Get the latest setups
Models/Ornith 1.5/Ornith 1.5 35B A3B
Text / ornith-ai

Ornith 1.5 35B A3B

Ornith 1.5 35B A3B is a text-generation model from the Ornith-1.5 family, identified as its mid-size mixture-of-experts member. It activates approximately 3B parameters per token. According to the official model card, Ornith-1.5 extends Ornith-1.0 by jointly optimizing task generation, scaffold construction, and solution rollouts within a self-improvement loop. The model card reports that it outperforms similar-sized peer Qwen 3.6-35B across all coding and agentic benchmarks, and outperforms dense models such as Gemma 4-31B and Muse Glimmer-30B by wide margins on agentic coding. The repository is listed under the MIT license and is tagged for text-generation with safetensors.

EstimatedRepository opened Aug 18, 2026Source checked 9/24/2026Version: 10fbf86f
LOCALRENTED GPUOPEN WEIGHTS
Freshness 80 out of 100. Repository age: 58 days old, Aug 18, 2026. Measures age, not quality.
Popularity 73 out of 100. 254,974 downloads in 30 days; 729 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
35.95B
Architecture
qwen3_5_moe
License
mit
Disk space
67 GB
Software
Ollama, llama.cpp, SGLang, vLLM
View the model source
MY HARDWARE

Will it run on your machine?

Save your machine to see a personalized rating and its reasoning.

Add my hardware

Best for

  • Text generation
  • Coding benchmarks
  • Agentic benchmarks
  • Terminal-Bench 2.1 evaluation
  • Self-improvement research involving task generation, scaffold construction, and solution rollouts

Tradeoffs

  • Only text-generation pipeline is specified.
  • No compatibility, performance, or hardware requirements beyond the supplied facts are stated.
  • The supplied source lists MIT license; no additional licensing details are provided beyond the model card.
  • Benchmark claims come from the official model card, not from independent testing.
  • No purchase recommendation or testing claim is made.

Ways to run it

Ollama · Windows, macOS, Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

ollama run hf.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF
Official instructions

llama.cpp · Windows, macOS, Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144
Official instructions

SGLang · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

python -m sglang.launch_server --model-path ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --host 0.0.0.0 --port 8000 --tp 2 --context-length 262144 --mem-fraction-static 0.85 --tool-call-parser qwen3_coder --reasoning-parser qwen3
Official instructions

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --host 0.0.0.0 --port 8000 --tensor-parallel-size 2 --max-model-len 262144 --gpu-memory-utilization 0.90 --enable-prefix-caching --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3 --trust-remote-code
Official instructions

Explore its uses

SAFETENSORSTEXT-GENERATION

Keep exploring

Text
Prism ML

Bonsai 2 · 27B

A compressed 27B-class reasoning model with publisher-provided GGUF packs and a dedicated llama.cpp fork for CUDA, Metal, and CPU.

Freshness 90 out of 100. Release age: 28 days old, Sep 17, 2026. Measures age, not quality.
Popularity 90 out of 100. 4,849,199 downloads in 30 days; 2,697 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
MODEL SIZE27.36B
LOCALRENTED GPU
Released Sep 17, 2026
Text
DeepSeek

DeepSeek V4.1 Flash

A multimodal reasoning model with a compressed key-value cache, published as open weights.

Freshness 87 out of 100. Repository age: 35 days old, Sep 10, 2026. Measures age, not quality.
Popularity 84 out of 100. 1,238,583 downloads in 30 days; 4,519 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
Arena Open Source Text: rank 5 of 225. Ranked model: deepseek-v4.1-flash-max. Export dated Oct 13, 2026. Source checked Oct 15, 2026. Human preference ranking, not hardware suitability.+3
LOCALRENTED GPU
Repository opened Sep 10, 2026
Text
Qwen

Qwen3.8

Explore 4 versions of Qwen3.8. Compare model sizes, packages, hardware requirements, and available setups.

4 versionsCompare sizes & formats
LOCALRENTED GPU
Compare versions →