Head to head · Open-weight models you run yourself · October 2026 research run
occamy-1.0 vs OTel-2.0-LLM-31B-IT
occamy-1.0 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. 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 an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.
Ahead on
- Reach, 90 against 50
- Agent interface, 70 against 60
Watch for
No safety results, prompt-injection guidance or security contact
Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware
No category where it leads by five points or more, and no fact that sets it apart.
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 occamy-1.0 +40
occamy-1.0 90
Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.
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 occamy-1.0 +10
occamy-1.0 70
Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. 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 | occamy-1.0 | OTel-2.0-LLM-31B-IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 50 | occamy-1.0 +40 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 70 | 60 | occamy-1.0 +10 |
| Documentation and upkeep | 10%10 | 74 | 74 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.8/100 |
Facts side by side
| Fact | occamy-1.0 | OTel-2.0-LLM-31B-IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Accio (Accio-Lab) | 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 | 2026-10-02 | 2026-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3/5 (4) | none |
How the scores have moved
OTel-2.0-LLM-31B-IT
No score change since it was listed.
Verdicts
occamy-1.0
A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.
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
occamy-1.0
- Serve with
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coderon vLLM 0.19.0 or SGLang 0.5.10 and later - BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
- Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
- With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
- The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head
- Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture
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, occamy-1.0 or OTel-2.0-LLM-31B-IT?
occamy-1.0 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.
Other comparisons with occamy-1.0 or OTel-2.0-LLM-31B-IT
- Agents-A1-4B vs occamy-1.0
- Agents-A1-4B vs OTel-2.0-LLM-31B-IT
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- Apodex-1.1-mini vs occamy-1.0
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- Arete-27B vs occamy-1.0
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- Carbon-X vs occamy-1.0
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- Cola2 vs occamy-1.0
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- Darwin-36B-Opus vs occamy-1.0
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- Devstral Small 2 vs occamy-1.0
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- DiffusionGemma 26B A4B IT vs occamy-1.0
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- EXAONE 4.5 33B vs occamy-1.0
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- Gemma 4 26B A4B IT vs occamy-1.0
- Gemma 4 26B A4B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 31B IT vs occamy-1.0
- Gemma 4 31B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E2B IT vs occamy-1.0
- Gemma 4 E2B IT vs OTel-2.0-LLM-31B-IT
- Gemma 4 E4B IT vs occamy-1.0
- Gemma 4 E4B IT vs OTel-2.0-LLM-31B-IT
- GLM-4.5-Air vs occamy-1.0
- GLM-4.5-Air vs OTel-2.0-LLM-31B-IT
- GLM-4.7-Flash vs occamy-1.0
- GLM-4.7-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3-Flash vs occamy-1.0
- GLM-5.3-Flash vs OTel-2.0-LLM-31B-IT
- GLM-5.3 vs occamy-1.0
- GLM-5.3 vs OTel-2.0-LLM-31B-IT
- gpt-oss-120b vs occamy-1.0
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- gpt-oss-20b vs occamy-1.0
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- Granite 4.2 30B vs occamy-1.0
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- Granite 4.2 3B vs occamy-1.0
- Granite 4.2 3B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 8B vs occamy-1.0
- Granite 4.2 8B vs OTel-2.0-LLM-31B-IT
- Hy-Memory-30B-A3B vs occamy-1.0
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- Hy3 vs occamy-1.0
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- Inkling-Small vs occamy-1.0
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- Kimi K3.1 Pro vs occamy-1.0
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- Kimi K3 vs occamy-1.0
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- Kolibri-1 vs occamy-1.0
