Which is cheaper, Codestral 2508 or Saba?

Saba is the cheaper of the two: one 1,000-word request costs about 302 toman, about 1.5× cheaper than Codestral 2508.

Which one has the larger context window?

Codestral 2508, with 256,000 tokens.

Codestral 2508 vs Saba

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, Saba comes out about 1.5× cheaper.

Feature	Codestral 2508	Saba
Provider	Mistral	Mistral
Model id	mistralai/codestral-2508	mistralai/mistral-saba
Context	256,000 tokens	32,768 tokens
Max output	204,800 tokens	26,214 tokens
Input / 1M tokens	87,038 toman	58,025 toman
Output / 1M tokens	261,113 toman	174,075 toman
≈ one 1,000-word request	453 toman	302 toman
Input caching	8,704 toman / 1M	5,803 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?

Codestral 2508

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

453 toman per 1,000 words · Model page

Saba

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

302 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/codestral-2508",
    "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/codestral-2508",
    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/codestral-2508",
  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/codestral-2508',
    '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/codestral-2508",
		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-saba"

More comparisons: all pairs · model rankings · full catalogue

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