Head to head · Open-weight models you run yourself · October 2026 research run
LongCat-2.5 vs Ornith-1.5-122B
Ornith-1.5-122B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
Ahead on
- Risk, 60 against 50
- Documentation and upkeep, 46 against 36
Watch for
3,562 GB in BF16 and 1,336 GB at 4 bits
Best 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 35
- Agent interface, 85 against 75
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
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Ornith-1.5-122B +40
LongCat-2.5 35
Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories 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. 0 + 0 + 10 + 15 + 10 + 0 = 35.
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.
Risk LongCat-2.5 +10
LongCat-2.5 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
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.
Agent interface Ornith-1.5-122B +10
LongCat-2.5 75
Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.
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.
Documentation and upkeep LongCat-2.5 +10
LongCat-2.5 46
Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
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.
Score by category
| Category | Weight this run | LongCat-2.5 | Ornith-1.5-122B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 35 | 75 | Ornith-1.5-122B +40 |
| Risk | 20%20 | 60 | 50 | LongCat-2.5 +10 |
| Agent interface | 10%10 | 75 | 85 | Ornith-1.5-122B +10 |
| Documentation and upkeep | 10%10 | 46 | 36 | LongCat-2.5 +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.5/100 | C 61/100 |
Facts side by side
| Fact | LongCat-2.5 | Ornith-1.5-122B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Meituan (LongCat) | Ornith |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-10 | 2026-10-24 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
LongCat-2.5
Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.
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
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Ornith-1.5-122B
- Start it with
ollama run ornith-1.5:122b-q4_K_M, orllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144 - 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
- 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 - Thinking is on by default. Send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it
- Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends
- 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, LongCat-2.5 or Ornith-1.5-122B?
Ornith-1.5-122B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and documentation and upkeep.
Other comparisons with LongCat-2.5 or Ornith-1.5-122B
- Agents-A1-4B vs LongCat-2.5
- Agents-A1-4B vs Ornith-1.5-122B
- Apertus v1.5 8B vs LongCat-2.5
- Apertus v1.5 8B vs Ornith-1.5-122B
- Apodex-1.1-mini vs LongCat-2.5
- Apodex-1.1-mini vs Ornith-1.5-122B
- Arete-27B vs LongCat-2.5
- Arete-27B vs Ornith-1.5-122B
- BigBang-v1 vs LongCat-2.5
- BigBang-v1 vs Ornith-1.5-122B
- Carbon-X vs LongCat-2.5
- Carbon-X vs Ornith-1.5-122B
- Cola2 vs LongCat-2.5
- Cola2 vs Ornith-1.5-122B
- Cosmos-Reason2-2B vs LongCat-2.5
- Cosmos-Reason2-2B vs Ornith-1.5-122B
- Darwin-36B-Opus vs LongCat-2.5
- Darwin-36B-Opus vs Ornith-1.5-122B
- DeepSeek-V3.2 vs LongCat-2.5
- DeepSeek-V3.2 vs Ornith-1.5-122B
- DeepSeek V4.1 Flash vs LongCat-2.5
- DeepSeek V4.1 Flash vs Ornith-1.5-122B
- DeepSeek-V4-Flash-0731 vs LongCat-2.5
- DeepSeek-V4-Flash-0731 vs Ornith-1.5-122B
- Devstral Small 2 vs LongCat-2.5
- Devstral Small 2 vs Ornith-1.5-122B
- DiffusionGemma 26B A4B IT vs LongCat-2.5
