DeepSeek V4 Flash

Two analysts argue that DeepSeek V4.1 Flash's results across benchmark vintages are consistent with public-benchmark contamination. The model also leads Artificial Analysis's new private evaluation, complicating the assessment.

DeepSeek V4.1 Flash leads Vals and Artificial Analysis open-weight comparisons, according to the evaluators. Its encoder-decoder design shares compressed KV state across decoder layers to reduce serving costs.

DeepSeek released V4.1 Flash, a 552B-parameter multimodal MoE model with 8B input and 16B output active parameters. It supports up to 1 million tokens of context, while an independent BridgeBench run used 23.5 million tokens on one task.

OpenCode’s thdxr said Go users spent $1.14 per day on DeepSeek V4 Flash last week. Wafer added a fast OpenRouter route, while Nous extended a 90% discount for the 0731 model.

Together says two V4 Flash attempts solved more DeepSWE tasks than one GPT-5.6 Luna attempt for roughly one-third the cost. Practitioners report Flash-0731 results vary sharply by harness and pass count.

ARC Prize verified DeepSeek V4 Flash at 61.4% on ARC-AGI-2 for $0.04 per task. Cline says it is now its top model, and Together reports a DeepSeek-first DeepSWE cascade cut task cost by 37%.

Baseten and Together AI added DeepSeek V4 Flash with a 1M-token context window, reasoning-effort controls, and DSpark decoding. ValsAI ranked it the cheapest model above 60 on its index, and Nous promoted a short 90% discount.

Cline tripled its free quota while Nous discounted Flash 0731 by 90% for a week and OpenHands offered free cloud use. OpenCode reported 8T Flash tokens on Aug. 1.

New tests showed DeepSeek V4 Flash as cheaper per token and faster on some serving paths. Ramp said it cost 3x more than GPT-5.6 Luna per SWE-Bench task because it used more turns.