Llama 3.3 70B Instruct vs Qwen3-VL 8B

	Llama 3.3 70B Instruct	Qwen3-VL 8B
Provider	Meta	Alibaba Qwen
Model id	`meta-llama/Llama-3.3-70B-Instruct-Turbo`	`Qwen/Qwen3-VL-8B-Instruct`
Input price	$1.35 / 1M tokens	$0.24 / 1M tokens
Output price	$1.35 / 1M tokens	$0.90 / 1M tokens
Context window	131K tokens	262K tokens
Capabilities	Chat, Tool calling, JSON mode, Multilingual	Vision, Chat, OCR, Tool calling
Weights	Open	Open
Availability	Available on Inference APIs	Coming soon
Rate limits	No daily caps; pay per request	No daily caps; pay per request
Time to first token	1.29 s	not measured
Output speed	67.5 tokens / s	not measured

Cost for 1M input + 1M output tokens

Llama 3.3 70B Instruct

$2.7

Qwen3-VL 8B

$1.14

Qwen3-VL 8B is about 58% cheaper for this workload at list price.

Measured speed

Speed figures for Inference APIs models are medians of three runs from a European client on 2026-09-16, against the public endpoint, using a ~120-word generation prompt (chat), a 300-character paragraph (speech) or a -second clip (transcription). External models are not measured here; treat "not measured" as unknown, not slow.

When to pick which

Llama 3.3 70B Instruct — Meta's instruction-tuned 70B model. Still served here after its retirement on Groq — the same llama-3.3-70b-versatile id works unchanged.
Qwen3-VL 8B — Compact vision-language model for image understanding, OCR and screenshot reasoning.

Try Llama 3.3 70B Instruct

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": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    "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="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    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: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
  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.