Claude Sonnet 5.5 vs Gemini 3.8 Flash
Sonnet 5.5 suits anything a person waits on and is the stronger partner for coding in conversation. Gemini 3.8 Flash reads more kinds of input — audio and video as well as images and PDFs — and fits batch work, where its slow start does not matter.
Choose Claude Sonnet 5.5 if
- Anything a person waits on: Gemini 3.8 Flash takes about 13 seconds to begin answering.
- Terminal and coding agents.
- Writing documents and slides.
Choose Gemini 3.8 Flash if
- You need to analyse audio or video.
- Large batch jobs, where total throughput matters more than the first word.
- Finance and legal agent work, where Google reports wins over Opus 5.
Side by side
| Claude Sonnet 5.5 | Gemini 3.8 Flash | |
|---|---|---|
| Made by | Anthropic | |
| Released | September 2026 | September 2026 |
| Input price, per 1M tokens | ₹201.88 | ₹75.71 |
| Output price, per 1M tokens | ₹1,009.40 | ₹378.53 |
| Cached input, per 1M tokens | ₹20.19 | ₹7.57 |
| Batch (about half price) | Yes | Yes |
| Context window | 1M tokens | 1.05M tokens |
| Longest answer | 128K tokens | 66K tokens |
| Reads | Text, Images, PDFs | 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.
Gemini 3.8 Flash does the same job for 2.7× 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="claude-sonnet-5-5", …) 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.