Inception: Mercury Coder
Mercury Coder from Inception is a coding-focused model with tool-use support and a 128K context window, but no benchmark data is available to assess its actual performance. It may be worth exploring for lightweight coding assistance, but businesses should validate output quality before relying on it for critical tasks.
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 Coder 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 Claude 3.5 Haiku and GPT-4o Mini while matching their performance. Mercury Coder's speed means that developers can stay in the flow while coding, enjoying rapid chat-based iteration and responsive code completion suggestions. On Copilot Arena, Mercury Coder ranks 1st in speed and ties for 2nd in quality.
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 |
