Which is cheaper, GLM 4.5 Air or Llama 3.1 Euryale 70B v2.2?

GLM 4.5 Air is the cheaper of the two: one 1,000-word request costs about 370 toman, about 1.7× cheaper than Llama 3.1 Euryale 70B v2.2.

Which one has the larger context window?

GLM 4.5 Air, with 131,072 tokens.

GLM 4.5 Air vs Llama 3.1 Euryale 70B v2.2

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, GLM 4.5 Air comes out about 1.7× cheaper.

Feature	GLM 4.5 Air	Llama 3.1 Euryale 70B v2.2
Provider	Z.ai (GLM)	Sao10k
Model id	z-ai/glm-4.5-air	sao10k/l3.1-euryale-70b
Context	131,072 tokens	131,072 tokens
Max output	98,304 tokens	16,384 tokens
Input / 1M tokens	37,716 toman	246,606 toman
Output / 1M tokens	246,606 toman	246,606 toman
≈ one 1,000-word request	370 toman	641 toman
Input caching	7,253 toman / 1M	No
Image input	No	No
File input	No	No
Tool calling	Yes	Yes
JSON output	No	Yes
Reasoning mode	Yes	No

Which one for what?

GLM 4.5 Air

Multi-step problems, maths, and tracking down a bug
Agents that call your own APIs and database
High-volume work where cost per call decides

370 toman per 1,000 words · Model page

Llama 3.1 Euryale 70B v2.2

Agents that call your own APIs and database

641 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": "z-ai/glm-4.5-air",
    "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="z-ai/glm-4.5-air",
    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: "z-ai/glm-4.5-air",
  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' => 'z-ai/glm-4.5-air',
    '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:    "z-ai/glm-4.5-air",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "sao10k/l3.1-euryale-70b"

More comparisons: all pairs · model rankings · full catalogue

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

GLM 4.5 Air vs Llama 3.1 Euryale 70B v2.2

Which one for what?

GLM 4.5 Air

SLlama 3.1 Euryale 70B v2.2

Run both with the same code

Llama 3.1 Euryale 70B v2.2