GLM-5.3-Flash vs Qwen 3.8 Flash
Both are low-cost models that read images and video. GLM-5.3-Flash is open under MIT and always thinks; Qwen 3.8 Flash activates only 6 billion parameters per token and lets you turn reasoning down, but it is famously wordy.
Choose GLM-5.3-Flash if
- Open weights under MIT.
- Coding agents — 63.4% on DeepSWE.
- Answers that are always reasoned through.
Choose Qwen 3.8 Flash if
- Phone and desktop automation — 84.5% on AndroidWorld.
- Reasoning effort you can turn down.
- Long video and documents in a million-token window.
Side by side
| GLM-5.3-Flash | Qwen 3.8 Flash | |
|---|---|---|
| Made by | Zhipu AI | Alibaba |
| Released | August 2026 | August 2026 |
| Input price, per 1M tokens | S$0.2535 | S$0.2155 |
| Output price, per 1M tokens | S$0.845 | S$0.6752 |
| Cached input, per 1M tokens | S$0.0507 | S$0.0243 |
| Batch (about half price) | No | No |
| Context window | 1M tokens | 1M tokens |
| Longest answer | 128K tokens | 131K tokens |
| Reads | Text, Images, Video | Text, Images, Video |
| Thinks before answering | Always on | Yes, adjustable |
| Open weights | Yes, MIT | No |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures. Prices are our published US dollar rates converted at S$1.30 to the dollar, our rate for October — the same rate your card is charged.
What a real job costs
Take 1,000 requests, each about 3,000 tokens in and 1,000 out — a long prompt and a page of answer.
Qwen 3.8 Flash does the same job for 1.2× less. Models that think before answering spend extra output tokens doing it, so on hard prompts the real gap can be wider than the rates suggest.
Use both, switch any time
On Roar AI both are on the same key and the same monthly invoice, paid in your local currency. Trying the other one is a one-word change:
client.chat.completions.create(model="glm-5.3-flash", …) client.chat.completions.create(model="qwen3.8-flash", …)
Send the same prompts to both for a day and compare the answers on your own work — that settles it faster than any benchmark. Read the docs.
Try both
on one key
Send the same prompts to each and compare the answers on your own work.