GPT-6 Luna vs Gemini 3.8 Flash
Different jobs at the low-cost end. GPT-6 Luna is the volume model — quick to answer and able to skip reasoning entirely. Gemini 3.8 Flash is far stronger at coding and agent work and reads audio and video, but it is slow to start and wordy.
Choose GPT-6 Luna if
- Short, high-volume calls where the first word matters.
- Simple sorting and extraction with reasoning switched off.
- Predictable output length.
Choose Gemini 3.8 Flash if
- Coding agents — 73.7% on DeepSWE.
- Audio and video analysis.
- Finance and legal agent work.
Side by side
| GPT-6 Luna | Gemini 3.8 Flash | |
|---|---|---|
| Made by | OpenAI | |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | ₹10.09 | ₹75.71 |
| Output price, per 1M tokens | ₹50.47 | ₹378.53 |
| Cached input, per 1M tokens | ₹1.01 | ₹7.57 |
| Batch (about half price) | Yes | Yes |
| Context window | 1.05M tokens | 1.05M tokens |
| Longest answer | 128K tokens | 66K tokens |
| Reads | Text, Images | Text, Images, Audio, Video, PDFs |
| Thinks before answering | Yes, adjustable | Always on |
| Open weights | No | 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 ₹98.00 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.
GPT-6 Luna does the same job for 7.5× 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="gpt-6-luna", …) client.chat.completions.create(model="gemini-3.8-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.