Price, context and performance head to head. Data current as of April 2026.
Cheaper
MiniMax M2.7
Larger context
MiniMax M2.7
Faster
Sonar
Higher quality
Tied
| Feature | MiniMax M2.7 | Sonar |
|---|---|---|
| Provider | MiniMax | Perplexity |
| Tier | Mid-tier | Mid-tier |
| Input per 1M tokens | $0.3 | $1 |
| Output per 1M tokens | $1.2 | $1 |
| Cached input per 1M | $0.06 | $0.1 |
| Context window | 205K | 127K |
| Speed | Standard | Fast |
| Vision (image input) | Yes | No |
| Function calling | Yes | No |
| Batch API | No | No |
Enter how many requests per day you send with an average prompt (1K input + 1K output) and compare the monthly cost of both models.
MiniMax M2.7 saves $1.5/mo vs Sonar
Want us to build it for you?
We integrate MiniMax M2.7 or Sonar into your product with caching, observability and continuous evaluation — typically 40-80% cheaper than the obvious first pick.
Other combinations developers frequently compare in 2026.
What people ask us when comparing GPT, Claude, Gemini and the rest.
A token is the unit an AI model processes: usually between half a word and a full word. Rule of thumb: 1,000 tokens ≈ 750 English words. A 20-word sentence is about 26 tokens; a 300-word email is around 400. Models charge for input tokens (your prompt) and output tokens (their answer) separately.
