Models

Models

Fugu

Fugu balances strong performance with low latency, making it the ideal default for everyday work. Fugu will route to the best model based on the task at hand. You can also opt specific agents out of its pool to meet data, privacy, and compliance constraints.

Fugu Ultra

Fugu Ultra coordinates a deeper pool of expert agents to maximize answer quality on hard, high-stakes problems. Fugu Ultra focuses on maximizing performance, for a higher cost. It routes between one to three agents, depending on the problem.

Benchmark comparison

Reported benchmark scores across Fugu and other frontier models.

Benchmark Fugu-Ultra Fugu Opus 4.8 Gemini 3.1 GPT-5.5
SWE Bench Pro 73.7 59.0 69.2 54.2 58.6
Terminal Bench 2.1 82.1 80.2 74.6 70.3 78.2
LiveCodeBench 93.2 92.9 87.8 88.5 85.3
LiveCodeBench Pro 90.8 87.8 84.8 82.9 88.4
Humanity’s Last Exam 50.0 47.2 49.8 44.4 41.4
CharXiv Reasoning 86.6 85.1 84.2 83.3 84.1
GPQA Diamond 95.5 95.5 92.0 94.3 93.6
SciCode 58.7 60.1 53.5 58.9 56.1
τ3 Banking 20.6 21.7 20.6 8.4 20.6
Long Context Reasoning 73.3 74.7 67.7 72.7 74.3
MRCRv2 93.6 86.6 87.9 84.9 94.8
CTI-REALM 69.4 67.5 69.6 56.0 67.3

Example Usage

These examples use the OpenAI-compatible Responses API. Set your environment variables once, then copy and run the Python or cURL example you need.

Python

# pip install openai
import os
from openai import OpenAI

base_url = os.environ["FUGU_BASE_URL"].rstrip("/")
if not base_url.endswith("/v1"):
    base_url = f"{base_url}/v1"

client = OpenAI(
    api_key=os.environ["FUGU_API_KEY"],
    base_url=base_url,
)

with client.responses.stream(
    model="fugu-ultra",
    input="Explain why streaming is useful in three short bullets.",
) as stream:
    for event in stream:
        if event.type == "response.output_text.delta":
            print(event.delta, end="", flush=True)

# Full response object assembled from the stream.
    response = stream.get_final_response()

print()
print(response)

Supported endpoints

Fugu currently supports the OpenAI-compatible Chat Completions, Responses, and Models APIs. For generation requests, we strongly recommend using the Responses API, for better performance.

Responses API

Use /v1/responses when you want the Responses API shape, especially for tool use, multimodal input, and proper reasoning or function calls management.

Supported request fields

Field Type Description
model string Required. Model ID to use, such as fugu orfugu-ultra.
input `string array`
instructions string System / developer message passed to the model.
metadata object Arbitrary key-value pairs attached to the request.
stream boolean Stream the response.
max_output_tokens number Maximum number of tokens to generate. Note for fugu-ultra, this is applied only to the final model response, orchestrator model still uses maximum token limit.
reasoning object Reasoning controls with an effort value ofhigh, xhigh, or max.xhigh and max are aliases of the same reasoning effort. Default is xhigh for fugu-ultra,high for fugu.
tools array Tool definitions the model may call.
tool_choice `string object`
text.format object Structured output with text,json_object, or json_schema.
temperature number Accepted but ignored.
parallel_tool_calls boolean Accepted but ignored. Set to True on the server side for models that support it.
previous_response_id string Not accepted. Send the full conversation history directly ininput instead.

Chat Completions

Use /v1/chat/completions when you want the OpenAI Chat Completions API shape.