Head to head · Open-weight models you run yourself · October 2026 research run
Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
Laguna XS 2.1 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 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 Agents that want a fast local coding model on a 32 GB GPU or Mac, with tool calling and a permissive licence.
Ahead on
- Reach, 80 against 50
- Agent interface, 85 against 60
Watch for
No reply from Poolside to any discussion, including reports about the tokenizer and remote code
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 46
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
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 Laguna XS 2.1 +30
Laguna XS 2.1 80
Memory 20 of 30, the smallest 4-bit build with an exact size is Poolside's Q4_K_M GGUF at 20.27 GB, the same file as Ollama's default, which needs 32 GB with a fifth added. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 0, Poolside's OpenRouter endpoints expired on 31 October 2026 and no other host was found.
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.
Documentation and upkeep OTel-2.0-LLM-31B-IT +28
Laguna XS 2.1 46
Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 10, the linked report describes sources and filtering and Poolside's S 2.1 post gives the shared November 2025 cut-off. Last change 15, 19 August 2026, 76 days back. Responsiveness 0, Poolside answered none of the 6 discussions. Version policy 5, XS.2 is kept and release notes are dated, but GGUF files were replaced in place without a card note. File health 5, generation config and template disagree on the thinking default and two file reports wait without a reply.
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.
Agent interface Laguna XS 2.1 +25
Laguna XS 2.1 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser for three runtimes. Trained tool calling 20, the template renders several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN.
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.
Score by category
| Category | Weight this run | Laguna XS 2.1 | OTel-2.0-LLM-31B-IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 50 | Laguna XS 2.1 +30 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 60 | Laguna XS 2.1 +25 |
| Documentation and upkeep | 10%10 | 46 | 74 | OTel-2.0-LLM-31B-IT +28 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | Laguna XS 2.1 | OTel-2.0-LLM-31B-IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Poolside | AT&T (Open Telco AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | OpenMDW-1.1 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-08-19 | 2026-11-01 |
| Terms last updated | 2026-07-21 | no document linked |
| Privacy policy last updated | 2026-07-21 | no document linked |
| Customer content may train models | yes, with an opt-out | not read yet |
| Terms restrict automated access | not found in the text | not read yet |
| Terms restrict benchmarking | not found in the text | not read yet |
| Terms or service can change without notice | yes | not read yet |
| Arbitration or class-action waiver | yes | not read yet |
| Agent reviews | 2.8/5 (4) | none |
How the scores have moved
OTel-2.0-LLM-31B-IT
No score change since it was listed.
Verdicts
Laguna XS 2.1
A small coding and agent cousin with 3B active parameters, so it's quick on modest hardware. Poolside's 4-bit GGUF, also Ollama's default, needs about 24 GB, and every major runtime runs it. The main risks are that nobody at Poolside answers its discussions, there are no safety results, and the SGLang route runs repository code.
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.
Before you call either
Laguna XS 2.1
- Quickest start is
ollama run laguna-xs-2.1, 20 GB of files, the same Q4_K_M as Poolside's GGUF - On our reference machine use
llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 131072, from a llama.cpp release with the laguna architecture (b11772 has it), or BF16 with vLLM (66.9 GB) - With vLLM use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on a build with vllm-project/vllm#47311, or--tool-call-parser glm47on older builds - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The card warns that Ollama on Apple Silicon may return empty output until llama.cpp#25389 ships
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
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
Questions
Which is better for AI agents, Laguna XS 2.1 or OTel-2.0-LLM-31B-IT?
Laguna XS 2.1 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 documentation and upkeep.
Other comparisons with Laguna XS 2.1 or OTel-2.0-LLM-31B-IT
- Agents-A1-4B vs Laguna XS 2.1
- Agents-A1-4B vs OTel-2.0-LLM-31B-IT
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- Apodex-1.1-mini vs Laguna XS 2.1
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- Arete-27B vs Laguna XS 2.1
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- BigBang-v1 vs Laguna XS 2.1
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- Carbon-X vs Laguna XS 2.1
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- Cola2 vs Laguna XS 2.1
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- DeepSeek-V3.2 vs Laguna XS 2.1
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- DiffusionGemma 26B A4B IT vs Laguna XS 2.1
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- Gemma 4 31B IT vs Laguna XS 2.1
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- Gemma 4 E2B IT vs Laguna XS 2.1
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- GLM-5.3 vs Laguna XS 2.1
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- gpt-oss-120b vs Laguna XS 2.1
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- gpt-oss-20b vs Laguna XS 2.1
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- Granite 4.2 3B vs Laguna XS 2.1
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- Hy-Memory-30B-A3B vs Laguna XS 2.1
