# Mercury 3

Whether diffusion language models can compete with autoregressive models on complex tasks had been an open question. Inception's Mercury 3 scales parameters more than 40x over Mercury 1 and runs at 1,068 tokens per second on NVIDIA GPUs; the company reports it matches or beats GPT-6 Luna and Claude Haiku 5.5 on several benchmarks while running 4-8x faster. In 50 live Mind2Web navigation tasks it completed 50%, well above GPT-6.1-Sol, using the same token volume and agent steps.

Whether diffusion language models can compete with autoregressive models on complex tasks had been an open question. Inception's Mercury 3 scales parameters more than 40x over Mercury 1 and runs at 1,068 tokens per second on NVIDIA GPUs; the company reports it matches or beats GPT-6 Luna and Claude Haiku 5.5 on several benchmarks while running 4-8x faster. In 50 live Mind2Web navigation tasks it completed 50%, well above GPT-6.1-Sol, using the same token volume and agent steps.

Inception's Mercury 3 is now available through AI Gateway, generating text by diffusion through parallel refinement rather than word-by-word sequential generation. Inception reports strong results across a range of benchmarks, generation speeds above 1,000 tokens per second, and a 1M-token context window. The model targets latency-sensitive workloads such as browser agents, real-time search, interactive education, and customer support, with improved reasoning.

Inception's Mercury 3 is now available through AI Gateway, generating text by diffusion through parallel refinement rather than word-by-word sequential generation. Inception reports strong results across a range of benchmarks, generation speeds above 1,000 tokens per second, and a 1M-token context window. The model targets latency-sensitive workloads such as browser agents, real-time search, interactive education, and customer support, with improved reasoning.