Head to head · Open-weight models you run yourself · October 2026 research run
OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories. 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
- Reach, 50 against 40
- Risk, 55 against 50
- Documentation and upkeep, 74 against 44
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Best for Researchers who want to study or fine-tune a looped transformer, more than agents that need a model to call today
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No released runtime runs it. vLLM and SGLang need unmerged pull requests
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 +30
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.
SenseNova-Loop-9B-Preview 44
Model card 14, Intended use, the architecture, the parameter counts, the training context and the languages (in the metadata) are on the card. Training data 5, one general sentence on the recipes, with no sources, sizes or cut-off. Last change 20, the last commit, on 2 November 2026, reworked the speculative decoding section of the card, 8 days before this check. Responsiveness 0, none of the 5 discussions that aren't pull requests (30 October to 9 November 2026) has a reply from a sensenova member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, A first release, labelled a preview, with dated news on the card. File health 0, the files contradict the card on precision. 14 + 5 + 20 + 0 + 5 + 0 = 44.
Reach OTel-2.0-LLM-31B-IT +10
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.
SenseNova-Loop-9B-Preview 40
Memory 15, the only build is the safetensors set, 38.14 GB with its MTP head, since the Hub counts 9.41B of its parameters in F32. Runtimes 0, none of the six runs it in a released version. Builds 0, SenseNova publishes one set of weights and no quantised build. Terms 15, Apache 2.0 in the card's metadata and on the card, which links the LICENSE in the GitHub repository, and nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 15 + 0 + 0 + 15 + 10 + 0 = 40.
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.
SenseNova-Loop-9B-Preview 50
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the sensenova organisation, which the GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on instructions inside tool results or images. Licence risk 15, Apache 2.0 with no use policy, no threshold and no right to change the terms. Security programme 5, SenseTime's about page gives a security contact (5). 15 + 15 + 0 + 0 + 15 + 5 = 50.
Score by category
| Category | Weight this run | OTel-2.0-LLM-31B-IT | SenseNova-Loop-9B-Preview | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 40 | OTel-2.0-LLM-31B-IT +10 |
| Risk | 20%20 | 55 | 50 | OTel-2.0-LLM-31B-IT +5 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 74 | 44 | OTel-2.0-LLM-31B-IT +30 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | D 46.8/100 |
Facts side by side
| Fact | OTel-2.0-LLM-31B-IT | SenseNova-Loop-9B-Preview |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | AT&T (Open Telco AI) | SenseTime (SenseNova) |
| 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 | 2026-11-02 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no 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.
SenseNova-Loop-9B-Preview
An unusual architecture under Apache 2.0, with a 256K training context, a thinking switch and tool calling in the template. It's a preview in practice too: no released runtime runs it, loading it runs the repository's code, the files are twice the size the card implies, and nobody from SenseNova has answered a question yet.
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
SenseNova-Loop-9B-Preview
- Read modeling_sensenova_loop.py before you load it. trust_remote_code runs it
- Pin transformers 5.18.0, the version the card checked
- Load with dtype=torch.bfloat16 as the card does, or the 38 GB of F32 weights stay at full size
- Pass enable_thinking=False to apply_chat_template for a direct answer
- Tool calls come back as tool_call blocks with function and parameter tags. Parse them yourself, since no runtime ships a parser
- Wait for the vLLM or SGLang pull request to land in a release before putting it behind an API
Questions
Which is better for AI agents, OTel-2.0-LLM-31B-IT or SenseNova-Loop-9B-Preview?
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against SenseNova-Loop-9B-Preview's 46.8 (D), and leads in 3 of 4 scored categories.
Other comparisons with OTel-2.0-LLM-31B-IT or SenseNova-Loop-9B-Preview
- Agents-A1-4B vs OTel-2.0-LLM-31B-IT
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- Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT
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- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
- Qwen-AgentWorld-35B-A3B vs SenseNova-Loop-9B-Preview
- Qwen3-30B-A3B-Instruct-2507 vs SenseNova-Loop-9B-Preview
- Qwen3-4B-Instruct-2507 vs SenseNova-Loop-9B-Preview
- Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview
- Qwen3.5-122B-A10B vs SenseNova-Loop-9B-Preview
- Qwen3.5-2B vs SenseNova-Loop-9B-Preview
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- Qwen3.5-4B vs SenseNova-Loop-9B-Preview
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- Qwen3.8-27B vs SenseNova-Loop-9B-Preview
- Qwen3.8-Flash-Next vs SenseNova-Loop-9B-Preview
- Qwen3-Coder-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-Coder-Next vs SenseNova-Loop-9B-Preview
- Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-2B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-4B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-VL-8B-Instruct vs SenseNova-Loop-9B-Preview
- Qwopus3.8-27B-Flash-V2 vs SenseNova-Loop-9B-Preview
- Recursive Flint vs SenseNova-Loop-9B-Preview
- Ripple-65B Preview vs SenseNova-Loop-9B-Preview
- SenseNova-Loop-9B-Preview vs SmolLM3-3B
- SenseNova-Loop-9B-Preview vs Spark-X2.6-4B
- SenseNova-Loop-9B-Preview vs Step 3.7 Flash
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Machine-readable
- This page as Markdown
/compare/otel-2-0-llm-31b-it-vs-sensenova-loop-9b-preview.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/sensenova-loop-9b-preview.json - From a terminal
anchor compare otel-2-0-llm-31b-it sensenova-loop-9b-preview(the CLI) - Over MCP
compare_tools {"a": "otel-2-0-llm-31b-it", "b": "sensenova-loop-9b-preview"}at/mcp, no key