Which is cheaper, Trinity Large Thinking or R1 Distill Llama 70B?

Trinity Large Thinking is the cheaper of the two: one 1,000-word request costs about 396 toman, about 1.5× cheaper than R1 Distill Llama 70B.

Which one has the larger context window?

Trinity Large Thinking, with 262,144 tokens.

Trinity Large Thinking vs R1 Distill Llama 70B

Both sit behind the same key and the same code on uttapen — the only thing that changes is the model string, so you can run each of them against your own workload without touching anything else. On cost, Trinity Large Thinking comes out about 1.5× cheaper.

Feature	Trinity Large Thinking	R1 Distill Llama 70B
Provider	Arcee	DeepSeek
Model id	arcee-ai/trinity-large-thinking	deepseek/deepseek-r1-distill-llama-70b
Context	262,144 tokens	8,192 tokens
Max output	80,000 tokens	7,372 tokens
Input / 1M tokens	72,531 toman	232,100 toman
Output / 1M tokens	232,100 toman	232,100 toman
≈ one 1,000-word request	396 toman	603 toman
Input caching	17,408 toman / 1M	No
Image input	No	No
File input	No	No
Tool calling	Yes	No
JSON output	No	No
Reasoning mode	Yes	Yes

Which one for what?

Trinity Large Thinking

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Agents that call your own APIs and database
High-volume work where cost per call decides

396 toman per 1,000 words · Model page

R1 Distill Llama 70B

Multi-step problems, maths, and tracking down a bug

603 toman per 1,000 words · Model page

Run both with the same code

Swap the model value between the two ids and send the same request twice; what each answer cost comes back in the X-Uttapen-Cost-Toman header.

curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arcee-ai/trinity-large-thinking",
    "messages": [{"role": "user", "content": "Introduce yourself in one sentence."}],
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uttapen.ir/v1",
    api_key="sk-up-…",
)

stream = client.chat.completions.create(
    model="arcee-ai/trinity-large-thinking",
    messages=[{"role": "user", "content": "Introduce yourself in one sentence."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.uttapen.ir/v1",
  apiKey: "sk-up-…",
});

const stream = await client.chat.completions.create({
  model: "arcee-ai/trinity-large-thinking",
  messages: [{ role: "user", content: "Introduce yourself in one sentence." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

<?php
// composer require openai-php/client guzzlehttp/guzzle
$client = OpenAI::factory()
    ->withBaseUri('https://api.uttapen.ir/v1')
    ->withApiKey('sk-up-…')
    ->make();

$result = $client->chat()->create([
    'model' => 'arcee-ai/trinity-large-thinking',
    'messages' => [['role' => 'user', 'content' => 'Introduce yourself in one sentence.']],
]);
echo $result->choices[0]->message->content;

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://api.uttapen.ir/v1"),
		option.WithAPIKey("sk-up-…"),
	)
	resp, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model:    "arcee-ai/trinity-large-thinking",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "deepseek/deepseek-r1-distill-llama-70b"

More comparisons: all pairs · model rankings · full catalogue

base_url = https://api.uttapen.ir/v1