Which is cheaper, Skyfall 36B V2 or Trinity Large Thinking?

Trinity Large Thinking is the cheaper of the two: one 1,000-word request costs about 396 toman, about 1.3× cheaper than Skyfall 36B V2.

Which one has the larger context window?

Trinity Large Thinking, with 262,144 tokens.

Skyfall 36B V2 vs Trinity Large Thinking

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 1.3× cheaper.

Feature	Skyfall 36B V2	Trinity Large Thinking
Provider	Thedrummer	Arcee
Model id	thedrummer/skyfall-36b-v2	arcee-ai/trinity-large-thinking
Context	32,768 tokens	262,144 tokens
Max output	29,491 tokens	80,000 tokens
Input / 1M tokens	159,569 toman	72,531 toman
Output / 1M tokens	232,100 toman	232,100 toman
≈ one 1,000-word request	509 toman	396 toman
Input caching	72,531 toman / 1M	17,408 toman / 1M
Image input	No	No
File input	No	No
Tool calling	No	Yes
JSON output	Yes	No
Reasoning mode	No	Yes

Which one for what?

Skyfall 36B V2

Chat, summarising, and everyday text generation

509 toman per 1,000 words · Model page

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

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": "thedrummer/skyfall-36b-v2",
    "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="thedrummer/skyfall-36b-v2",
    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: "thedrummer/skyfall-36b-v2",
  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' => 'thedrummer/skyfall-36b-v2',
    '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:    "thedrummer/skyfall-36b-v2",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "arcee-ai/trinity-large-thinking"

More comparisons: all pairs · model rankings · full catalogue

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

Skyfall 36B V2 vs Trinity Large Thinking

Which one for what?

TSkyfall 36B V2

Trinity Large Thinking

Run both with the same code

Skyfall 36B V2