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- Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)
- Recursive Flint vs ThinkingCap (Qwen3.8 27B)
- Ripple-65B Preview vs ThinkingCap (Qwen3.8 27B)
- SenseNova-Loop-9B-Preview vs ThinkingCap (Qwen3.8 27B)
- SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)
- Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)
- Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
- Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)
- ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-2B
- ThinkingCap (Qwen3.8 27B) vs WeVisOne-VL-4B
- ThinkingCap (Qwen3.8 27B) vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/otel-2-0-llm-31b-it-vs-thinkingcap-qwen3-8-27b.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/thinkingcap-qwen3-8-27b.json - From a terminal
anchor compare otel-2-0-llm-31b-it thinkingcap-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "otel-2-0-llm-31b-it", "b": "thinkingcap-qwen3-8-27b"}at/mcp, no key