Qwen3.8 Flash vs GPT-4.1 mini
Qwen3.8 Flash is an open-weight chat model you can call on this endpoint; GPT-4.1 mini is a closed model from OpenAI. Below: what each costs for three realistic workloads, the limits that apply, and when to choose which.
OpenAI figures are public list prices and documented limits, checked 2026-09-16 against the provider page. Inference APIs prices are read live from the price list.
- Cost: Qwen3.8 Flash is cheaper in all three workloads, by 64–68%.
- Limits: OpenAI: Rate limits scale with cumulative spend through usage tiers 1–5. Here: no per-minute or per-day caps.
Side by side
| Qwen3.8 Flash | GPT-4.1 mini | |
|---|---|---|
| Served by | Inference APIs | OpenAI |
| Model author | Alibaba Qwen | OpenAI |
| Model id | Qwen/Qwen3.8-Flash | gpt-4.1-mini |
| Input price | $0.15 / 1M tokens | $0.40 / 1M tokens |
| Output price | $0.50 / 1M tokens | $1.60 / 1M tokens |
| Context window | 1M tokens | 1M tokens |
| Capabilities | Chat, Reasoning, Tool calling, JSON mode, Cached input | Chat, Vision, Tool calling, JSON mode |
| Weights | Open | Closed |
| Status | Available | Available |
| Limits | No per-minute or per-day request or token caps; usage draws on a prepaid balance | Rate limits scale with cumulative spend through usage tiers 1–5. |
What three workloads cost
List prices applied to the same work. The cheaper side of each row is in bold.
| Workload | Qwen3.8 Flash | GPT-4.1 mini | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $2.70 | $8.00 | 66% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $14.50 | $40.00 | 64% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $10.75 | $34.00 | 68% |
Qwen3.8 Flash is cheaper in all three workloads, by 64–68%. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.
When to choose which
- Cost is the deciding factor; it is cheaper in every workload above
- You want open weights: the same model can be moved to another host or self-hosted later
- You want a fast, inexpensive model with a 1M-token window and prompt caching
- Long-document summarisation and extraction where the whole file goes in one request
- You want open weights, or the option to move the same model to another host
- You need to send images; Qwen3.8 Flash is text-only
- You need image input together with a 1M-token context
Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each.
Switching from OpenAI
Both speak the OpenAI wire format. The edit is the base URL, the key and the model id; then re-run your own prompts, because it is a different model.
import os
from openai import OpenAI
# before: OpenAI
# client = OpenAI(base_url="https://api.openai.com/v1", api_key=os.environ["OPENAI_API_KEY"])
# MODEL = "gpt-4.1-mini"
# after: Inference APIs
client = OpenAI(base_url="https://api.inferenceapis.com/v1", api_key=os.environ["INFERENCE_API_KEY"])
MODEL = "Qwen/Qwen3.8-Flash"
resp = client.chat.completions.create(model=MODEL, messages=[{"role": "user", "content": "Hello"}], max_tokens=500)More detail, including Node, LangChain and LiteLLM: switching OpenAI-compatible providers.
FAQ
Which is cheaper, Qwen3.8 Flash or GPT-4.1 mini?
Qwen3.8 Flash is cheaper in all three workloads, by 64–68%. The table above uses list prices: Qwen3.8 Flash $0.15 / 1M tokens input, $0.50 / 1M tokens output; GPT-4.1 mini $0.40 / 1M tokens input, $1.60 / 1M tokens output.
Can I switch from GPT-4.1 mini to Qwen3.8 Flash without rewriting code?
Yes, if you call it through an OpenAI-compatible client. Change the base URL to https://api.inferenceapis.com/v1, swap the API key, and set the model to Qwen/Qwen3.8-Flash. Because it is a different model, re-run your prompts and evaluations before moving production traffic.
What limits apply to GPT-4.1 mini?
Rate limits scale with cumulative spend through usage tiers 1–5. On Inference APIs there are no per-minute or per-day request or token caps; usage draws on a prepaid balance.
Why does Qwen3.8 Flash use more output tokens than the visible answer?
It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.