DeepSeek-V4.1-Flash vs GPT-6 Luna
Two inexpensive workhorses. DeepSeek-V4.1-Flash is much stronger at coding and agent work and is open under MIT; GPT-6 Luna is the steadier pick for simple, high-volume calls with reasoning switched off.
Choose DeepSeek-V4.1-Flash if
- Coding and tool-use loops — 74.2% on DeepSWE.
- Fine control over reasoning, on a 1–100 scale.
- Open weights under MIT.
Choose GPT-6 Luna if
- Simple calls with reasoning switched off.
- Classification, extraction and routing at volume.
- You already build on OpenAI models.
Side by side
| DeepSeek-V4.1-Flash | GPT-6 Luna | |
|---|---|---|
| Made by | DeepSeek | OpenAI |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | S$0.2535 | S$0.1339 |
| Output price, per 1M tokens | S$1.01 | S$0.6695 |
| Cached input, per 1M tokens | S$0.0254 | S$0.0134 |
| Batch (about half price) | No | Yes |
| Context window | 1.05M tokens | 1.05M tokens |
| Longest answer | 384K tokens | 128K tokens |
| Reads | Text, Images | Text, Images |
| Thinks before answering | Yes, adjustable | 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.
GPT-6 Luna does the same job for 1.7× 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="deepseek-v4.1-flash", …) client.chat.completions.create(model="gpt-6-luna", …)
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.