Unverified50% confidenceBenchmarkExact time
In the 3D game procedural generation task, DeepSeek 4.1 Flash ran 16 rounds of iteration and scored at most only 5.5 points (target 8.5 points), with overall intelligence at about 80% of frontier models.
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/25/2026
First Seen
Valid until: 11/8/2026
Sources
Related Entities
Related Claims
Partially VerifiedDeepSeek says V4.1-Flash-1017 uses reinforcement learning with self-feedback to drive end-to-end project work, including game creation, 3D modeling and long agent runs.74% similarUnverifiedThe DeepSeek API offers three effort levels—low, high (default) and max—which the V4.1 Flash paper maps to internal effort values of 50, 75 and 100, and reports that going from high to max lengthens agent trajectories by 1.6–1.8 times while yielding only marginal gains.74% similarUnverifiedDeepSeek V4.1 Flash offers two reasoning levels, Hide and Max, with the Max mode's thinking process being excessively lengthy.70% similarUnverifiedIn a comparison test where four models each built a small 3D game from the same prompt, only DeepSeek successfully produced a scene with thousands of glowing windows.69% similarVerifiedDeepSeek has released an experimental multimodal model, DeepSeek V4 Flash Vision EXP, which performs excellently in both text and multimodal tasks.68% similar
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