# Cola2 vs Ornith-1.5-122B > Ornith-1.5-122B scores 61 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b - Markdown: https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.md (~9,000 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 Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself. - Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.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 ### Cola2 (F) Good for: Researchers working on latent or diffusion language models who want a checkpoint to study Ahead on: - Documentation and upkeep, 58 against 36 Watch for: No licence stated for the weights ### 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: - Reach, 75 against 25 - Risk, 50 against 40 - Agent interface, 85 against 20 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 ### Agent interface: Ornith-1.5-122B +65 - Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20. - 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. ### Reach: Ornith-1.5-122B +50 - Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25. - 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. ### Documentation and upkeep: Cola2 +22 - Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58. - 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. ### Risk: Ornith-1.5-122B +10 - Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40. - 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 | Cola2 | Ornith-1.5-122B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 25 | 75 | Ornith-1.5-122B +50 | | Risk | 20% (20 this run) | 40 | 50 | Ornith-1.5-122B +10 | | Agent interface | 10% (10 this run) | 20 | 85 | Ornith-1.5-122B +65 | | Documentation and upkeep | 10% (10 this run) | 58 | 36 | Cola2 +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **33.9 · F** | **61 · C** | | ## Facts side by side | Fact | Cola2 | Ornith-1.5-122B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | ByteDance (Seed) | Ornith | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | None stated for the weights (the code is Apache 2.0) | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-11-06 | 2026-10-24 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | ## Verdicts **Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights. **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 ### Cola2 1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions 2. Don't use it commercially on the strength of this listing. The weights have no stated licence 3. If you study it, read modeling_cola2.py first: trust_remote_code runs it 4. Pin Transformers below 5, as the card requires 5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding 6. Check the download with `sha256sum -c SHA256SUMS` ### 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, Cola2 or Ornith-1.5-122B? Ornith-1.5-122B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "cola2", "b": "ornith-1-5-122b"}`. 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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) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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