DeepSeek V4 Flash 0731 uses a 200B-parameter MoE architecture with 20B active parameters, achieving 54% on ARC-AGI – the best open-source score?

DeepSeek V4 Flash 0731 uses a 200B-parameter MoE architecture with 20B active parameters, achieving 54% on ARC-AGI – the best open-source score

Inference cost is $0.50/M input and $2.00/M output tokens, roughly 10x cheaper than GPT-4o for comparable reasoning performance?

Inference cost is $0.50/M input and $2.00/M output tokens, roughly 10x cheaper than GPT-4o for comparable reasoning performance

Supports a 128K context window and includes a new attention sparsity pattern that reduces memory usage by 30%

Open Source

r/LocalLLaMA August 08, 2026

⚡DeepSeek's new 200B-parameter MoE model hits 54% ARC-AGI with 10x lower inference cost.

Deep Dive

This source is a Reddit submission by user johnnyApplePRNG, consisting of a link and a comments section.

Key Points

DeepSeek V4 Flash 0731 uses a 200B-parameter MoE architecture with 20B active parameters, achieving 54% on ARC-AGI – the best open-source score
Inference cost is $0.50/M input and $2.00/M output tokens, roughly 10x cheaper than GPT-4o for comparable reasoning performance
Supports a 128K context window and includes a new attention sparsity pattern that reduces memory usage by 30%

Open-source AI is closing the reasoning gap: near-frontier performance at 10x lower cost is a game-changer.