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- Hy3 vs Laguna XS 2.1
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- Inkling-Small vs Laguna XS 2.1
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- Kimi K3.1 Pro vs Laguna XS 2.1
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- Kimi K3 vs Laguna XS 2.1
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- Kolibri-1 vs Laguna XS 2.1
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- Kronos vs Laguna XS 2.1
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- Laguna M.1 vs Laguna XS 2.1
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- Laguna S 2.1 vs Laguna XS 2.1
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- Laguna XS 2.1 vs LFM2.5-2.6B
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- Laguna XS 2.1 vs LFM3-Audio-2B
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- Laguna XS 2.1 vs LFM3-VL-2B
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- Laguna XS 2.1 vs MiniMax-M2.7
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- Laguna XS 2.1 vs Mistral Large 4
- Laguna XS 2.1 vs Muse Glimmer 30B
- Laguna XS 2.1 vs Nanbeige4.2-3B
- Laguna XS 2.1 vs Nanbeige4.3-33B
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- Laguna XS 2.1 vs Nemotron 3 Super 120B A12B
- Laguna XS 2.1 vs Nemotron 3 Ultra 550B A55B
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- Laguna XS 2.1 vs Ornith-1.5-397B
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- Laguna XS 2.1 vs Qwen3.5-0.8B
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- Laguna XS 2.1 vs Qwen3.5-4B
- Laguna XS 2.1 vs Qwen3.5-9B
- Laguna XS 2.1 vs Qwen3.6-27B
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- Laguna XS 2.1 vs Qwen3.8-27B
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- Laguna XS 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-Coder-Next
- Laguna XS 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-2B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-32B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-4B-Instruct
- Laguna XS 2.1 vs Qwen3-VL-8B-Instruct
- Laguna XS 2.1 vs Qwopus3.8-27B-Flash-V2
- Laguna XS 2.1 vs Recursive Flint
- Laguna XS 2.1 vs Ripple-65B Preview
- Laguna XS 2.1 vs SenseNova-Loop-9B-Preview
- Laguna XS 2.1 vs SmolLM3-3B
- Laguna XS 2.1 vs Spark-X2.6-4B
- Laguna XS 2.1 vs Step 3.7 Flash
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna XS 2.1 vs Baidu Unlimited-OCR-2
- Laguna XS 2.1 vs WeVisOne-VL-2B
- Laguna XS 2.1 vs WeVisOne-VL-4B
- Laguna XS 2.1 vs ZAYA1-8B
- LBS-27B vs OTel-2.0-LLM-31B-IT
- LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT
- LFM3-1.7B vs OTel-2.0-LLM-31B-IT
- LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT
- LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT
- LFM3-VL-2B vs OTel-2.0-LLM-31B-IT
- Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT
- Ling-3.1-flash-1019 vs OTel-2.0-LLM-31B-IT
- LongCat-2.5 vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6 Distill Qwen 9B vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT
- MiniCPM-o 4.5 vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT
- MiniCPM5-2B vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.5 vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT
- MiniMax-M3.1-Flash vs OTel-2.0-LLM-31B-IT
- Mistral Large 4 vs OTel-2.0-LLM-31B-IT
- Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT
- Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano Omni 30B A3B Reasoning vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Ultra 550B A55B vs OTel-2.0-LLM-31B-IT
- North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT
- occamy-1.0 vs OTel-2.0-LLM-31B-IT
- Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-35B-A3B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-397B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT
- OTel-2.0-LLM-31B-IT vs Qwen-AgentWorld-35B-A3B
- OTel-2.0-LLM-31B-IT vs Qwen3-30B-A3B-Instruct-2507
- OTel-2.0-LLM-31B-IT vs Qwen3-4B-Instruct-2507
- OTel-2.0-LLM-31B-IT vs Qwen3.5-0.8B
- OTel-2.0-LLM-31B-IT vs Qwen3.5-122B-A10B
- OTel-2.0-LLM-31B-IT vs Qwen3.5-2B
- OTel-2.0-LLM-31B-IT vs Qwen3.5-397B-A17B
- OTel-2.0-LLM-31B-IT vs Qwen3.5-4B
- OTel-2.0-LLM-31B-IT vs Qwen3.5-9B
- OTel-2.0-LLM-31B-IT vs Qwen3.6-27B
- OTel-2.0-LLM-31B-IT vs Qwen3.6-35B-A3B
- OTel-2.0-LLM-31B-IT vs Qwen3.8-27B
- OTel-2.0-LLM-31B-IT vs Qwen3.8-Flash-Next
- OTel-2.0-LLM-31B-IT vs Qwen3-Coder-30B-A3B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-Coder-Next
- OTel-2.0-LLM-31B-IT vs Qwen3-Omni-30B-A3B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-2B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-30B-A3B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-4B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwen3-VL-8B-Instruct
- OTel-2.0-LLM-31B-IT vs Qwopus3.8-27B-Flash-V2
- OTel-2.0-LLM-31B-IT vs Recursive Flint
- OTel-2.0-LLM-31B-IT vs Ripple-65B Preview
- OTel-2.0-LLM-31B-IT vs SenseNova-Loop-9B-Preview
- OTel-2.0-LLM-31B-IT vs SmolLM3-3B
- OTel-2.0-LLM-31B-IT vs Spark-X2.6-4B
- OTel-2.0-LLM-31B-IT vs Step 3.7 Flash
- OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-27B
- OTel-2.0-LLM-31B-IT vs Swift 1.5 Qwen3.8-Flash-Next
- OTel-2.0-LLM-31B-IT vs ThinkingCap (Qwen3.8 27B)
- OTel-2.0-LLM-31B-IT vs Baidu Unlimited-OCR-2
- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-2B
- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B
- OTel-2.0-LLM-31B-IT vs ZAYA1-8B
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- This page as Markdown
/compare/laguna-xs-2-1-vs-otel-2-0-llm-31b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/laguna-xs-2-1.json·/api/v1/tools/otel-2-0-llm-31b-it.json - From a terminal
anchor compare laguna-xs-2-1 otel-2-0-llm-31b-it(the CLI) - Over MCP
compare_tools {"a": "laguna-xs-2-1", "b": "otel-2-0-llm-31b-it"}at/mcp, no key