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- Kronos vs occamy-1.0
- Kronos vs OTel-2.0-LLM-31B-IT
- Laguna M.1 vs occamy-1.0
- Laguna M.1 vs OTel-2.0-LLM-31B-IT
- Laguna S 2.1 vs occamy-1.0
- Laguna S 2.1 vs OTel-2.0-LLM-31B-IT
- Laguna XS 2.1 vs occamy-1.0
- Laguna XS 2.1 vs OTel-2.0-LLM-31B-IT
- LBS-27B vs occamy-1.0
- LBS-27B vs OTel-2.0-LLM-31B-IT
- LFM2.5-2.6B vs occamy-1.0
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- LFM3-1.7B vs occamy-1.0
- LFM3-1.7B vs OTel-2.0-LLM-31B-IT
- LFM3-Audio-2B vs occamy-1.0
- LFM3-Audio-2B vs OTel-2.0-LLM-31B-IT
- LFM3-Omni-2B vs occamy-1.0
- LFM3-Omni-2B vs OTel-2.0-LLM-31B-IT
- LFM3-VL-2B vs occamy-1.0
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- Ling-3.0-tiny vs occamy-1.0
- Ling-3.0-tiny vs OTel-2.0-LLM-31B-IT
- Ling-3.1-flash-1019 vs occamy-1.0
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- LongCat-2.5 vs occamy-1.0
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- MiMo-V2.6 Distill Qwen 9B vs occamy-1.0
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- MiniCPM-o 4.5 vs occamy-1.0
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- MiniCPM-V 4.7 1B vs occamy-1.0
- MiniCPM-V 4.7 1B vs OTel-2.0-LLM-31B-IT
- MiniCPM-V 4.7 35B-A3B vs occamy-1.0
- MiniCPM-V 4.7 35B-A3B vs OTel-2.0-LLM-31B-IT
- MiniCPM5-2B vs occamy-1.0
- MiniCPM5-2B vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.5 vs occamy-1.0
- MiniMax-M2.5 vs OTel-2.0-LLM-31B-IT
- MiniMax-M2.7 vs occamy-1.0
- MiniMax-M2.7 vs OTel-2.0-LLM-31B-IT
- MiniMax-M3.1-Flash vs occamy-1.0
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- Mistral Large 4 vs occamy-1.0
- Mistral Large 4 vs OTel-2.0-LLM-31B-IT
- Muse Glimmer 30B vs occamy-1.0
- Muse Glimmer 30B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.2-3B vs occamy-1.0
- Nanbeige4.2-3B vs OTel-2.0-LLM-31B-IT
- Nanbeige4.3-33B vs occamy-1.0
- Nanbeige4.3-33B vs OTel-2.0-LLM-31B-IT
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs occamy-1.0
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs OTel-2.0-LLM-31B-IT
- Nemotron 3.5 Lightning 30B A3B vs occamy-1.0
- Nemotron 3.5 Lightning 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 30B A3B vs occamy-1.0
- Nemotron 3 Nano 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 4B vs occamy-1.0
- Nemotron 3 Nano 4B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano Omni 30B A3B Reasoning vs occamy-1.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Super 120B A12B vs occamy-1.0
- Nemotron 3 Super 120B A12B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Ultra 550B A55B vs occamy-1.0
- Nemotron 3 Ultra 550B A55B vs OTel-2.0-LLM-31B-IT
- North Mini Code 1.0 vs occamy-1.0
- North Mini Code 1.0 vs OTel-2.0-LLM-31B-IT
- occamy-1.0 vs Olmo 3 7B Think
- occamy-1.0 vs Ornith-1.5-122B
- occamy-1.0 vs Ornith-1.5-35B-A3B
- occamy-1.0 vs Ornith-1.5-397B
- occamy-1.0 vs Ornith-1.5-9B
- occamy-1.0 vs Qwen-AgentWorld-35B-A3B
- occamy-1.0 vs Qwen3-30B-A3B-Instruct-2507
- occamy-1.0 vs Qwen3-4B-Instruct-2507
- occamy-1.0 vs Qwen3.5-0.8B
- occamy-1.0 vs Qwen3.5-122B-A10B
- occamy-1.0 vs Qwen3.5-2B
- occamy-1.0 vs Qwen3.5-397B-A17B
- occamy-1.0 vs Qwen3.5-4B
- occamy-1.0 vs Qwen3.5-9B
- occamy-1.0 vs Qwen3.6-27B
- occamy-1.0 vs Qwen3.6-35B-A3B
- occamy-1.0 vs Qwen3.8-27B
- occamy-1.0 vs Qwen3.8-Flash-Next
- occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-Coder-Next
- occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-2B-Instruct
- occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct
- occamy-1.0 vs Qwen3-VL-32B-Instruct
- occamy-1.0 vs Qwen3-VL-4B-Instruct
- occamy-1.0 vs Qwen3-VL-8B-Instruct
- occamy-1.0 vs Qwopus3.8-27B-Flash-V2
- occamy-1.0 vs Recursive Flint
- occamy-1.0 vs Ripple-65B Preview
- occamy-1.0 vs SenseNova-Loop-9B-Preview
- occamy-1.0 vs SmolLM3-3B
- occamy-1.0 vs Spark-X2.6-4B
- occamy-1.0 vs Step 3.7 Flash
- occamy-1.0 vs Swift 1.5 Qwen3.8-27B
- occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next
- occamy-1.0 vs ThinkingCap (Qwen3.8 27B)
- occamy-1.0 vs Baidu Unlimited-OCR-2
- occamy-1.0 vs WeVisOne-VL-2B
- occamy-1.0 vs WeVisOne-VL-4B
- occamy-1.0 vs ZAYA1-8B
- 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/occamy-1-0-vs-otel-2-0-llm-31b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/occamy-1-0.json·/api/v1/tools/otel-2-0-llm-31b-it.json - From a terminal
anchor compare occamy-1-0 otel-2-0-llm-31b-it(the CLI) - Over MCP
compare_tools {"a": "occamy-1-0", "b": "otel-2-0-llm-31b-it"}at/mcp, no key