Head to head · Open-weight models you run yourself · October 2026 research run
OTel-2.0-LLM-31B-IT vs Qwen3.5-0.8B
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 1 of 4 scored categories. Qwen3.5-0.8B leads on reach. 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
- Documentation and upkeep, 74 against 37
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Best for Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU
Ahead on
- Reach, 80 against 50
Watch for
No quantised build from Alibaba and no inference provider found
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 +37
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.
Qwen3.5-0.8B 37
The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 248 days before this check, within a year (5). No Qwen member has answered any of the 15 discussions that aren't pull requests (0). Qwen3-0.6B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37.
Reach Qwen3.5-0.8B +30
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.
Qwen3.5-0.8B 80
Memory earns the full 30, since LM Studio's Q4_K_M build with its projector is 0.73 GB (0.88 GB with the fifth added) and Ollama's Q8_0 build was 1.0 GB, both far within 16 GB. Alibaba publishes no quantised 0.8B, so builds scores 0. Runtimes reach the 25 cap on llama.cpp, Ollama (through its pinned llama.cpp), vLLM, SGLang, LM Studio and Transformers. Apache 2.0 with nothing added gives terms 15 and the ungated repository download 10. Hosted too scores 0, as the Hub maps no inference provider to this size. 30+25+0+15+10+0 = 80.
Score by category
| Category | Weight this run | OTel-2.0-LLM-31B-IT | Qwen3.5-0.8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 80 | Qwen3.5-0.8B +30 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 74 | 37 | OTel-2.0-LLM-31B-IT +37 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 56.8/100 | D 46.7/100 |
Facts side by side
| Fact | OTel-2.0-LLM-31B-IT | Qwen3.5-0.8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | AT&T (Open Telco AI) | Alibaba (Qwen team) |
| 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 | 2026-03-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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.
Qwen3.5-0.8B
A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.
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
Qwen3.5-0.8B
- Smallest start is
ollama run qwen3.5:0.8b, orqwen3.5:0.8b-q8_0at 1.0 GB - Full precision
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Thinking is off by default; if you turn it on with
"chat_template_kwargs": {"enable_thinking": true}, stream and cut off runaway output, since the card warns of thinking loops, and add--reasoning-parser qwen3in vLLM or SGLang to split the reasoning out - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0for text without thinking - Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, OTel-2.0-LLM-31B-IT or Qwen3.5-0.8B?
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 1 of 4 scored categories. Qwen3.5-0.8B leads on reach.
Other comparisons with OTel-2.0-LLM-31B-IT or Qwen3.5-0.8B
- Agents-A1-4B vs OTel-2.0-LLM-31B-IT
- Agents-A1-4B vs Qwen3.5-0.8B
- Apertus v1.5 8B vs OTel-2.0-LLM-31B-IT
- Apertus v1.5 8B vs Qwen3.5-0.8B
- Apodex-1.1-mini vs OTel-2.0-LLM-31B-IT
- Apodex-1.1-mini vs Qwen3.5-0.8B
- Arete-27B vs OTel-2.0-LLM-31B-IT
- Arete-27B vs Qwen3.5-0.8B
- BigBang-v1 vs OTel-2.0-LLM-31B-IT
- BigBang-v1 vs Qwen3.5-0.8B
- Carbon-X vs OTel-2.0-LLM-31B-IT
- Carbon-X vs Qwen3.5-0.8B
- Cola2 vs OTel-2.0-LLM-31B-IT
- Cola2 vs Qwen3.5-0.8B
