Ornith 1.5 9B: Self-Improving Agentic Coding Model

Ornith-1.5-9B is the smallest member of the Ornith-1.5 family from Ornith AI, a 9B dense reasoning model built for agentic coding and tool use. The Ornith line started from Qwen3.5 and Gemma 4 base models and added its own continued pretraining, mid-training and post-training. This 9B checkpoint uses the Qwen3.5 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-built agent harnesses, the training loop generates its own new tasks, builds the scaffolds used to attempt them, discovers strategies that solve them, and improves the model through reinforcement learning. Ornith-1.0 optimized scaffolds and rollouts; Ornith-1.5 extends the loop to task generation as well.

Key Features

• Reasoning model - each answer opens with a thinking block, which the server returns separately from the final answer; the chat template also accepts an enable_thinking switch to skip it
• Tool calling - emits structured function calls that the server turns into standard OpenAI-style tool calls, so it plugs into existing agent frameworks
• Built for coding agents - tuned for terminal-based coding assistants and works with any OpenAI-compatible client or coding CLI
• Long context - a 262,144-token context window, enough for large codebases and long agent sessions
• Lightweight - the smallest model in the Ornith-1.5 family, a dense model designed for efficient deployment

Architecture

Ornith-1.5-9B keeps the Qwen3.5 hybrid design: 32 layers in which every fourth layer is full attention and the other three are linear-attention (gated delta) layers. The linear layers hold a fixed-size state instead of a growing key-value cache, so memory grows slowly as context gets longer. A vision encoder feeds image tokens into the same text backbone. Weights are published in BF16.

Benchmarks

Ornith reports every result as an average over five runs, and compares the 9B against its predecessor Ornith-1.0-9B and the Qwen3.5-9B base. On coding, it scores 70.6 on SWE-bench Verified (Qwen3.5-9B: 53.2), 47.5 on SWE-bench Pro (31.3), 54.4 on SWE-bench Multilingual (39.7) and 32.4 on NL2Repo (16.2). On Terminal-Bench 2.1 it reaches 46.2 with the Terminus-2 harness and 47 with Claude Code (Qwen3.5-9B: 21.3 and 18.9).

Agentic results follow the same pattern: 54.2 on MCP-Atlas, 41.2 on Toolathlon-Verified, 56.4 on BrowseComp, 59.5 on WideSearch and 66.5 on ClawEval. On reasoning it scores 86.4 on GPQA Diamond and 20.2 on Humanity's Last Exam without tools, rising to 30.5 with tools.

Use Cases

• Backend model for terminal coding agents and coding CLIs
• Software engineering agents that read a repository, edit code, run tests and iterate
• Tool-calling and MCP-based agents for workflow automation
• Research and browsing agents that chain many search and tool steps
• Reasoning-heavy question answering where a visible thinking trace is useful

Full model details are available on the Ornith-1.5-9B model card.