Which is cheaper, Gemini 2.5 Flash or DeepSeek V3?

DeepSeek V3 is the cheaper of the two: one 1,000-word request costs about 456 toman, about 2.3× cheaper than Gemini 2.5 Flash.

Which one has the larger context window?

Gemini 2.5 Flash, with 1,048,576 tokens.

Gemini 2.5 Flash vs DeepSeek V3

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, DeepSeek V3 comes out about 2.3× cheaper.

Feature	Gemini 2.5 Flash	DeepSeek V3
Provider	Google	DeepSeek
Model id	google/gemini-2.5-flash	deepseek/deepseek-chat
Context	1,048,576 tokens	163,840 tokens
Max output	65,535 tokens	16,384 tokens
Input / 1M tokens	87,038 toman	92,840 toman
Output / 1M tokens	725,313 toman	258,211 toman
≈ one 1,000-word request	1,056 toman	456 toman
Input caching	8,704 toman / 1M	No
Image input	Yes	No
File input	Yes	No
Tool calling	Yes	Yes
JSON output	Yes	Yes
Reasoning mode	Yes	No

Which one for what?

Gemini 2.5 Flash

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

1,056 toman per 1,000 words · Model page

DeepSeek V3

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

456 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": "google/gemini-2.5-flash",
    "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="google/gemini-2.5-flash",
    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: "google/gemini-2.5-flash",
  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' => 'google/gemini-2.5-flash',
    '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:    "google/gemini-2.5-flash",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "deepseek/deepseek-chat"

More comparisons: all pairs · model rankings · full catalogue

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