Compare Kimi K3 and DeepSeek V4

Kimi K3 and DeepSeek V4 Flash have been added as built-in Braintrust models alongside GLM-5.2, allowing users to test them in playgrounds, prompts, scorers, and deployments without separate inference providers or API keys. In an evaluation using 327 MathTutorBench tutoring dialogs, GLM-5.2 achieved the highest teaching-quality score of 0.680, DeepSeek V4 Flash scored 0.653 while delivering the fastest first visible token at 0.57 seconds with reasoning disabled, and Kimi K3 scored 0.589 while using the fewest completion tokens at a median of 48. An example tutoring task showed GLM-5.2 correcting a student’s unsupported assumption, while DeepSeek V4 Flash reached the correct equation but initially reinforced the error and Kimi K3 retained the mistaken framing. Reasoning effort affected Kimi K3’s quality most substantially, whereas enabling reasoning also increased response latency for DeepSeek and GLM. The comparison recommends evaluating models against representative proprietary data and production needs, with GLM-5.2 positioned for quality, DeepSeek V4 Flash for speed, and Kimi K3 for lower token use; both new models can be accessed through Braintrust’s interface or gateway APIs.

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