Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog.

Ornith 1.5 397B

This model card documents Ornith-1.5-397B, the flagship member of the Ornith-1.5 family — a 397B mixture-of-experts model. It scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, performing on par with Claude Opus 4.8 (85.0 and 59.0) while outperforming leading open-source models of similar scale, including GLM-5.2 and DeepSeek-V4-Flash-0731.

Benchmarks

	Ornith-1.5-397B	DeepSeek-V4-Flash-0731 _(284B)	GLM-5.2 _(753B)	Claude Opus 4.8	Kimi K3 _(2.8T)	Ornith-1.0-397B
Coding
Terminal-Bench 2.1 _(Terminus-2)	86.1	82.7	81	85	88.3	77.5
Terminal-Bench 2.1 _{(Claude Code)}	85.2	81.8	82.7	78.9	-	78.2
SWE-bench Verified	86	81.6	83	85.8	86.2	82.4
SWE-bench Pro	65.1	64.4	62.1	68	-	62.2
SWE-bench Multilingual	79.6	77.9	78.4	75.7	-	78.9
DeepSWE	56	54.4	46.2	59	67.5	8
Frontier-Bench v0.1	13.5	6.1	5.1	21.1	23	2.7
NL2Repo	59.5	54.2	48.9	69.7	-	48.2
SWE Atlas - QnA	55.6	51.6	50	59.7	59.7	41.2
Reasoning
HLE _{(no tools)}	44.6	35	40.5	49.8	43.5	30.2
HLE _{(with tools)}	56.1	50.8	54.7	57.9	56	47.5
GPQA Diamond	92.8	91.4	91.2	93.6	93.5	88.1
Agentic
MCP-Atlas	80	74.6	77.8	82.2	82.3	76.4
Toolathlon-Verified	71.2	70.3	48.2	76.2	73.2	43.2
WideSearch	80.8	77.3	79	72.9	-	75.2
BrowseComp	86.6	84.8	85.6	84.3	91.2	79.7
ClawEval	81.4	77.6	78.8	80.2	-	77.1

* All results reported for Ornith-1.5 are averaged over five independent runs.
* Terminal-Bench 2.1 (Terminus-2): We evaluate Terminal-Bench 2.1 using the Harbor/Terminus-2 framework with parser=json, temperature=1.0, top_p=1.0, and a 128K context window. Each run uses a 4-hour timeout with 32 CPU cores and 48GB RAM, and results are averaged over 5 runs. We adjust the Qwen chat template to ensure consistency between training and inference (https://huggingface.co/ornith-ai/Ornith-1.5-397B/blob/main/chat_template.jinja), and modify Harbor to align with vLLM's reasoning_content key.
* Terminal-Bench 2.1 (Claude Code): We evaluate Terminal-Bench 2.1 using Claude Code 2.1.126 with parser=json, temperature=1.0, top_p=1.0, max_new_tokens=131072. Results are averaged over 5 runs. Again, Qwen chat template needs to be modified.
* SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window. Anti-hacking safeguards are applied throughout evaluation: Git history is removed from the local repository image to prevent access to prior solutions or commits; network access is disabled, preventing the model from retrieving external information or resources.
* DeepSWE: Evaluated using the Claude Code harness with temperature=1.0, top_p=0.95, and a 256K context window.
* SWE Atlas QnA: using mini SWE agent harness with temp=1.0, top_p=0.95, 128K context window. Results are averaged over 5 runs.
* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output. Access to specified GitHub repositories and pip packages is blocked to prevent reward hacking.
* HLE: Evaluated using Claude 4.6 Opus as the judge model.
* MCP-Atlas: All models were evaluated in thinking mode on the 500-task public subset, with a 10-minute timeout per task. We use Claude 4.8 Opus as the judge model.
* Toolathlon-Verified: We use the official evaluation service with the maximum token limit set to 128K.
* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.

Quickstart

📝 NOTE

Ornith-1.5-397B is a reasoning model: by default the assistant turn opens with a <think> … </think> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate reasoning_content field, and a tool-call parser so the model's <tool_call> blocks are surfaced as OpenAI-style tool_calls.

Serving Ornith-1.5-397B requires recent runtimes:

Transformers ≥ 5.8.1
vLLM ≥ 0.19.1
SGLang ≥ 0.5.9

Recommended sampling parameters:

For general tasks: temperature=0.6, top_p=0.95, top_k=20
To reproduce the reported benchmarks: temperature=1.0

Serving Ornith-1.5-397B

Ornith-1.5-397B is a ~397B mixture-of-experts model (≈800 GB in bf16), so multi-GPU serving is required. The recipes below use 8-way tensor parallelism on a single node (e.g., 8× H200 141GB); adjust --tensor-parallel-size / --tp to match your hardware, or use FP8/INT4 quantized builds for smaller deployments.

vLLM

bash

vllm serve ornith-ai/Ornith-1.5-397B \
    --served-model-name Ornith-1.5-397B \
    --host 0.0.0.0 --port 8000 \
    --tensor-parallel-size 8 \
    --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

SGLang

bash

python -m sglang.launch_server \
    --model-path ornith-ai/Ornith-1.5-397B \
    --served-model-name Ornith-1.5-397B \
    --host 0.0.0.0 --port 8000 \
    --tp 8 \
    --context-length 262144 \
    --mem-fraction-static 0.85 \
    --tool-call-parser qwen3_coder \
    --reasoning-parser qwen3

For Long-Context

Ornith-1.5-397B handles context windows of up to 262,144 tokens. When a task's combined input and output must go beyond this limit, we suggest extending the effective window with RoPE scaling — YaRN is the technique we validate against, and it is already built into both vLLM and SGLang. With a scaling factor of 4.0, the usable window grows to roughly 1M tokens.

