Inception: Mercury
Inception Mercury supports tool use and function calling at a low price point, but with no benchmark data available its capabilities remain unverified — it may suit lightweight automation tasks but cannot be confidently recommended for professional workflows.
Assessment date: March 12, 2026
Our methodology takes into account a range of factors including pricing, functionality, capabilities, benchmark performance, and real-world applicability. Rankings are reviewed and updated regularly as new models are released. Issues with our rankings? Contact us
Mercury is the first diffusion large language model (dLLM). Applying a breakthrough discrete diffusion approach, the model runs 5-10x faster than even speed optimized models like GPT-4.1 Nano and Claude 3.5 Haiku while matching their performance. Mercury's speed enables developers to provide responsive user experiences, including with voice agents, search interfaces, and chatbots. Read more in the [blog post]
(https://www.inceptionlabs.ai/blog/introducing-mercury) here.
Capabilities
Architecture
| Modality | Text → Text |
| Tokenizer | Other |
Model Information
Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.25 | $0.000250 |
| Output | $0.75 | $0.000750 |
Live Performance
Live endpoint metrics — refreshed every 30 minutes.
