Multiverse says its 438B model is fast enough for AI agents. The benchmarks tell a more complicated story.
A 438-billion-parameter reasoning model isn’t an obvious choice when speed is a priority. Multiverse Computing is betting that compression can The post Multiverse says its 438B model is fast enough for AI agents. The benchmarks tell a more complicated story. appeared first on The New Stack .
Multiverse Computing has unveiled Quasar 438B, its first large-scale 438-billion-parameter reasoning model tailored for coding and enterprise agents. The model claims a score of 43 on Artificial Analysis' Intelligence Index and 69.3 on Terminal-Bench v2.1, outperforming competitors like Mistral Medium 3.5 and NVIDIA Nemotron 3 Ultra.
Quasar boasts a 1-million-token context window, offered in English and Spanish, accessible via the CompactifAI API. Compression technology reduces model size by 80-95% with minimal accuracy loss, yet Multiverse hasn't disclosed the specifics of Quasar's compression or starting point. The model's performance shows trade-offs, with a Terminal-Bench v2.1 score of 69.3 placing it ahead of Mistral Medium 3.5 but behind the best frontier systems, while Claude Opus 5 leads with a score of 89.1.
Multiverse claims Quasar starts responding in 1.1 seconds and produces a 500-token response in 15.3 seconds, but agent tooling and repeated model calls may affect overall latency. Quasar is a proprietary model available only through Multiverse's API, making it hard to assess its real-world performance. As European AI companies develop their own models and infrastructure, Multiverse's compression approach presents an intriguing middle ground, but further benchmarks are needed to validate its speed claims.
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