- Cosmos-Reason2-2B vs OTel-2.0-LLM-31B-IT
- Cosmos-Reason2-2B vs Qwen3.5-0.8B
- Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT
- Darwin-36B-Opus vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs OTel-2.0-LLM-31B-IT
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek V4.1 Flash vs OTel-2.0-LLM-31B-IT
- DeepSeek V4.1 Flash vs Qwen3.5-0.8B
- DeepSeek-V4-Flash-0731 vs OTel-2.0-LLM-31B-IT
- DeepSeek-V4-Flash-0731 vs Qwen3.5-0.8B
- Devstral Small 2 vs OTel-2.0-LLM-31B-IT
- Devstral Small 2 vs Qwen3.5-0.8B
- DiffusionGemma 26B A4B IT vs OTel-2.0-LLM-31B-IT
- DiffusionGemma 26B A4B IT vs Qwen3.5-0.8B
- EXAONE 4.5 33B vs OTel-2.0-LLM-31B-IT
- EXAONE 4.5 33B vs Qwen3.5-0.8B
- Gemma 4 12B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 12B IT vs Qwen3.5-0.8B
- Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 26B A4B IT vs Qwen3.5-0.8B
- Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 31B IT vs Qwen3.5-0.8B
- Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E2B IT vs Qwen3.5-0.8B
- Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E4B IT vs Qwen3.5-0.8B
- GLM-4.5-Air vs OTel-2.0-LLM-31B-IT
- GLM-4.5-Air vs Qwen3.5-0.8B
- GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT
- GLM-4.7-Flash vs Qwen3.5-0.8B
- GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3-Flash vs Qwen3.5-0.8B
- GLM-5.3 vs OTel-2.0-LLM-31B-IT
- GLM-5.3 vs Qwen3.5-0.8B
- gpt-oss-120b vs OTel-2.0-LLM-31B-IT
- gpt-oss-120b vs Qwen3.5-0.8B
- gpt-oss-20b vs OTel-2.0-LLM-31B-IT
- gpt-oss-20b vs Qwen3.5-0.8B
- Granite 4.2 30B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 30B vs Qwen3.5-0.8B
- Granite 4.2 3B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 3B vs Qwen3.5-0.8B
- Granite 4.2 8B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 8B vs Qwen3.5-0.8B
- Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT
- Hy-Memory-30B-A3B vs Qwen3.5-0.8B
- Hy3 vs OTel-2.0-LLM-31B-IT
- Hy3 vs Qwen3.5-0.8B
- Inkling-Small vs OTel-2.0-LLM-31B-IT
- Inkling-Small vs Qwen3.5-0.8B
- Inkling vs OTel-2.0-LLM-31B-IT
- Inkling vs Qwen3.5-0.8B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Qwen3.5-0.8B
- Kimi K3.1 Pro vs OTel-2.0-LLM-31B-IT
- Kimi K3.1 Pro vs Qwen3.5-0.8B
- Kimi K3 vs OTel-2.0-LLM-31B-IT
- Kimi K3 vs Qwen3.5-0.8B
- Kolibri-1 vs OTel-2.0-LLM-31B-IT
- Kolibri-1 vs Qwen3.5-0.8B
- Kronos vs OTel-2.0-LLM-31B-IT
- Kronos vs Qwen3.5-0.8B
- Laguna M.1 vs OTel-2.0-LLM-31B-IT
- Laguna M.1 vs Qwen3.5-0.8B
- Laguna S 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna S 2.1 vs Qwen3.5-0.8B
- Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna XS 2.1 vs Qwen3.5-0.8B
- LBS-27B vs OTel-2.0-LLM-31B-IT
- LBS-27B vs Qwen3.5-0.8B
- LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT
- LFM2.5-2.6B vs Qwen3.5-0.8B
- LFM3-1.7B vs OTel-2.0-LLM-31B-IT
- LFM3-1.7B vs Qwen3.5-0.8B
- LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT
- LFM3-Audio-2B vs Qwen3.5-0.8B
- LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT
- LFM3-Omni-2B vs Qwen3.5-0.8B
- LFM3-VL-2B vs OTel-2.0-LLM-31B-IT
- LFM3-VL-2B vs Qwen3.5-0.8B
- Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT
- Ling-3.0-tiny vs Qwen3.5-0.8B
- Ling-3.1-flash-1019 vs OTel-2.0-LLM-31B-IT
- Ling-3.1-flash-1019 vs Qwen3.5-0.8B
- LongCat-2.5 vs OTel-2.0-LLM-31B-IT
- LongCat-2.5 vs Qwen3.5-0.8B
- MiMo-V2.6 Distill Qwen 9B vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6 Distill Qwen 9B vs Qwen3.5-0.8B
- MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT
- MiMo-V2.6-Flash-RL vs Qwen3.5-0.8B
- MiniCPM-o 4.5 vs OTel-2.0-LLM-31B-IT
- MiniCPM-o 4.5 vs Qwen3.5-0.8B
- MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 1B vs Qwen3.5-0.8B
- MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 35B-A3B vs Qwen3.5-0.8B
- MiniCPM5-2B vs OTel-2.0-LLM-31B-IT
- MiniCPM5-2B vs Qwen3.5-0.8B
- MiniMax-M2.5 vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.5 vs Qwen3.5-0.8B
- MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.7 vs Qwen3.5-0.8B
- MiniMax-M3.1-Flash vs OTel-2.0-LLM-31B-IT
- MiniMax-M3.1-Flash vs Qwen3.5-0.8B
- Mistral Large 4 vs OTel-2.0-LLM-31B-IT
- Mistral Large 4 vs Qwen3.5-0.8B
- Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT
- Muse Glimmer 30B vs Qwen3.5-0.8B
- Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.2-3B vs Qwen3.5-0.8B
- Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.3-33B vs Qwen3.5-0.8B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Qwen3.5-0.8B
- Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3.5 Lightning 30B A3B vs Qwen3.5-0.8B
- Nemotron 3 Nano 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 30B A3B vs Qwen3.5-0.8B
- Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 4B vs Qwen3.5-0.8B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.5-0.8B
- Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Super 120B A12B vs Qwen3.5-0.8B
- Nemotron 3 Ultra 550B A55B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Ultra 550B A55B vs Qwen3.5-0.8B
- North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT
- North Mini Code 1.0 vs Qwen3.5-0.8B
- occamy-1.0 vs OTel-2.0-LLM-31B-IT
- occamy-1.0 vs Qwen3.5-0.8B
- Olmo 3 7B Think vs OTel-2.0-LLM-31B-IT
- Olmo 3 7B Think vs Qwen3.5-0.8B
- Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-122B vs Qwen3.5-0.8B
- Ornith-1.5-35B-A3B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-35B-A3B vs Qwen3.5-0.8B
- Ornith-1.5-397B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-397B vs Qwen3.5-0.8B
- Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-9B vs Qwen3.5-0.8B
- 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 Qwen3.5-122B-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
- Qwen-AgentWorld-35B-A3B vs Qwen3.5-0.8B
- Qwen3-30B-A3B-Instruct-2507 vs Qwen3.5-0.8B
- Qwen3-4B-Instruct-2507 vs Qwen3.5-0.8B
- Qwen3.5-0.8B vs Qwen3.5-122B-A10B
- Qwen3.5-0.8B vs Qwen3.5-2B
- Qwen3.5-0.8B vs Qwen3.5-397B-A17B
- Qwen3.5-0.8B vs Qwen3.5-4B
- Qwen3.5-0.8B vs Qwen3.5-9B
- Qwen3.5-0.8B vs Qwen3.6-27B
- Qwen3.5-0.8B vs Qwen3.6-35B-A3B
- Qwen3.5-0.8B vs Qwen3.8-27B
- Qwen3.5-0.8B vs Qwen3.8-Flash-Next
- Qwen3.5-0.8B vs Qwen3-Coder-30B-A3B-Instruct
- Qwen3.5-0.8B vs Qwen3-Coder-Next
- Qwen3.5-0.8B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-2B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-32B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-4B-Instruct
- Qwen3.5-0.8B vs Qwen3-VL-8B-Instruct
- Qwen3.5-0.8B vs Qwopus3.8-27B-Flash-V2
- Qwen3.5-0.8B vs Recursive Flint
- Qwen3.5-0.8B vs Ripple-65B Preview
- Qwen3.5-0.8B vs SenseNova-Loop-9B-Preview
- Qwen3.5-0.8B vs SmolLM3-3B
- Qwen3.5-0.8B vs Spark-X2.6-4B
- Qwen3.5-0.8B vs Step 3.7 Flash
- Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.5-0.8B vs Baidu Unlimited-OCR-2
- Qwen3.5-0.8B vs WeVisOne-VL-2B
- Qwen3.5-0.8B vs WeVisOne-VL-4B
- Qwen3.5-0.8B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/otel-2-0-llm-31b-it-vs-qwen3-5-0-8b.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/qwen3-5-0-8b.json - From a terminal
anchor compare otel-2-0-llm-31b-it qwen3-5-0-8b(the CLI) - Over MCP
compare_tools {"a": "otel-2-0-llm-31b-it", "b": "qwen3-5-0-8b"}at/mcp, no key