[whatmodel.sh](../../README.md) / Ornith 1.0 35B (35B-A3B)
# Ornith 1.0 35B (35B-A3B)
DeepReinforce · released 2026-06 · 35B params (3B active) · mit
**score 66.6** ±1.0 · #118 of 732 · from 17 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Ornith 1.0 35B (35B-A3B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅●▄▄▂▁ #103 of 313 78% 9
coding ▁▁▁▃▅▇█▇●▄▃▁ #111 of 469 52% 4
web search ●··········█ #2 of 2 8.3% 3
agent tasks · #103 of 313 · 78% of its score
Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [o3-pro](o3-pro.md), [Grok 4.3](grok-4-3.md) here, though they rank higher overall. Trails [Nova 2 Pro (Preview)](nova-2-pro.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md), [Hy3 preview (295B-A21B)](hy3-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 55.8% ▄▄·····●▄▄▄█ beats 2/7 hard · avg 16% 19% sharp · at its level · several runs
Terminal-Bench 2.1 36.0% ▄······█▄▄●▄ beats 6/7 very hard · avg 7.2% 18% sharp · at its level · several runs
SWE-bench Pro (split unspecified) 34.5% ▄···▄···▄●██ beats 3/7 very hard · avg 6.2% 18% sharp · at its level · several runs
SWE-bench Multilingual 51.7% ▄·▄·····█●█▄ beats 4/7 hard · avg 18% 10% at its level · several runs
MCP-Atlas (public set) (maker) 64.4% ▂·▁▂▂·▁▃●▆▆▂ beats 18/39 hard · avg 30% 3.3% at its level · small run
BrowseComp (maker) 63.5% ▂·▃▁▂▂▆▅●▂▂▄ beats 30/46 hard · avg 27% 3.2% at its level · small run
Humanity's Last Exam with tools (… 30.1% ▁·▁▂▂▁●▄▇█▄▅ beats 13/62 very hard · avg 11% 2.9% at its level · small run
WideSearch (maker) 63.4% █·●▆··▂···▂▂ beats 4/10 medium · avg 37% 2.2% at its level · small run
Toolathlon-Verified (maker) 42.4% ▂·▂●▅▃···▂██ beats 2/18 hard · avg 27% 1.8% blunt · at its level · small run
coding · #111 of 469 · 52% of its score
Beats [Grok 4.3](grok-4-3.md), [Muse Glimmer 30B](muse-glimmer-30b.md), [Qwen3.5 Plus](qwen3-5-plus.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 55.8% ▄▄·····●▄▄▄█ beats 2/7 hard · avg 16% 19% sharp · at its level · several runs
SWE-bench Pro (split unspecified) 34.5% ▄···▄···▄●██ beats 3/7 very hard · avg 6.2% 18% sharp · at its level · several runs
SWE-bench Multilingual 51.7% ▄·▄·····█●█▄ beats 4/7 hard · avg 18% 10% at its level · several runs
SciCode (level unspecified) 30.3% ▄···▄▄●▄·▄·▄ beats 3/7 very hard · avg 13% 5.4% at its level
web search · #2 of 2 · 8.3% of its score
test result vs the others in its run difficulty counts why
BrowseComp (maker) 63.5% ▂·▃▁▂▂▆▅●▂▂▄ beats 30/46 hard · avg 27% 3.2% at its level · small run
Humanity's Last Exam with tools (… 30.1% ▁·▁▂▂▁●▄▇█▄▅ beats 13/62 very hard · avg 11% 2.9% at its level · small run
WideSearch (maker) 63.4% █·●▆··▂···▂▂ beats 4/10 medium · avg 37% 2.2% at its level · small run
other tests (3)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 86.2% ▁▁▁▁▂▄▅▅██●▄ beats 243/322 hard · avg 45% 8.9% sharp · small run
Humanity's Last Exam (maker) 20.8% ▁▁▁▄▆▆●██▆▁▃ beats 53/111 very hard · avg 4.8% 4.3% small run
NL2Repo (maker) 34.6% ▂▂▂·▃●▅█▃▂▅▃ beats 6/22 very hard · avg 12% 3.2% at its level · small run
source results share of the score
ornith-ai/Ornith-1.5-35B-A3B model card… 12 52% ▇▇▇▇▇▇
Kwaipilot/KAT-Coder-V2.5-Dev model card 5 47% ▇▇▇▇▇▇
prior: pull toward the middle 0% ▇
weighted average = 66.6
Licences: ornith-ai/Ornith-1.5-35B-A3B model card ([MIT](https://opensource.org/licenses/MIT)) · Kwaipilot/KAT-Coder-V2.5-Dev model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Ornith 1.0 35B (35B-A3B) (Q4_K_M, needs ~21.2 GB, from [huggingface.co/ornith-ai](https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF/tree/383064f72a1ef3087b779f268d3ca117eb989aac))
```sh
llama cli -hf ornith-ai/Ornith-1.0-35B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ornith-ai/Ornith-1.0-35B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~36.9 GB) `llama cli -hf ornith-ai/Ornith-1.0-35B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Ornith 1.0 35B (35B-A3B) (Q4_K_M, needs ~21.2 GB, from [huggingface.co/ornith-ai](https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF/tree/383064f72a1ef3087b779f268d3ca117eb989aac))
```sh
lms get https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~36.9 GB) `lms get https://huggingface.co/ornith-ai/Ornith-1.0-35B-GGUF@Q8_0 -y` · 4bit (~20.4 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Ornith-1.0-35B-MLX-4bit -y` · 8bit (~37.7 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Ornith-1.0-35B-MLX-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Ornith 1.0 35B (35B-A3B) (Q4_K_M, needs ~21 GB, from [ollama.com](https://ollama.com/library/ornith/tags))
```sh
ollama run ornith:35b
```
Other versions: Q8_0 (~37 GB) `ollama run ornith:35b-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Ornith 1.0 35B (35B-A3B) (4bit, needs ~20.4 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Ornith-1.0-35B-MLX-4bit/tree/f5d815a19138fc9a75091688303a4abb959ac408))
```sh
mlx_lm.chat --model lmstudio-community/Ornith-1.0-35B-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Ornith-1.0-35B-MLX-4bit
```
Other versions: 8bit (~37.7 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Ornith-1.0-35B-MLX-8bit`
Model repository and documentation: [huggingface.co/ornith-ai](https://huggingface.co/ornith-ai/Ornith-1.0-35B)
## Published speed measurements
Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine.
hardware precision speed engine and settings
Apple M5 Max (40-core GPU),… q4_k_m 102.76 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
[interactive version](https://whatmodel.sh/model/ornith-1-0-35b) · [JSON](https://whatmodel.sh/api?format=json&all=1)