DeepSeek V4.1 Flash (TensorX)

The article confirms DeepSeek-V4.1-Flash as a released Flash-series model with native multimodal visual understanding, a 1M-token context, and 384K maximum output. It targets coding, software agents, long-context reasoning, and high-throughput API workloads. The review reports DeepSeek-stated benchmarks of 90.9 on GPQA Compared with the previous V4 Flash 0731 release, Terminal-Bench 2.1 is reported to rise from 82.7 to 90.6 and DeepSWE from 54.4 to 74.2. Pricing is listed as $0.003 per million cache-hit input tokens, $0.15 per million uncached input tokens, and $0.60 per million output tokens off-peak, with peak pricing at double tho

DeepSeek V4.1 Flash was released on September 10, 2026, under the API endpoint name deepseek-flash, published with MIT-licensed weights and a technical report titled "Pushing the Limits of KV Cache Compression." The model is a 552B-parameter multimodal mixture-of-experts built on a new Causal Encoder-Decoder architectu Architectural components listed in the analysis include Compressed Sparse Attention 2 with Full/Reindex/Reuse modes, FP4 KV cache compression in E2M1 format, SWA Bounded Replay, Single-Pass mHC residuals, an Engram conditional memory of 196B parameters, DSpark speculative decoding, and native multimodal vision via Deep

The aggregator page ranks DeepSeek-V4.1-Flash 13th overall on its composite LLM Stats Score, placing it in the Top 2% for Tool Calling (2 of 194) and Top 10% for Coding (5 of 267). It scores top-half on Reasoning (18 of 363), and mid-pack on Vision (31 of 208) and Math (43 of 327). The blended price is listed at $0.24 The article independently corroborates several DeepSeek-reported benchmarks, citing HuggingFace model card sources: a CodeForces rating of 3471.00/3000, GPQA Diamond of 0.91/1, and Terminal-Bench 2.1 among the tracked benchmarks. The page provides a third-party cross-check on price-efficiency and capability tier standi

DeepSeek released V4.1 Flash, a 552 billion parameter Mixture-of-Experts model described as the smallest member of its new architecture family. According to the article, the model uses a Causal Encoder-Decoder design with only 8 billion active parameters for input and 16 billion for output, and now natively supports vi The article cites DeepSeek-reported benchmarks including GPQA Diamond 90.9, Codeforces rating 3471, Terminal-Bench 2.1 90.6, DeepSWE v1.1 74.2, CyberGym 88.1, and NL2Repo-Bench 65.4, with DeepSeek claiming these results outperform Kimi-K3, GLM-5.3, Claude Opus 5, GPT 5.6-Sol, and DeepSeek-V4-Pro. A comparative table al