Agnes-3.0-Flash

• Competitive across core capabilities. Agnes-3.0-Flash Preview posts competitive results across reasoning, coding, and instruction-following evaluations.
• Built for demanding work. A 262 144-token context window, adjustable reasoning effort, tool calling, and text, image and video understanding.

Benchmarks

Benchmark scope: The Agnes results in the chart and table below belong to the Agnes-3.0-Flash Preview open-weight checkpoint released in this repository. They are not results for the production/API Agnes 3.0 Flash model listed on Artificial Analysis.

The Agnes-3.0-Flash Preview scores in the chart correspond to the open-weight checkpoint released in this repository. Reference results across contemporary models are shown below. The figures were compiled from different sources, harnesses, and model snapshots and do not constitute a controlled head-to-head comparison.

Higher is better for every row. Header parameter figures mix total and active counts, and harnesses and snapshot dates differ across sources, so treat cross-column comparisons as reference values rather than a controlled head-to-head evaluation.

Architecture

Agnes-3.0-Flash Preview is a hybrid-attention decoder: three of every four layers run a gated delta rule (recurrent, with per-layer state independent of sequence length), and the fourth runs standard global attention. Only 18 of the 72 layers therefore hold a KV cache that grows with context.

Quickstart

Requirements

pip install "transformers>=5.12" torch torchvision accelerate

Tested on transformers 5.12.1. Image and video inputs go through the bundled processor, which needs torchvision.

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
path = "Agnes-AI/Agnes-3.0-Flash"
tok = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(
    path, dtype="bfloat16", device_map="auto", trust_remote_code=True
)
msgs = [{"role": "user", "content": "请用三句话解释什么是人工智能。"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Images and video

Image and video inputs go through the bundled processor (also remote code):

from transformers import AutoProcessor
proc = AutoProcessor.from_pretrained(path, trust_remote_code=True)
msgs = [{"role": "user", "content": [{"type": "image", "image": "photo.jpg"},
                                     {"type": "text", "text": "描述这张图。"}]}]
inputs = proc.apply_chat_template(msgs, add_generation_prompt=True, tokenize=True,
                                  return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256)
print(proc.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])

Reasoning effort

The chat template exposes three reasoning levels — high (default), medium, low — plus a thinking-off switch:

ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt",
                              reasoning_effort="medium")   # or enable_thinking=False

Tool calling

The chat template renders tool definitions for you. The model emits calls as ``, and you feed results back as a tool role message:

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Look up current weather for a city",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string", "description": "City name"}},
            "required": ["city"],
        },
    },
}]
msgs = [{"role": "user", "content": "What's the weather in Beijing right now?"}]
ids = tok.apply_chat_template(msgs, tools=tools, add_generation_prompt=True,
                              return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256)
reply = tok.decode(out[0][ids.shape[1]:], skip_sp