Kimi K3 vs Claude Sonnet 5.5
The open-versus-closed question at the upper middle. Kimi K3 is a 2.8-trillion-parameter open model that leads on web research; Sonnet 5.5 is a faster, newer closed model that is better at everyday coding and documents.
Choose Kimi K3 if
- Research agents that browse many sources.
- Video as input.
- An open-weight model you could later run yourself.
Choose Claude Sonnet 5.5 if
- Speed — Kimi always thinks, Sonnet can be dialled back.
- Everyday coding and command-line agents.
- PDFs, slides and spreadsheets.
Side by side
| Kimi K3 | Claude Sonnet 5.5 | |
|---|---|---|
| Made by | Moonshot AI | Anthropic |
| Released | July 2026 | September 2026 |
| Input price, per 1M tokens | ₹363.09 | ₹201.88 |
| Output price, per 1M tokens | ₹1,815.45 | ₹1,009.40 |
| Cached input, per 1M tokens | ₹34.40 | ₹20.19 |
| Batch (about half price) | No | Yes |
| Context window | 1.05M tokens | 1M tokens |
| Longest answer | — | 128K tokens |
| Reads | Text, Images, Video | Text, Images, PDFs |
| Thinks before answering | Always on | Yes, adjustable |
| Open weights | Yes, Kimi K3 License | 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.
Claude Sonnet 5.5 does the same job for 1.8× 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="kimi-k3", …) client.chat.completions.create(model="claude-sonnet-5-5", …)
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.