Mercury

Mercury by Inception excels in the Coding category, where it ranks #161 with a composite score of 47/100. 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. It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Mercury is priced at $0.25 per million input tokens and $1.00 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Mercury holds rank #161 out of 6 models tracked. Its quality rank is #161 and adoption rank is #161. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Mercury has been evaluated across 6 different signals. Its strongest areas include Capabilities (43/100), Context Window (81/100), Output Capacity (70/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 47/100 reflects a weighted combination of all tracked signals.

Mercury is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check Inception's website for current free tier availability and promotional offers.