Which is cheaper, Hunyuan A13B Instruct or Mistral Small 3.1 24B?

Hunyuan A13B Instruct is the cheaper of the two: one 1,000-word request costs about 268 toman, about 1.3× cheaper than Mistral Small 3.1 24B.

Which one has the larger context window?

Hunyuan A13B Instruct, with 131,072 tokens.

Hunyuan A13B Instruct vs Mistral Small 3.1 24B

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, Hunyuan A13B Instruct comes out about 1.3× cheaper.

Feature	Hunyuan A13B Instruct	Mistral Small 3.1 24B
Provider	Tencent	Mistral
Model id	tencent/hunyuan-a13b-instruct	mistralai/mistral-small-3.1-24b-instruct
Context	131,072 tokens	128,000 tokens
Max output	117,964 tokens	102,400 tokens
Input / 1M tokens	40,618 toman	101,834 toman
Output / 1M tokens	165,371 toman	161,019 toman
≈ one 1,000-word request	268 toman	342 toman
Input caching	No	No
Image input	No	Yes
File input	No	No
Tool calling	No	No
JSON output	Yes	No
Reasoning mode	Yes	No

Which one for what?

Hunyuan A13B Instruct

Multi-step problems, maths, and tracking down a bug
High-volume work where cost per call decides

268 toman per 1,000 words · Model page

Mistral Small 3.1 24B

Reading screenshots, invoices, and scanned forms
High-volume work where cost per call decides

342 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": "tencent/hunyuan-a13b-instruct",
    "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="tencent/hunyuan-a13b-instruct",
    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: "tencent/hunyuan-a13b-instruct",
  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' => 'tencent/hunyuan-a13b-instruct',
    '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:    "tencent/hunyuan-a13b-instruct",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "mistralai/mistral-small-3.1-24b-instruct"

More comparisons: all pairs · model rankings · full catalogue

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