- DiffusionGemma 26B A4B IT vs Ornith-1.5-122B
- EXAONE 4.5 33B vs LongCat-2.5
- EXAONE 4.5 33B vs Ornith-1.5-122B
- Gemma 4 12B IT vs LongCat-2.5
- Gemma 4 12B IT vs Ornith-1.5-122B
- Gemma 4 26B A4B IT vs LongCat-2.5
- Gemma 4 26B A4B IT vs Ornith-1.5-122B
- Gemma 4 31B IT vs LongCat-2.5
- Gemma 4 31B IT vs Ornith-1.5-122B
- Gemma 4 E2B IT vs LongCat-2.5
- Gemma 4 E2B IT vs Ornith-1.5-122B
- Gemma 4 E4B IT vs LongCat-2.5
- Gemma 4 E4B IT vs Ornith-1.5-122B
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.5-Air vs Ornith-1.5-122B
- GLM-4.7-Flash vs LongCat-2.5
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-5.3-Flash vs LongCat-2.5
- GLM-5.3-Flash vs Ornith-1.5-122B
- GLM-5.3 vs LongCat-2.5
- GLM-5.3 vs Ornith-1.5-122B
- gpt-oss-120b vs LongCat-2.5
- gpt-oss-120b vs Ornith-1.5-122B
- gpt-oss-20b vs LongCat-2.5
- gpt-oss-20b vs Ornith-1.5-122B
- Granite 4.2 30B vs LongCat-2.5
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 3B vs LongCat-2.5
- Granite 4.2 3B vs Ornith-1.5-122B
- Granite 4.2 8B vs LongCat-2.5
- Granite 4.2 8B vs Ornith-1.5-122B
- Hy-Memory-30B-A3B vs LongCat-2.5
- Hy-Memory-30B-A3B vs Ornith-1.5-122B
- Hy3 vs LongCat-2.5
- Hy3 vs Ornith-1.5-122B
- Inkling-Small vs LongCat-2.5
- Inkling-Small vs Ornith-1.5-122B
- Inkling vs LongCat-2.5
- Inkling vs Ornith-1.5-122B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-122B
- Kimi K3.1 Pro vs LongCat-2.5
- Kimi K3.1 Pro vs Ornith-1.5-122B
- Kimi K3 vs LongCat-2.5
- Kimi K3 vs Ornith-1.5-122B
- Kolibri-1 vs LongCat-2.5
- Kolibri-1 vs Ornith-1.5-122B
- Kronos vs LongCat-2.5
- Kronos vs Ornith-1.5-122B
- Laguna M.1 vs LongCat-2.5
- Laguna M.1 vs Ornith-1.5-122B
- Laguna S 2.1 vs LongCat-2.5
- Laguna S 2.1 vs Ornith-1.5-122B
- Laguna XS 2.1 vs LongCat-2.5
- Laguna XS 2.1 vs Ornith-1.5-122B
- LBS-27B vs LongCat-2.5
- LBS-27B vs Ornith-1.5-122B
- LFM2.5-2.6B vs LongCat-2.5
- LFM2.5-2.6B vs Ornith-1.5-122B
- LFM3-1.7B vs LongCat-2.5
- LFM3-1.7B vs Ornith-1.5-122B
- LFM3-Audio-2B vs LongCat-2.5
- LFM3-Audio-2B vs Ornith-1.5-122B
- LFM3-Omni-2B vs LongCat-2.5
- LFM3-Omni-2B vs Ornith-1.5-122B
- LFM3-VL-2B vs LongCat-2.5
- LFM3-VL-2B vs Ornith-1.5-122B
- Ling-3.0-tiny vs LongCat-2.5
- Ling-3.0-tiny vs Ornith-1.5-122B
- Ling-3.1-flash-1019 vs LongCat-2.5
- Ling-3.1-flash-1019 vs Ornith-1.5-122B
- LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B
- LongCat-2.5 vs MiMo-V2.6-Flash-RL
- LongCat-2.5 vs MiniCPM-o 4.5
- LongCat-2.5 vs MiniCPM-V 4.7 1B
- LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B
- LongCat-2.5 vs MiniCPM5-2B
- LongCat-2.5 vs MiniMax-M2.5
- LongCat-2.5 vs MiniMax-M2.7
- LongCat-2.5 vs MiniMax-M3.1-Flash
- LongCat-2.5 vs Mistral Large 4
- LongCat-2.5 vs Muse Glimmer 30B
- LongCat-2.5 vs Nanbeige4.2-3B
- LongCat-2.5 vs Nanbeige4.3-33B
- LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 4B
- LongCat-2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LongCat-2.5 vs Nemotron 3 Super 120B A12B
- LongCat-2.5 vs Nemotron 3 Ultra 550B A55B
- LongCat-2.5 vs North Mini Code 1.0
- LongCat-2.5 vs occamy-1.0
- LongCat-2.5 vs Olmo 3 7B Think
- LongCat-2.5 vs Ornith-1.5-35B-A3B
- LongCat-2.5 vs Ornith-1.5-397B
- LongCat-2.5 vs Ornith-1.5-9B
- LongCat-2.5 vs OTel-2.0-LLM-31B-IT
- LongCat-2.5 vs Qwen-AgentWorld-35B-A3B
- LongCat-2.5 vs Qwen3-30B-A3B-Instruct-2507
- LongCat-2.5 vs Qwen3-4B-Instruct-2507
- LongCat-2.5 vs Qwen3.5-0.8B
- LongCat-2.5 vs Qwen3.5-122B-A10B
- LongCat-2.5 vs Qwen3.5-2B
- LongCat-2.5 vs Qwen3.5-397B-A17B
- LongCat-2.5 vs Qwen3.5-4B
- LongCat-2.5 vs Qwen3.5-9B
- LongCat-2.5 vs Qwen3.6-27B
- LongCat-2.5 vs Qwen3.6-35B-A3B
- LongCat-2.5 vs Qwen3.8-27B
