Ornith 1.5 35B A3B: Self-Improving Agentic Coding MoE
Ornith 1.5 35B A3B is the mid-size Mixture-of-Experts member of the Ornith-1.5 family from Ornith. It has about 35B total parameters and activates about 3B per token. Ornith-1.5 builds on Ornith-1.0, which was developed on top of Qwen3.5 and Gemma 4 with additional continued pretraining, mid-training and post-training. This checkpoint uses the Qwen3.5 MoE architecture.
Ornith-1.5 is trained through end-to-end self-improvement. Rather than relying on a fixed set of human-curated tasks and hand-designed harnesses, the training loop generates new tasks, builds the scaffolds to solve them, discovers effective strategies, and improves the policy through reinforcement learning.
Key Features
- Agentic Coding - Built for terminal-based coding agents and tool-calling workflows
- Sparse Mixture-of-Experts - 256 experts with 8 routed per token, so only about 3B of the 35B parameters are active for each token
- Hybrid Attention - Alternates linear-attention (Gated DeltaNet) layers with full attention, one full-attention layer in every four
- Reasoning Model - Opens each answer with a thinking block; the serving setup returns it in a separate reasoning field
- Tool Calling - Emits function calls that the server parses into standard OpenAI-style tool calls
- Native 262K Context - Handles 262,144 tokens natively, extensible to roughly 1M tokens with YaRN RoPE scaling
- Image Input - The checkpoint keeps the vision encoder from its Qwen3.5 base and accepts images alongside text
Use Cases
- Coding agents and coding CLIs pointed at an OpenAI-compatible endpoint
- Repository-level software engineering tasks and bug fixing
- Multi-step tool use and MCP-based agent workflows
- Long-context code understanding across large codebases
- Research and web-browsing agents
Benchmark Performance
The publisher reports the following results, averaged over five independent runs.
Coding:
- SWE-bench Verified: 79
- SWE-bench Multilingual: 71.4
- SWE-bench Pro: 59.6
- Terminal-Bench 2.1 (Terminus-2): 67.8
- Terminal-Bench 2.1 (Claude Code): 68.5
- NL2Repo: 46.2
- SWE Atlas QnA: 39.8
- DeepSWE: 22
Reasoning:
- GPQA Diamond: 89.2
- HLE (no tools): 25.6
- HLE (with tools): 33.4
Agentic:
- ClawEval: 72.5
- MCP-Atlas: 70.2
- WideSearch: 67.8
- BrowseComp: 67.6
- Toolathlon-Verified: 48.7
Recommended Sampling
The publisher recommends temperature 0.6, top_p 0.95 and top_k 20 for general tasks, and temperature 1.0 to reproduce the reported benchmarks.
Ornith 1.5 35B A3B gives teams a small-footprint agentic coding model: about 3B active parameters per token, a 262K native context window, and built-in reasoning and tool calling for coding agents. Model weights and the publisher's quantized builds are available on HuggingFace.
