Unbiased: Pareto
Blended multimodal model that runs multiple language models in parallel and returns a synthesized answer
- Model lab
- Unbiased
- Model family
- —
Quickstart
Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to moduck/unbiased/pareto.
import os
import requests
base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "moduck/unbiased/pareto"
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model_id,
"messages": [{"role": "user", "content": "Hello!"}],
"stream": False,
},
timeout=60,
)
response.raise_for_status()
print(response.json())Use the familiar OpenAI-style chat request format to make model integration easier.
Pareto API Pricing
| Type | Price information |
|---|---|
| Input | USD 2 / 1M tokens |
| Output | USD 6 / 1M tokens |
| Cached input | USD 0.2 / 1M tokens |
Performance
Measured on the Moduck gateway across live traffic, hour by hour over the last 24 hours. Time to first token is how long before anything appears; throughput is how fast the answer is written after that; end-to-end latency is the whole round trip. Figures are medians unless the card says otherwise.
Performance trends
Availability
Uptime is the share of time at least one provider serving this model responded. Availability is stricter: the share of requests actually served. A provider that answers but fails part of the traffic lowers availability without lowering uptime. Both cover the last three days.
Pareto Compare models
| Model | Input price | Output price | Context window | Capabilities |
|---|---|---|---|---|
| ParetoThis page | USD 2 / 1M tokens | USD 6 / 1M tokens | 262,144 | Text output · Tool calling |
| DeepSeek V4.1 Flash 1017 | USD 0.028 / 1M tokens · Quantization fp4 | USD 0.4 / 1M tokens · Quantization fp4 | 1,000,000 | Text output · Reasoning · Tool calling |
| Mistral Large 4 | USD 0.544 / 1M tokens | USD 1.672 / 1M tokens | 1,048,576 | Text output · Reasoning · Tool calling |
| LongCat-2.5-Preview | USD 0.6 / 1M tokens | USD 2.36 / 1M tokens | 1,000,000 | Text output · Reasoning · Tool calling |
| Ember-1 | USD 2.4 / 1M tokens | USD 12 / 1M tokens | 1,048,576 | Text output · Reasoning · Tool calling |
Model specifications
- Input
- text, image
- Output
- text
- Context window
- 262,144
- Maximum output
- 131,072
- Release date
- 2026-09-17
- Attachments
- Yes
- Reasoning
- No
- Tool calling
- Yes
- Structured output
- No
- Temperature setting
- Yes
- Open weights
- No
Frequently asked questions
What is Pareto for?
Blended multimodal model that runs multiple language models in parallel and returns a synthesized answer
What inputs and outputs does Pareto support?
Recorded inputs: text, images; outputs: text.
How is Pareto priced?
The pricing table shows the current recorded Moduck price. For example: Input USD 2 / 1M tokens; Output USD 6 / 1M tokens. See the pricing table for all units, variants, and conditions.
What specifications are recorded for Pareto?
Current model records list Context window 262,144 tokens; Maximum output 131,072 tokens.
What model parameter does the Quickstart use?
The example uses moduck/unbiased/pareto. This is a cataloged Moduck public or reserved ID; check your account’s available models before calling it.
Can I call this model through the Moduck API now?
This page has no verified Moduck API call for this model. A Quickstart example or reserved ID does not establish access; check the models available to your account.
Related models
No related catalog models are recorded for this model family.