- LongCat-2.5 vs Qwen3.8-Flash-Next
- LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-Coder-Next
- LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-2B-Instruct
- LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-32B-Instruct
- LongCat-2.5 vs Qwen3-VL-4B-Instruct
- LongCat-2.5 vs Qwen3-VL-8B-Instruct
- LongCat-2.5 vs Qwopus3.8-27B-Flash-V2
- LongCat-2.5 vs Recursive Flint
- LongCat-2.5 vs Ripple-65B Preview
- LongCat-2.5 vs SenseNova-Loop-9B-Preview
- LongCat-2.5 vs SmolLM3-3B
- LongCat-2.5 vs Spark-X2.6-4B
- LongCat-2.5 vs Step 3.7 Flash
- LongCat-2.5 vs Swift 1.5 Qwen3.8-27B
- LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next
- LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)
- LongCat-2.5 vs Baidu Unlimited-OCR-2
- LongCat-2.5 vs WeVisOne-VL-2B
- LongCat-2.5 vs WeVisOne-VL-4B
- LongCat-2.5 vs ZAYA1-8B
- MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B
- MiMo-V2.6-Flash-RL vs Ornith-1.5-122B
- MiniCPM-o 4.5 vs Ornith-1.5-122B
- MiniCPM-V 4.7 1B vs Ornith-1.5-122B
- MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B
- MiniCPM5-2B vs Ornith-1.5-122B
- MiniMax-M2.5 vs Ornith-1.5-122B
- MiniMax-M2.7 vs Ornith-1.5-122B
- MiniMax-M3.1-Flash vs Ornith-1.5-122B
- Mistral Large 4 vs Ornith-1.5-122B
- Muse Glimmer 30B vs Ornith-1.5-122B
- Nanbeige4.2-3B vs Ornith-1.5-122B
- Nanbeige4.3-33B vs Ornith-1.5-122B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-122B
- Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 4B vs Ornith-1.5-122B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-122B
- Nemotron 3 Super 120B A12B vs Ornith-1.5-122B
- Nemotron 3 Ultra 550B A55B vs Ornith-1.5-122B
- North Mini Code 1.0 vs Ornith-1.5-122B
- occamy-1.0 vs Ornith-1.5-122B
- Olmo 3 7B Think vs Ornith-1.5-122B
- Ornith-1.5-122B vs Ornith-1.5-35B-A3B
- Ornith-1.5-122B vs Ornith-1.5-397B
- Ornith-1.5-122B vs Ornith-1.5-9B
- Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT
- Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B
- Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507
- Ornith-1.5-122B vs Qwen3-4B-Instruct-2507
- Ornith-1.5-122B vs Qwen3.5-0.8B
- Ornith-1.5-122B vs Qwen3.5-122B-A10B
- Ornith-1.5-122B vs Qwen3.5-2B
- Ornith-1.5-122B vs Qwen3.5-397B-A17B
- Ornith-1.5-122B vs Qwen3.5-4B
- Ornith-1.5-122B vs Qwen3.5-9B
- Ornith-1.5-122B vs Qwen3.6-27B
- Ornith-1.5-122B vs Qwen3.6-35B-A3B
- Ornith-1.5-122B vs Qwen3.8-27B
- Ornith-1.5-122B vs Qwen3.8-Flash-Next
- Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-Coder-Next
- Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-2B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-32B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-4B-Instruct
- Ornith-1.5-122B vs Qwen3-VL-8B-Instruct
- Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2
- Ornith-1.5-122B vs Recursive Flint
- Ornith-1.5-122B vs Ripple-65B Preview
- Ornith-1.5-122B vs SenseNova-Loop-9B-Preview
- Ornith-1.5-122B vs SmolLM3-3B
- Ornith-1.5-122B vs Spark-X2.6-4B
- Ornith-1.5-122B vs Step 3.7 Flash
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next
- Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)
- Ornith-1.5-122B vs Baidu Unlimited-OCR-2
- Ornith-1.5-122B vs WeVisOne-VL-2B
- Ornith-1.5-122B vs WeVisOne-VL-4B
- Ornith-1.5-122B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/longcat-2-5-vs-ornith-1-5-122b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/longcat-2-5.json·/api/v1/tools/ornith-1-5-122b.json - From a terminal
anchor compare longcat-2-5 ornith-1-5-122b(the CLI) - Over MCP
compare_tools {"a": "longcat-2-5", "b": "ornith-1-5-122b"}at/mcp, no key