# Ornith-1.5-122B vs Qwen3-4B-Instruct-2507 > Ornith-1.5-122B scores 61 (C) to Qwen3-4B-Instruct-2507's 54.5 (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-qwen3-4b-instruct-2507 - Markdown: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.md (~10,150 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 Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 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 - Qwen3-4B-Instruct-2507: grade C, 54.5/100, rank #130 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-4b-instruct-2507.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 75 - Documentation and upkeep, 36 against 26 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 ### Qwen3-4B-Instruct-2507 (C) Good for: Agents that want a small instruct model on a laptop, phone-class device or spare GPU, with a plain licence and long context. 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: Qwen3-4B-Instruct-2507 +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. - Qwen3-4B-Instruct-2507 (90): Memory 30, LM Studio's and Ollama's 4-bit builds are 2.3 to 2.5 GB, about 3 GB with a fifth added, inside 16 GB. Runtimes 25, released Transformers, vLLM, SGLang and llama.cpp run the qwen3 architecture, Ollama's released build pins a llama.cpp that has it, and LM Studio's catalogue carries it. 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 0, on 4 November 2026 the Hub's provider mapping showed only Featherless AI with status error, Nscale was gone and Alibaba's Model Studio doesn't list it. 30 + 25 + 10 + 15 + 10 + 0 = 90. ### Agent interface: Ornith-1.5-122B +10 - 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. - Qwen3-4B-Instruct-2507 (75): Chat template 20, a Jinja template in tokenizer_config.json. Tool-calling format 25, Qwen's own Function Calling guide documents the Hermes-style declaration and how calls come back, and names vLLM's hermes parser. Trained tool calling 20, the template allows one or more calls a turn. Structured output 0, not documented for the model, and Qwen's docs only point to vLLM's generic guided decoding. Reasoning control 0, this edition has no thinking. Context 10, 262,144 tokens native. Template and config unchanged on 4 November. 20 + 25 + 20 + 0 + 0 + 10 = 75. ### Documentation and upkeep: Ornith-1.5-122B +10 - 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. - Qwen3-4B-Instruct-2507 (26): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, named languages and a knowledge cut-off aren't. Training data 5, the cited Qwen3 report describes the data in general terms without a cut-off. Last change 0, the files last changed on 17 September 2025 (Hub lastModified on 4 November), more than a year ago. Responsiveness 0, counted by script on 4 November, Qwen answered none of the 20 questions and reports. Version policy 5, the older Qwen3-4B stays in its own repository without a note. File health 5, the files agree with the card in substance but reports wait without a reply. 11 + 5 + 0 + 0 + 5 + 5 = 26. ### Risk: Qwen3-4B-Instruct-2507 +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. - Qwen3-4B-Instruct-2507 (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, with a checksum on every file. Safety evaluation 0, none published. 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, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent. Files unchanged on 4 November, and QwenLM/Qwen3 still has no SECURITY.md. 25 + 15 + 0 + 0 + 15 + 0 = 55. ## Score by category | Category | Weight | Ornith-1.5-122B | Qwen3-4B-Instruct-2507 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 75 | 90 | Qwen3-4B-Instruct-2507 +15 | | Risk | 20% (20 this run) | 50 | 55 | Qwen3-4B-Instruct-2507 +5 | | Agent interface | 10% (10 this run) | 85 | 75 | Ornith-1.5-122B +10 | | Documentation and upkeep | 10% (10 this run) | 36 | 26 | Ornith-1.5-122B +10 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **54.5 · C** | | ## Facts side by side | Fact | Ornith-1.5-122B | Qwen3-4B-Instruct-2507 | | --- | --- | --- | | 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 | 2025-09-17 | | 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. ### Qwen3-4B-Instruct-2507 - -1.2 on 4 November 2026: product ## 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. **Qwen3-4B-Instruct-2507.** A small dense instruct model for instruction following, short tool-calling jobs and long documents, with no thinking step. Its 4-bit builds need about 3 GB under Apache 2.0, but no live inference provider was found for it on 4 November 2026. Its files haven't changed in over a year, Alibaba hasn't answered its discussions, and no safety results are published. ## 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 ### Qwen3-4B-Instruct-2507 1. Quickest start is `ollama run qwen3:4b-instruct`, 2.5 GB. Plain `qwen3:4b` is the Thinking-2507 edition, not this one 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-4B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes` 3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags 4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed 5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition 6. Drop the context to 32,768 tokens if memory runs out, as the card suggests 7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, Ornith-1.5-122B or Qwen3-4B-Instruct-2507? Ornith-1.5-122B scores 61 (C) on agent readiness against Qwen3-4B-Instruct-2507's 54.5 (C), and leads in 2 of 4 scored categories. Qwen3-4B-Instruct-2507 leads on reach and risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ornith-1-5-122b", "b": "qwen3-4b-instruct-2507"}`. 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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) - [Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.md) - [Ornith-1.5-397B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.md) - [Ornith-1.5-9B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.md) - [OTel-2.0-LLM-31B-IT vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-4b-instruct-2507.md) - [Qwen-AgentWorld-35B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-qwen3-4b-instruct-2507.md) - [Qwen3-30B-A3B-Instruct-2507 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-qwen3-4b-instruct-2507.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-0-8b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-122b-a10b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-2b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-397b-a17b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-4b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-5-9b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-27b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-6-35b-a3b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-27b.md) - [Qwen3-4B-Instruct-2507 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-8-flash-next.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-coder-30b-a3b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-coder-next.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-omni-30b-a3b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-2b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-30b-a3b-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) - [Qwen3-4B-Instruct-2507 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-4b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwen3-vl-8b-instruct.md) - [Qwen3-4B-Instruct-2507 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-qwopus3-8-27b-flash-v2.md) - [Qwen3-4B-Instruct-2507 vs Recursive Flint](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint.md) - [Qwen3-4B-Instruct-2507 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-ripple-65b-preview.md) - [Qwen3-4B-Instruct-2507 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-sensenova-loop-9b-preview.md) - [Qwen3-4B-Instruct-2507 vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-smollm3-3b.md) - [Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-spark-x2-6-4b.md) - [Qwen3-4B-Instruct-2507 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-step-3-7-flash.md) - [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-27b.md) - [Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.md) - [Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.md) - [Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-2b.md) - [Qwen3-4B-Instruct-2507 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-wevisone-vl-4b.md) - [Qwen3-4B-Instruct-2507 vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-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