Head to head · Open-weight models you run yourself · October 2026 research run
Laguna S 2.1 vs OTel-2.0-LLM-31B-IT
Laguna S 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 doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.
Ahead on
- Reach, 75 against 50
- Agent interface, 85 against 60
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
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 41
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.
Documentation and upkeep OTel-2.0-LLM-31B-IT +33
Laguna S 2.1 41
Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several 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.
Reach Laguna S 2.1 +25
Laguna S 2.1 75
Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. 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 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
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.
Agent interface Laguna S 2.1 +25
Laguna S 2.1 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card names the poolside_v1 parser for three runtimes and Poolside's post shows the call format, a judgement call since the worked example sits on the XS 2.1 card. Trained tool calling 20, several calls a turn. Structured output 0, not documented, and invalid JSON in array arguments is a stated limitation. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 1M tokens trained per Poolside's build cards.
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 S 2.1 | OTel-2.0-LLM-31B-IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 75 | 50 | Laguna S 2.1 +25 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 60 | Laguna S 2.1 +25 |
| Documentation and upkeep | 10%10 | 41 | 74 | OTel-2.0-LLM-31B-IT +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | Laguna S 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 | 2.3/5 (4) | none |
Verdicts
Laguna S 2.1
A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.
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 S 2.1
- Quickest start is
ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon - On our reference machine try
llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB - With vLLM or SGLang use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM--tool_parser poolside_v1 --reasoning_parser laguna - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality - Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
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 S 2.1 or OTel-2.0-LLM-31B-IT?
Laguna S 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 S 2.1 or OTel-2.0-LLM-31B-IT
- Agents-A1-4B vs Laguna S 2.1
- Agents-A1-4B vs OTel-2.0-LLM-31B-IT
- Apertus v1.5 8B vs Laguna S 2.1
- Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT
- Apodex-1.1-mini vs Laguna S 2.1
- Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT
- Arete-27B vs Laguna S 2.1
- Arete-27B vs OTel-2.0-LLM-31B-IT
- BigBang-v1 vs Laguna S 2.1
- BigBang-v1 vs OTel-2.0-LLM-31B-IT
- Carbon-X vs Laguna S 2.1
- Carbon-X vs OTel-2.0-LLM-31B-IT
- Cola2 vs Laguna S 2.1
- Cola2 vs OTel-2.0-LLM-31B-IT
- Cosmos-Reason2-2B vs Laguna S 2.1
- Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT
- Darwin-36B-Opus vs Laguna S 2.1
- Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT
- DeepSeek V4.1 Flash vs Laguna S 2.1
- DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT
- DeepSeek-V4-Flash-0731 vs Laguna S 2.1
- DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT
- Devstral Small 2 vs Laguna S 2.1
- Devstral Small 2 vs OTel-2.0-LLM-31B-IT
- DiffusionGemma 26B A4B IT vs Laguna S 2.1
- DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT
- EXAONE 4.5 33B vs Laguna S 2.1
- EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT
- Gemma 4 12B IT vs Laguna S 2.1
- Gemma 4 12B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 26B A4B IT vs Laguna S 2.1
- Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 31B IT vs Laguna S 2.1
- Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E2B IT vs Laguna S 2.1
- Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E4B IT vs Laguna S 2.1
- Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT
- GLM-4.5-Air vs Laguna S 2.1
- GLM-4.5-Air vs OTel-2.0-LLM-31B-IT
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3-Flash vs Laguna S 2.1
- GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3 vs Laguna S 2.1
- GLM-5.3 vs OTel-2.0-LLM-31B-IT
- gpt-oss-120b vs Laguna S 2.1
- gpt-oss-120b vs OTel-2.0-LLM-31B-IT
- gpt-oss-20b vs Laguna S 2.1
- gpt-oss-20b vs OTel-2.0-LLM-31B-IT
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 3B vs Laguna S 2.1
- Granite 4.2 3B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 8B vs Laguna S 2.1
- Granite 4.2 8B vs OTel-2.0-LLM-31B-IT
- Hy-Memory-30B-A3B vs Laguna S 2.1
- Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT
- Hy3 vs Laguna S 2.1
- Hy3 vs OTel-2.0-LLM-31B-IT
- Inkling-Small vs Laguna S 2.1
- Inkling-Small vs OTel-2.0-LLM-31B-IT
- Inkling vs Laguna S 2.1
- Inkling vs OTel-2.0-LLM-31B-IT
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna S 2.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT
- Kimi K3.1 Pro vs Laguna S 2.1
- Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT
- Kimi K3 vs Laguna S 2.1
- Kimi K3 vs OTel-2.0-LLM-31B-IT
- Kolibri-1 vs Laguna S 2.1
- Kolibri-1 vs OTel-2.0-LLM-31B-IT
- Kronos vs Laguna S 2.1
- Kronos vs OTel-2.0-LLM-31B-IT
- Laguna M.1 vs Laguna S 2.1
- Laguna M.1 vs OTel-2.0-LLM-31B-IT
- Laguna S 2.1 vs Laguna XS 2.1
- Laguna S 2.1 vs LBS-27B
- Laguna S 2.1 vs LFM2.5-2.6B
- Laguna S 2.1 vs LFM3-1.7B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs LFM3-Omni-2B
- Laguna S 2.1 vs LFM3-VL-2B
- Laguna S 2.1 vs Ling-3.0-tiny
- Laguna S 2.1 vs Ling-3.1-flash-1019
- Laguna S 2.1 vs LongCat-2.5
- Laguna S 2.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna S 2.1 vs MiMo-V2.6-Flash-RL
- Laguna S 2.1 vs MiniCPM-o 4.5
- Laguna S 2.1 vs MiniCPM-V 4.7 1B
- Laguna S 2.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna S 2.1 vs MiniCPM5-2B
- Laguna S 2.1 vs MiniMax-M2.5
- Laguna S 2.1 vs MiniMax-M2.7
- Laguna S 2.1 vs MiniMax-M3.1-Flash
- Laguna S 2.1 vs Mistral Large 4
- Laguna S 2.1 vs Muse Glimmer 30B
- Laguna S 2.1 vs Nanbeige4.2-3B
- Laguna S 2.1 vs Nanbeige4.3-33B
- Laguna S 2.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Laguna S 2.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 4B
- Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Laguna S 2.1 vs Nemotron 3 Super 120B A12B
- Laguna S 2.1 vs Nemotron 3 Ultra 550B A55B
- Laguna S 2.1 vs North Mini Code 1.0
- Laguna S 2.1 vs occamy-1.0
- Laguna S 2.1 vs Olmo 3 7B Think
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs Ornith-1.5-35B-A3B
- Laguna S 2.1 vs Ornith-1.5-397B
- Laguna S 2.1 vs Ornith-1.5-9B
- Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B
- Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna S 2.1 vs Qwen3-4B-Instruct-2507
- Laguna S 2.1 vs Qwen3.5-0.8B
- Laguna S 2.1 vs Qwen3.5-122B-A10B
- Laguna S 2.1 vs Qwen3.5-2B
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-9B
- Laguna S 2.1 vs Qwen3.6-27B
- Laguna S 2.1 vs Qwen3.6-35B-A3B
- Laguna S 2.1 vs Qwen3.8-27B
- Laguna S 2.1 vs Qwen3.8-Flash-Next
- Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-Coder-Next
- Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-2B-Instruct
- Laguna S 2.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-32B-Instruct
- Laguna S 2.1 vs Qwen3-VL-4B-Instruct
- Laguna S 2.1 vs Qwen3-VL-8B-Instruct
- Laguna S 2.1 vs Qwopus3.8-27B-Flash-V2
- Laguna S 2.1 vs Recursive Flint
- Laguna S 2.1 vs Ripple-65B Preview
- Laguna S 2.1 vs SenseNova-Loop-9B-Preview
- Laguna S 2.1 vs SmolLM3-3B
- Laguna S 2.1 vs Spark-X2.6-4B
- Laguna S 2.1 vs Step 3.7 Flash
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna S 2.1 vs Baidu Unlimited-OCR-2
- Laguna S 2.1 vs WeVisOne-VL-2B
- Laguna S 2.1 vs WeVisOne-VL-4B
- Laguna S 2.1 vs ZAYA1-8B
- Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
- 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
Machine-readable
- This page as Markdown
/compare/laguna-s-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-s-2-1.json·/api/v1/tools/otel-2-0-llm-31b-it.json - From a terminal
anchor compare laguna-s-2-1 otel-2-0-llm-31b-it(the CLI) - Over MCP
compare_tools {"a": "laguna-s-2-1", "b": "otel-2-0-llm-31b-it"}at/mcp, no key