Qwen3-VL 8B vs Llama 3.3 70B on Groq

	Qwen3-VL 8B	Llama 3.3 70B on Groq
Provider	Alibaba Qwen	Groq
Model id	`Qwen/Qwen3-VL-8B-Instruct`	`llama-3.3-70b-versatile`
Input price	$0.24 / 1M tokens	$0.59 / 1M tokens
Output price	$0.90 / 1M tokens	$0.79 / 1M tokens
Context window	262K tokens	131K tokens
Capabilities	Vision, Chat, OCR, Tool calling	Chat, Tool calling, JSON mode
Weights	Open	Open
Availability	Coming soon	Retired
Rate limits	No daily caps; pay per request	Retired from Groq free and developer tiers on 2026-08-16 (enterprise contracts only).

Cost for 1M input + 1M output tokens

Qwen3-VL 8B

$1.14

Llama 3.3 70B on Groq

$1.38

Qwen3-VL 8B is about 17% cheaper for this workload at list price. Price is only part of it: free tiers and usage tiers cap how much you can send per day, and a retired model is unavailable at any price. The "Rate limits" row above is the practical difference for a production app.

When to pick which

Qwen3-VL 8B — Compact vision-language model for image understanding, OCR and screenshot reasoning.
Llama 3.3 70B on Groq — Retired from Groq free and developer tiers on 2026-08-16 (enterprise contracts only).

Try Qwen3-VL 8B

Same OpenAI request shape; the change is the base URL and key. Full parameters, aliases and pricing on the model page.

curl https://api.inferenceapis.com/v1/chat/completions \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-VL-8B-Instruct",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello! What can you do?"}
    ]
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.inferenceapis.com/v1",
    api_key=os.environ["INFERENCE_API_KEY"],
)
response = client.chat.completions.create(
    model="Qwen/Qwen3-VL-8B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello! What can you do?"},
    ],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.inferenceapis.com/v1",
  apiKey: process.env.INFERENCE_API_KEY,
});
const response = await client.chat.completions.create({
  model: "Qwen/Qwen3-VL-8B-Instruct",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello! What can you do?" },
  ],
});
console.log(response.choices[0].message.content);

External prices and limits come from the linked provider pages and were verified on 2026-09-16. Spotted a change? Tell us.