Mistral Nemo vs Llama 3 8B Lunaris
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, Mistral Nemo comes out about 1.9× cheaper.
| Feature |  Mistral Nemo | Llama 3 8B Lunaris |
|---|---|---|
| Provider | Mistral | Sao10k |
| Model id | mistralai/mistral-nemo | sao10k/l3-lunaris-8b |
| Context | 131,072 tokens | 8,192 tokens |
| Max output | 16,384 tokens | 7,372 tokens |
| Input / 1M tokens | 5,512 toman | 11,605 toman |
| Output / 1M tokens | 8,704 toman | 14,506 toman |
| ≈ one 1,000-word request | 18 toman | 34 toman |
| Input caching | No | No |
| Image input | No | No |
| File input | No | No |
| Tool calling | Yes | No |
| JSON output | Yes | Yes |
| Reasoning mode | No | No |
Which one for what?
Mistral Nemo
- Agents that call your own APIs and database
- High-volume work where cost per call decides
18 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-nemo",
"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-nemo",
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-nemo",
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-nemo',
'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-nemo",
Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
})
if err != nil {
panic(err)
}
fmt.Println(resp.Choices[0].Message.Content)
}model = "sao10k/l3-lunaris-8b"
More comparisons: all pairs · model rankings · full catalogue
base_url = https://api.uttapen.ir/v1