You can turn YaRN on in either of two ways:

Edit the checkpoint's config.json. Add a rope_scaling block to the model configuration:

json
1{ 2 "rope_scaling": { 3 "rope_type": "yarn", 4 "factor": 4.0, 5 "original_max_position_embeddings": 262144 6 } 7}
Override at launch time. Leave the checkpoint untouched and extend the serve commands above with the equivalent flags.

vLLM:

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ornith-ai/Ornith-1.5-397B ... --hf-overrides '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --max-model-len 1000000

SGLang:

SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --context-length 1000000

📝 NOTE

Open-source runtimes implement YaRN statically: the same scaling factor is applied to every request regardless of its length, which can slightly hurt quality on ordinary-length inputs. Only enable rope_scaling when your workload genuinely needs the longer window, and size factor to match it — the target window is roughly factor × 262,144, so if your requests top out around 524,288 tokens, factor: 2.0 is the better setting.

Using Ornith-1.5-397B via the Chat Completions API

Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.

Basic Usage

python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY",  # any non-empty string works for a local server
)

response = client.chat.completions.create(
    model="Ornith-1.5-397B",
    messages=[
        {"role": "user", "content": "Write a one-line Python lambda that squares a number."}
    ],
    temperature=0.6,
    top_p=0.95,
    max_tokens=1024,
)

message = response.choices[0].message
# reasoning_content holds the <think> trace; content holds the final answer.
print("reasoning:", getattr(message, "reasoning_content", None))
print("answer:", message.content)

You can also stream tokens, or hand the model tools — Ornith-1.5-397B emits well-formed function calls that the server parses into the standard tool_calls field:

python

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="Ornith-1.5-397B",
    messages=[{"role": "user", "content": "What is the weather in Paris right now?"}],
    tools=tools,
    tool_choice="auto",
    temperature=0.6,
    max_tokens=2048,
)

tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.function.name, tool_call.function.arguments)
# -> get_weather {"city": "Paris"}

You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or curl at the same /v1/chat/completions endpoint.

Agentic Usage

Ornith-1.5-397B excels in tool-calling and agentic coding. It exposes an OpenAI-compatible endpoint with tool calling and works out of the box with standard agent frameworks.

Examples of using Ornith with agents:

Ollama

ollama run hf.co/ornith-ai/Ornith-1.5-397B-GGUF

Atomic.chat

bash

# Atomic.chat loads a GGUF build of Ornith (ornith-ai/Ornith-1.5-397B-GGUF)
# through llama.cpp's OpenAI-compatible API on port 8000.
llama-server -hf ornith-ai/Ornith-1.5-397B-GGUF --port 8000 -c 262144

llama.cpp

bash

# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf ornith-ai/Ornith-1.5-397B-GGUF --port 8000 -c 262144

Hermes Agent

bash

# Hermes talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export MODEL="ornith-ai/Ornith-1.5-397B"

OpenClaw

bash

# OpenClaw talks to any OpenAI-compatible endpoint — point it at your Ornith server.
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
export OPENAI_MODEL="ornith-ai/Ornith-1.5-397B"

Unsloth Studio

bash

pip install unsloth

# Load Ornith for fast local inference or fine-tuning (Python):
#   from unsloth import FastLanguageModel
#   model, tokenizer = FastLanguageModel.from_pretrained(
#       "ornith-ai/Ornith-1.5-397B",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

Coding CLIs

Ornith-1.5-397B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.5-397B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.

OpenCode

bash

# Register your local Ornith endpoint as a provider in ~/.config/opencode/opencode.json:
#
# {
#   "$schema": "https://opencode.ai/config.json",
#   "provider": {
#     "ornith": {
#       "npm": "@ai-sdk/openai-compatible",
#       "name": "Ornith (local)",
#       "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" },
#       "models": { "ornith-ai/Ornith-1.5-397B": { "name": "Ornith-1.5-397B" } }
#     }
#   }
# }

opencode

Citation

If you find our work helpful, feel free to give us a cite.

bibtex

@misc{ornith_1_5,
    title = {{Ornith-1.5}: From Self-Scaffolding to Self-Improvement},
    url = {https://ornith.ai/ornith_1_5.html},
    author = {{Ornith Team}},
    year = {2026}
}

ornith-ai/Ornith-1.5-397B-NVFP4

Ornith-1.5-397B

Ornith 1.5 397B

Benchmarks

Quickstart

Serving Ornith-1.5-397B

vLLM

SGLang

For Long-Context

Using Ornith-1.5-397B via the Chat Completions API

Basic Usage

Agentic Usage

Ollama

Atomic.chat

llama.cpp

Hermes Agent

OpenClaw

Unsloth Studio

Coding CLIs

OpenCode

Citation

ornith-ai/
Ornith-1.5-397B-NVFP4