Head to head · Open-weight models you run yourself · October 2026 research run
Darwin-36B-Opus vs OTel-2.0-LLM-31B-IT
Darwin-36B-Opus 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 Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required
Ahead on
- Reach, 80 against 50
- Agent interface, 70 against 60
Watch for
The merge method is proprietary and the current weights include an undescribed targeted refinement
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 61
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 Darwin-36B-Opus +30
Darwin-36B-Opus 80
Memory 20, the publisher's Q4_K_M GGUF is 21.2 GB and needs about 25.4 GB with the fifth added, so 32 GB. Runtimes 20, the text-only Qwen3.5 MoE class runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, FINAL-Bench's POCKET-35B-GGUF has it at four precisions. Terms 15, Apache 2.0 with nothing added, with a LICENSE file since 1 November 2026, and Apache 2.0 on the base Qwen3.6-35B-A3B too. Download 10, nothing is gated. Hosted 0, no provider or API serves it.
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 +13
Darwin-36B-Opus 61
Model card 11, it covers architecture, parameters, context and languages but not intended use, limitations or a knowledge cut-off. Training data 5, the reasoning parent's fine-tune is described without datasets or a cut-off. Last change 20, on 1 November 2026 the publisher added a LICENSE file and a corrected eval_results note to the main repository and edited the POCKET-35B-GGUF card, read as this run reads card edits on other open-weight listings (Muse Glimmer 30B, MiMo-V2.6 Distill Qwen 9B, Apertus v1.5 8B). The weights date from 23 April and the GGUF build from 22 July. Responsiveness 20, the publisher answered 8 of the 13 discussions, and two of the rest are its own posts. Version policy 5, a first release whose weights were replaced twice in its first day with commit titles but no card note. File health 0, the card's head counts and base still disagree with the files, and the generation config drops the base's end-of-turn stop token. 11 + 5 + 20 + 20 + 5 + 0 = 61.
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 Darwin-36B-Opus +10
Darwin-36B-Opus 70
Chat template 20, Qwen3.6's Jinja template in the repository. Tool-calling format 10, the format is only in the template and the card says nothing about tools. Trained tool calling 20, inherited from the base that was trained for it, with several calls in one turn in the template, though the publisher neither claims nor tests it. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card shows where the reasoning comes back. Context 10, 262,144 tokens.
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 | Darwin-36B-Opus | OTel-2.0-LLM-31B-IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 50 | Darwin-36B-Opus +30 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 60 | Darwin-36B-Opus +10 |
| Documentation and upkeep | 10%10 | 61 | 74 | OTel-2.0-LLM-31B-IT +13 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | Darwin-36B-Opus | OTel-2.0-LLM-31B-IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | FINAL-Bench (VIDRAFT) | AT&T (Open Telco AI) |
| 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 | 2026-04-23 | 2026-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
OTel-2.0-LLM-31B-IT
No score change since it was listed.
Verdicts
Darwin-36B-Opus
A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results.
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
Darwin-36B-Opus
- Quickest start is
llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja, about 21 GB of files - On the reference machine BF16 fits,
vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, the parsers for Qwen3.6's format - Thinking is on by default, send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces
- generation_config.json stops only on the end-of-text token, so add
<|im_end|>as a stop token if a runtime keeps generating after the turn ends - Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files
- Validate JSON output yourself, 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, Darwin-36B-Opus or OTel-2.0-LLM-31B-IT?
Darwin-36B-Opus 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 Darwin-36B-Opus or OTel-2.0-LLM-31B-IT
- Agents-A1-4B vs Darwin-36B-Opus
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- BigBang-v1 vs Darwin-36B-Opus
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- Carbon-X vs Darwin-36B-Opus
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- Cola2 vs Darwin-36B-Opus
- Cola2 vs OTel-2.0-LLM-31B-IT
- Cosmos-Reason2-2B vs Darwin-36B-Opus
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- Darwin-36B-Opus vs Devstral Small 2
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- Darwin-36B-Opus vs EXAONE 4.5 33B
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- Darwin-36B-Opus vs Gemma 4 31B IT
- Darwin-36B-Opus vs Gemma 4 E2B IT
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- Darwin-36B-Opus vs Inkling-Small
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- Darwin-36B-Opus vs Kimi K3
- Darwin-36B-Opus vs Kimi K3.1 Pro
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- Darwin-36B-Opus vs Laguna S 2.1
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- Darwin-36B-Opus vs LFM3-1.7B
- Darwin-36B-Opus vs LFM3-Audio-2B
- Darwin-36B-Opus vs LFM3-Omni-2B
- Darwin-36B-Opus vs LFM3-VL-2B
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- Darwin-36B-Opus vs Ling-3.1-flash-1019
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- Darwin-36B-Opus vs MiMo-V2.6 Distill Qwen 9B
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- Darwin-36B-Opus vs Olmo 3 7B Think
- Darwin-36B-Opus vs Ornith-1.5-122B
- Darwin-36B-Opus vs Ornith-1.5-35B-A3B
- Darwin-36B-Opus vs Ornith-1.5-397B
- Darwin-36B-Opus vs Ornith-1.5-9B
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- Darwin-36B-Opus vs Qwen3-30B-A3B-Instruct-2507
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- Darwin-36B-Opus vs Qwen3.5-0.8B
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- Darwin-36B-Opus vs Qwen3.5-4B
- Darwin-36B-Opus vs Qwen3.5-9B
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- Darwin-36B-Opus vs Qwen3.8-27B
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- Darwin-36B-Opus vs Qwen3-Coder-30B-A3B-Instruct
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- Darwin-36B-Opus vs Qwen3-VL-2B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-30B-A3B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-32B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-4B-Instruct
- Darwin-36B-Opus vs Qwen3-VL-8B-Instruct
- Darwin-36B-Opus vs Qwopus3.8-27B-Flash-V2
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- Darwin-36B-Opus vs Ripple-65B Preview
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- Darwin-36B-Opus vs ThinkingCap (Qwen3.8 27B)
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- Darwin-36B-Opus vs WeVisOne-VL-4B
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- Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT
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- GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3 vs OTel-2.0-LLM-31B-IT
- gpt-oss-120b vs OTel-2.0-LLM-31B-IT
- gpt-oss-20b vs OTel-2.0-LLM-31B-IT
- Granite 4.2 30B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 3B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 8B vs OTel-2.0-LLM-31B-IT
- Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT
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- Inkling vs OTel-2.0-LLM-31B-IT
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs OTel-2.0-LLM-31B-IT
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- Laguna S 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
- LBS-27B vs OTel-2.0-LLM-31B-IT
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- 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/darwin-36b-opus-vs-otel-2-0-llm-31b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/darwin-36b-opus.json·/api/v1/tools/otel-2-0-llm-31b-it.json - From a terminal
anchor compare darwin-36b-opus otel-2-0-llm-31b-it(the CLI) - Over MCP
compare_tools {"a": "darwin-36b-opus", "b": "otel-2-0-llm-31b-it"}at/mcp, no key