Which is cheaper, Mistral Large or Mistral Large 2407?

Mistral Large is the cheaper of the two: one 1,000-word request costs about 3,017 toman.

Which one has the larger context window?

Mistral Large 2407, with 131,072 tokens.

Mistral Large vs Mistral Large 2407

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	Mistral Large	Mistral Large 2407
Provider	Mistral	Mistral
Model id	mistralai/mistral-large	mistralai/mistral-large-2407
Context	128,000 tokens	131,072 tokens
Max output	102,400 tokens	104,857 tokens
Input / 1M tokens	580,250 toman	580,250 toman
Output / 1M tokens	1,740,750 toman	1,740,750 toman
≈ one 1,000-word request	3,017 toman	3,017 toman
Input caching	58,025 toman / 1M	58,025 toman / 1M
Image input	No	No
File input	Yes	Yes
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	No	No

Which one for what?

Mistral Large

Agents that call your own APIs and database

3,017 toman per 1,000 words · Model page

Mistral Large 2407

Agents that call your own APIs and database

3,017 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/mistral-large",
    "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/mistral-large",
    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/mistral-large",
  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/mistral-large',
    '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/mistral-large",
		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-large-2407"

More comparisons: all pairs · model rankings · full catalogue

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