Moduck

Arcee Ai: Trinity Large Thinking

Reasoning-optimized 398B MoE agent model with extended thinking for long-horizon and multi-turn tool use

Model lab
Arcee Ai
Model family
trinity
Modalitiestext → text
Context window524,288
Maximum output262,144
Input · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.2 / 1M tokens
Output · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.64 / 1M tokens

Quickstart

Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to arcee-ai/trinity-large-thinking.

import os
import requests

base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "arcee-ai/trinity-large-thinking"

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.

Trinity Large Thinking API Pricing

TypePrice information
Input · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.2 / 1M tokens
Output · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.64 / 1M tokens
Cached input · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.048 / 1M tokens

Benchmarks

Intelligence Index · v4.310.8
Coding Index25.8
Agentic Index1

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.

Artificial Analysis measurement

Output speed335.88 tokens/s
Time to first token1.19 s
Time to first answer token—
End-to-end response time—

Performance trends

Throughput335.88 tok/sCurrent
Time to first token1.19 sCurrent
End-to-end latency7.2 sCurrent
Throughput
Current335.88 tok/s
Window average378.3 tok/s
Time to first token
Current1.19 s
Window average1.2 s
End-to-end latency
Current7.2 s
Window average7.5 s
Tail latency (P99)
Current15.0 s
Window average17.2 s
Prompt cache hit rate
Current26.4%
Window average24.7%
Error rate
Current0.7%
Window average0.2%

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.

Uptime100%At least one provider responded
Availability100%Requests actually served
Error rate0%Share of failed requests
Served requests, hour by hour
24 hours agoNow

Trinity Large Thinking Compare models

ModelInput priceOutput priceContext windowCapabilities
Trinity Large ThinkingThis pageUSD 0.2 / 1M tokens · Context ≤ 262144 tokens · Maximum output 262144 tokensUSD 0.64 / 1M tokens · Context ≤ 262144 tokens · Maximum output 262144 tokens524,288Text output · Reasoning · Tool calling
Trinity MiniUSD 0.036 / 1M tokensUSD 0.12 / 1M tokens131,072Text output · Reasoning · Tool calling
Trinity Nano PreviewPrice currently unavailablePrice currently unavailable131,072Text output · Tool calling
Beam 1.0 501B A23BUSD 0.2 / 1M tokensUSD 0.8 / 1M tokens1,048,576Text output · Reasoning · Tool calling
Ling 3.1 FlashUSD 0.06 / 1M tokensUSD 0.176 / 1M tokens262,144Text output · Reasoning · Tool calling

Model specifications

Input
text
Output
text
Context window
524,288
Maximum output
262,144
Release date
2026-04-01
Attachments
No
Reasoning
Yes
Tool calling
Yes
Temperature setting
Yes
Open weights
Yes

Frequently asked questions

What is Trinity Large Thinking for?

Reasoning-optimized 398B MoE agent model with extended thinking for long-horizon and multi-turn tool use

What inputs and outputs does Trinity Large Thinking support?

Recorded inputs: text; outputs: text.

How is Trinity Large Thinking priced?

The pricing table shows the current recorded Moduck price. For example: Input · Context ≤ 262144 tokens · Maximum output 262144 tokens USD 0.2 / 1M tokens; Output · Context ≤ 262144 tokens · Maximum output 262144 tokens USD 0.64 / 1M tokens. See the pricing table for all units, variants, and conditions.

What specifications are recorded for Trinity Large Thinking?

Current model records list Context window 524,288 tokens; Maximum output 262,144 tokens.

What model parameter does the Quickstart use?

The example uses arcee-ai/trinity-large-thinking. This value comes from the page identifier and is not confirmed as a callable Moduck public ID.

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.