Qwen3-VL 8B vs GPT-OSS 120B on Groq
Two chat models compared on price, context, capabilities. External prices are public list rates verified 2026-09-16; Inference APIs prices are live.
| Qwen3-VL 8B | GPT-OSS 120B on Groq | |
|---|---|---|
| Provider | Alibaba Qwen | Groq |
| Model id | Qwen/Qwen3-VL-8B-Instruct | openai/gpt-oss-120b |
| Input price | $0.24 / 1M tokens | $0.15 / 1M tokens |
| Output price | $0.90 / 1M tokens | $0.75 / 1M tokens |
| Context window | 262K tokens | 131K tokens |
| Capabilities | Vision, Chat, OCR, Tool calling | Chat, Reasoning, Tool calling, JSON mode |
| Weights | Open | Open |
| Availability | Coming soon | Available |
| Rate limits | No daily caps; pay per request | Free tier: 30 RPM, 1,000 RPD, 8K TPM, 200K TPD. Developer-tier upgrades paused as of Sep 2026. |
Cost for 1M input + 1M output tokens
GPT-OSS 120B on Groq is about 21% 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.
- GPT-OSS 120B on Groq — Free tier: 30 RPM, 1,000 RPD, 8K TPM, 200K TPD. Developer-tier upgrades paused as of Sep 2026.
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.
