# Ornith-1.5-122B vs Qwen3-VL-32B-Instruct > Ornith-1.5-122B scores 61 (C) to Qwen3-VL-32B-Instruct's 56.4 (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-vl-32b-instruct - Markdown: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.md (~10,050 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 Ornith-1.5-122B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 2 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach and risk. 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-VL-32B-Instruct: grade C, 56.4/100, rank #115 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-32b-instruct.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 60 - 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-VL-32B-Instruct (C) Good for: An agent with a 32 GB GPU or more that wants the largest dense Qwen3-VL model for images, documents and video Ahead on: - Reach, 85 against 75 - Risk, 55 against 50 Watch for: No safety evaluation, agent-use guidance or security contact ## Why the edge ### Agent interface: Ornith-1.5-122B +25 - 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-VL-32B-Instruct (60): A Jinja chat template in the repository (20). The tool-call format lives only in the template, Hermes-style JSON in tool_call tags, and Alibaba names no parser (10). Trained for function calling with several calls allowed in a turn, per the family's technical report (20). No documented structured output (0). No thinking mode in this edition, so reasoning control scores 0 by the rule. 262,144 tokens of native context (10). 20+10+20+0+0+10 = 60. ### Reach: Qwen3-VL-32B-Instruct +10 - 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-VL-32B-Instruct (85): Alibaba's own Q4_K_M GGUF is 19.76 GB and 20.53 GB with the vision file it needs for images, so with a fifth added it needs about 25 GB and the 32 GB band (20). Transformers 4.57.0, vLLM, SGLang, llama.cpp and Ollama all run it in released versions, which reaches the 25 cap. Alibaba publishes FP8 and GGUF builds at F16, Q8_0 and Q4_K_M (15). Apache 2.0 with nothing added (15). No gate and no account (10). On 4 November the Hub's provider mapping lists only Featherless, live, and OpenRouter listed it with no endpoints per the 21 October check, so it isn't hosted too (0). 20+25+15+15+10+0 = 85. ### Risk: Qwen3-VL-32B-Instruct +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-VL-32B-Instruct (55): Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25). Files in the Qwen organisation with a checksum on every file (15). No safety evaluation at all (0). No guidance for agent use, which matters for a model sold as a GUI agent (0). Apache 2.0 carries none of the licence risks (15). No security contact, disclosure policy or public advisories found for Qwen (0), and Alibaba's group-wide security centre wasn't checked. 25+15+0+0+15+0 = 55. ### 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-VL-32B-Instruct (31): The card misses limitations, languages and a cut-off (11). Training data is described only in the technical report, with no cut-off (5). The last change to code that runs it was the qwen-vl-utils fix of 4 January 2026, and the weights haven't changed since November 2025; the Hub commits for all three repositories and the QwenLM/Qwen3-VL head (30 January 2026) were the same on 4 November (5). Jie Huang, a Qwen organisation member, a named author of the Qwen3-VL Technical Report and the author of nine commits in QwenLM/Qwen3-VL, answered two of the 11 Hugging Face discussions that aren't pull requests, both on 27 October 2025; no publisher reply since, and GitHub issues weren't counted (5). The FP8 build's files were replaced in place the day after release with no note, after it had shipped the Thinking edition's files for three days (0). The GGUF card's example names a file the repository doesn't hold (5). 11+5+5+5+0+5 = 31. ## Score by category | Category | Weight | Ornith-1.5-122B | Qwen3-VL-32B-Instruct | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 85 | Qwen3-VL-32B-Instruct +10 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-32B-Instruct +5 | | Agent interface | 10% (10 this run) | 85 | 60 | Ornith-1.5-122B +25 | | Documentation and upkeep | 10% (10 this run) | 36 | 31 | Ornith-1.5-122B +5 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **56.4 · C** | | ## Facts side by side | Fact | Ornith-1.5-122B | Qwen3-VL-32B-Instruct | | --- | --- | --- | | 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 | 2025-11-01 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.5/5 (4) | ## 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-VL-32B-Instruct.** Qwen3-VL-32B-Instruct is the largest dense Qwen3-VL model, for screenshots, documents, charts and video. Alibaba's Q4_K_M GGUF with its vision file needs about 25 GB, and Transformers, vLLM, SGLang, llama.cpp and Ollama run it under plain Apache 2.0. It has no thinking mode or published safety work, and only one provider was found serving 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-VL-32B-Instruct 1. Start it with `llama-server -hf Qwen/Qwen3-VL-32B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:32b-instruct` 2. In Ollama the plain `qwen3-vl:32b` tag is the Thinking edition, so name `32b-instruct` 3. With the GGUF, load the mmproj vision file too, or images won't be read 4. Parse tool calls as JSON inside `` tags, or use SGLang's `--tool-call-parser qwen` 5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text 6. Validate JSON output yourself, since structured output isn't documented 7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later 8. If you pulled the FP8 build before 22 October 2025, pull it again, since the earlier files were the Thinking edition's ## Questions ### Which is better for AI agents, Ornith-1.5-122B or Qwen3-VL-32B-Instruct? Ornith-1.5-122B scores 61 (C) on agent readiness against Qwen3-VL-32B-Instruct's 56.4 (C), and leads in 2 of 4 scored categories. Qwen3-VL-32B-Instruct leads on reach and risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ornith-1-5-122b", "b": "qwen3-vl-32b-instruct"}`. 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[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) - [Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.md) - [Ornith-1.5-397B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.md) - [Ornith-1.5-9B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.md) - [OTel-2.0-LLM-31B-IT vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-vl-32b-instruct.md) - [Qwen-AgentWorld-35B-A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-vl-32b-instruct.md) - [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-vl-32b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-0.8B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-122B-A10B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-2B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-2b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-397B-A17B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-4B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-4b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.5-9B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.6-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.8-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-qwen3-vl-32b-instruct.md) - [Qwen3.8-Flash-Next vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-qwen3-vl-32b-instruct.md) - [Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.md) - [Qwen3-Coder-Next vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-coder-next-vs-qwen3-vl-32b-instruct.md) - [Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.md) - [Qwen3-VL-2B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-qwen3-vl-32b-instruct.md) - [Qwen3-VL-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-qwen3-vl-32b-instruct.md) - [Qwen3-VL-32B-Instruct vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-4b-instruct.md) - [Qwen3-VL-32B-Instruct vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwen3-vl-8b-instruct.md) - [Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-qwopus3-8-27b-flash-v2.md) - [Qwen3-VL-32B-Instruct vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.md) - [Qwen3-VL-32B-Instruct vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-ripple-65b-preview.md) - [Qwen3-VL-32B-Instruct vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-sensenova-loop-9b-preview.md) - [Qwen3-VL-32B-Instruct vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-smollm3-3b.md) - [Qwen3-VL-32B-Instruct vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.md) - [Qwen3-VL-32B-Instruct vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-step-3-7-flash.md) - [Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-27b.md) - [Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next.md) - [Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.md) - [Qwen3-VL-32B-Instruct vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-2b.md) - [Qwen3-VL-32B-Instruct vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-wevisone-vl-4b.md) - [Qwen3-VL-32B-Instruct vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-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