Which is cheaper, Voxtral Small 24B 2507 or Ministral 3 14B 2512?

Voxtral Small 24B 2507 is the cheaper of the two: one 1,000-word request costs about 151 toman.

Which one has the larger context window?

Ministral 3 14B 2512, with 262,144 tokens.

Voxtral Small 24B 2507 vs Ministral 3 14B 2512

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.

Feature	Voxtral Small 24B 2507	Ministral 3 14B 2512
Provider	Mistral	Mistral
Model id	mistralai/voxtral-small-24b-2507	mistralai/ministral-14b-2512
Context	32,768 tokens	262,144 tokens
Max output	26,214 tokens	209,715 tokens
Input / 1M tokens	29,013 toman	58,025 toman
Output / 1M tokens	87,038 toman	58,025 toman
≈ one 1,000-word request	151 toman	151 toman
Input caching	2,901 toman / 1M	5,803 toman / 1M
Image input	No	Yes
File input	Yes	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Voxtral Small 24B 2507

Agents that call your own APIs and database
High-volume work where cost per call decides

151 toman per 1,000 words · Model page

Ministral 3 14B 2512

Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database
High-volume work where cost per call decides

151 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": "mistralai/voxtral-small-24b-2507",
    "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="mistralai/voxtral-small-24b-2507",
    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: "mistralai/voxtral-small-24b-2507",
  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' => 'mistralai/voxtral-small-24b-2507',
    '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:    "mistralai/voxtral-small-24b-2507",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "mistralai/ministral-14b-2512"

More comparisons: all pairs · model rankings · full catalogue

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