Weaver (alpha) vs Gemini 3.1 Flash Lite (batch)
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, Gemini 3.1 Flash Lite (batch) comes out about 1.3× cheaper.
| Feature | Weaver (alpha) |  Gemini 3.1 Flash Lite (batch) |
|---|---|---|
| Provider | Mancer | |
| Model id | mancer/weaver | google/gemini-3.1-flash-lite:batch |
| Context | 8,000 tokens | 1,048,576 tokens |
| Max output | 6,000 tokens | 65,536 tokens |
| Input / 1M tokens | 116,050 toman | 36,266 toman |
| Output / 1M tokens | 217,594 toman | 217,594 toman |
| ≈ one 1,000-word request | 434 toman | 330 toman |
| Input caching | No | 3,627 toman / 1M |
| Image input | No | Yes |
| File input | No | Yes |
| Tool calling | No | Yes |
| JSON output | Yes | Yes |
| Reasoning mode | No | Yes |
Which one for what?
Gemini 3.1 Flash Lite (batch)
- Multi-step problems, maths, and tracking down a bug
- 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
330 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": "mancer/weaver",
"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="mancer/weaver",
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: "mancer/weaver",
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' => 'mancer/weaver',
'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: "mancer/weaver",
Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
})
if err != nil {
panic(err)
}
fmt.Println(resp.Choices[0].Message.Content)
}model = "google/gemini-3.1-flash-lite:batch"
More comparisons: all pairs · model rankings · full catalogue
base_url = https://api.uttapen.ir/v1