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On October 29, 2026, Diffusion Intelligence released the diffusion language model Clover-35B-A3B-Preview. According to QbitAI, the model uses a MoE architecture with 35 billion total parameters and about 3 billion activated per token, and supports a thinking mode; in the 49 tasks of the web Agent benchmark WindTunnel, its median execution time was 1.52 seconds, lower than the 3.21 seconds of the Jev+Mercury 2.5 combination, with success rates of 98.6% and 95.9% respectively; on code benchmarks it scored 73.6 on SWE-bench Verified and 51.7 on Terminal-Bench 2.1. The team said it will subsequently launch a multimodal version and advance on-device adaptation. On October 30, IT Home · Artificial Intelligence reported that the artificial intelligence lab DiffuSpace released Clover-35B-A3B-Preview, the first model in the Clover series, which uses a MoE architecture and is open-sourced under the Apache license, generating results through multiple rounds of iteration; DiffuSpace said it outperforms the same-scale autoregressive model Qwen3.6-35B-A3B on multiple benchmarks while maintaining faster generation speed, and is especially good at agentic coding and tool-use tasks.
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DiffuSpace Open-Sources Clover-35B-A3B-Preview Diffusion Language ModelDevelopments
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Diffusion Intelligence releases diffusion language model Clover-35B-A3B-Preview
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Oct 30
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DiffuSpace Open-Sources Clover-35B-A3B-Preview Diffusion Language Model
AI lab DiffuSpace has released Clover-35B-A3B-Preview, the first model in the Clover series, built on a MoE architecture and open-sourced under the Apache license. The model generates results through multiple rounds of iterative generation; DiffuSpace claims it outperforms the same-scale autoregressive model Qwen3.6-35B-A3B on multiple benchmarks while maintaining faster generation speeds, and is particularly strong at agentic coding and tool-use tasks.
Oct 29
QbitAI
Diffusion Intelligence releases Clover-35B-A3B-Preview, a 35-billion-parameter diffusion language model
Diffusion Intelligence releases the diffusion language model Clover-35B-A3B-Preview, which uses a MoE architecture with 35 billion total parameters and about 3 billion activated per token, and supports a thinking mode. On the 49 tasks of the web Agent benchmark WindTunnel, its median execution time was 1.52 seconds, lower than the 3.21 seconds of the Jev + Mercury 2.5 combination, with success rates of 98.6% and 95.9% respectively. On code benchmarks, it scored 73.6 on SWE-bench Verified and 51.7 on Terminal-Bench 2.1, and the team said it will subsequently launch a multimodal version and advance on-device adaptation.