# occamy-1.0 vs Ornith-1.5-122B

> occamy-1.0 and Ornith-1.5-122B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b
- Markdown: https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.md (~9,200 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

occamy-1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-122B leads on agent interface. Both do open-weight models you run yourself.

- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json
- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### occamy-1.0 (C)

Good 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 75
- Risk, 55 against 50
- Documentation and upkeep, 74 against 36

Watch for: No safety results, prompt-injection guidance or security contact

### Ornith-1.5-122B (C)

Good for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required

Ahead on:
- Agent interface, 85 against 70

Watch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on


## Why the edge

### Documentation and upkeep: occamy-1.0 +38

- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.
- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.

### Reach: occamy-1.0 +15

- 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.
- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.

### Agent interface: Ornith-1.5-122B +15

- 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.
- Ornith-1.5-122B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.

### Risk: occamy-1.0 +5

- occamy-1.0 (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the organisation Accio's GitHub links, with MD5 sums and SHA-256 for every file. Safety evaluation 0, none published. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, bug reports go to public GitHub issues and there's no security contact.
- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.

## Score by category

| Category | Weight | occamy-1.0 | Ornith-1.5-122B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 90 | 75 | occamy-1.0 +15 |
| Risk | 20% (20 this run) | 55 | 50 | occamy-1.0 +5 |
| Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-122B +15 |
| Documentation and upkeep | 10% (10 this run) | 74 | 36 | occamy-1.0 +38 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | occamy-1.0 | Ornith-1.5-122B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Accio (Accio-Lab) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-02 | 2026-10-24 |
| 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

### occamy-1.0

- no change on 3 November 2026: correction

### Ornith-1.5-122B

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.

**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.

## Before you call either

### occamy-1.0

1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later
2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism
3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says
4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5
5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata
6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head
7. 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

### Ornith-1.5-122B

1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`
2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model
3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed
4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention
5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented

## Questions

### Which is better for AI agents, occamy-1.0 or Ornith-1.5-122B?

occamy-1.0 and Ornith-1.5-122B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-122B leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "occamy-1-0", "b": "ornith-1-5-122b"}`. From a terminal: `anchor compare occamy-1-0 ornith-1-5-122b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json

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- [Laguna XS 2.1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b.md)
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- [occamy-1.0 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-2b.md)
- [occamy-1.0 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-397b-a17b.md)
- [occamy-1.0 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-5-4b.md)
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- [occamy-1.0 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-6-35b-a3b.md)
- [occamy-1.0 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-8-27b.md)
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- [occamy-1.0 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.md)
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- [Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507.md)
- [Ornith-1.5-122B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.md)
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- [Ornith-1.5-122B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b.md)
- [Ornith-1.5-122B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-397b-a17b.md)
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- [Ornith-1.5-122B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.md)
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- [Ornith-1.5-122B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.md)
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- [Ornith-1.5-122B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.md)
- [Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.md)
- [Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.md)
- [Ornith-1.5-122B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.md)
- [Ornith-1.5-122B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.md)
- [Ornith-1.5-122B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.md)
- [Ornith-1.5-122B vs ZAYA1-8B](https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.md)

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