Which is cheaper, DeepSeek V4 Flash Vision Exp or R1 Distill Llama 70B?

DeepSeek V4 Flash Vision Exp is the cheaper of the two: one 1,000-word request costs about 332 toman, about 1.8× cheaper than R1 Distill Llama 70B.

Which one has the larger context window?

DeepSeek V4 Flash Vision Exp, with 1,048,576 tokens.

DeepSeek V4 Flash Vision Exp vs R1 Distill Llama 70B

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 V4 Flash Vision Exp comes out about 1.8× cheaper.

Feature	DeepSeek V4 Flash Vision Exp	R1 Distill Llama 70B
Provider	DeepSeek	DeepSeek
Model id	deepseek/deepseek-v4-flash-vision-exp	deepseek/deepseek-r1-distill-llama-70b
Context	1,048,576 tokens	8,192 tokens
Max output	384,000 tokens	7,372 tokens
Input / 1M tokens	63,828 toman	232,100 toman
Output / 1M tokens	191,483 toman	232,100 toman
≈ one 1,000-word request	332 toman	603 toman
Input caching	2,031 toman / 1M	No
Image input	Yes	No
File input	No	No
Tool calling	Yes	No
JSON output	Yes	No
Reasoning mode	Yes	Yes

Which one for what?

DeepSeek V4 Flash Vision Exp

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

332 toman per 1,000 words · Model page

R1 Distill Llama 70B

Multi-step problems, maths, and tracking down a bug

603 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": "deepseek/deepseek-v4-flash-vision-exp",
    "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="deepseek/deepseek-v4-flash-vision-exp",
    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: "deepseek/deepseek-v4-flash-vision-exp",
  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' => 'deepseek/deepseek-v4-flash-vision-exp',
    '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:    "deepseek/deepseek-v4-flash-vision-exp",
		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-r1-distill-llama-70b"

More comparisons: all pairs · model rankings · full catalogue

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