# Ornith-1.5-122B vs Qwen3.6-27B > Qwen3.6-27B scores 66.7 (B) to Ornith-1.5-122B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b - Markdown: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.md (~9,500 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 .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. 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 Qwen3.6-27B scores 66.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself. - 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 - Qwen3.6-27B: grade B, 66.7/100, rank #16 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.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 ### 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 - Documentation and upkeep, 36 against 31 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 ### Qwen3.6-27B (B) Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms. Ahead on: - Reach, 90 against 75 - Risk, 55 against 50 Watch for: No safety results, prompt-injection guidance or security contact ## Why the edge ### Reach: Qwen3.6-27B +15 - 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. - Qwen3.6-27B (90): Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card. ### Agent interface: Ornith-1.5-122B +15 - 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. - Qwen3.6-27B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native. ### Risk: Qwen3.6-27B +5 - 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. - Qwen3.6-27B (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 Qwen organisation that Qwen's GitHub links to, and every file has a hash on the Hub. Safety evaluation 0, none published on the card or README. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, SECURITY.md or reporting route was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. ### Documentation and upkeep: Ornith-1.5-122B +5 - 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. - Qwen3.6-27B (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply. ## Score by category | Category | Weight | Ornith-1.5-122B | Qwen3.6-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 90 | Qwen3.6-27B +15 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3.6-27B +5 | | Agent interface | 10% (10 this run) | 85 | 70 | Ornith-1.5-122B +15 | | Documentation and upkeep | 10% (10 this run) | 36 | 31 | Ornith-1.5-122B +5 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **66.7 · B** | | ## Facts side by side | Fact | Ornith-1.5-122B | Qwen3.6-27B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Ornith | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-24 | 2026-04-22 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 3/5 (4) | ## How the scores have moved ### Ornith-1.5-122B No score change since it was listed. ### Qwen3.6-27B - -0.5 on 1 November 2026: time ## Verdicts **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. **Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it. ## Before you call either ### 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 ### Qwen3.6-27B 1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector 2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later 3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call 4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported 5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt 6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without 7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens 8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented 9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes ## Questions ### Which is better for AI agents, Ornith-1.5-122B or Qwen3.6-27B? Qwen3.6-27B scores 66.7 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ornith-1-5-122b", "b": "qwen3-6-27b"}`. 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[Qwen3.6-27B vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.md) - [Qwen3.6-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-ripple-65b-preview.md) - [Qwen3.6-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-sensenova-loop-9b-preview.md) - [Qwen3.6-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-smollm3-3b.md) - [Qwen3.6-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.md) - [Qwen3.6-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.md) - [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md) - [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md) - [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3.6-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.md) - [Qwen3.6-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.md) - [Qwen3.6-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.md) - [Qwen3.6-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp