DeepSeek released V4.1-Flash, the smallest model of a new architecture family with native multimodal vision: a 552B backbone (763B total safetensors params including a 196B Engram conditional-memory module), 8B activated per token during prefill and 16B during decode, 1 shared + 384 routed experts with 6 active. The CED design organizes 40 layers as a 20-layer causal encoder followed by a 20-layer decoder whose KV cache is projected from final encoder hidden states; CSA2 provides three static attention modes (Full/Reindex/Reuse) with a hierarchical sparse indexer, FP4 (E2M1) main KV caching at 890 bytes/token (~1/4 of V4-Flash; SWA bounded replay cuts persistent KV to ~1/8), plus Single-Pass mHC kernels and DSpark speculative decoding. Pre-trained on 45T multimodal tokens; context extended to 1M. Benchmarks (reasoning_effort=100): Terminal-Bench 2.1 90.6, TB 3.0 30.0, TB 4.0 31.2, DeepSWE v1.1 74.2 (vs V4-Flash 54.4), GPQA Diamond 90.9, Codeforces 3471, HLE-with-tools 63.9, CyberGym 88.1, ExploitGym 15.3. MIT license, ungated, day-0 vLLM/SGLang/Docker Model Runner/Transformers support (deepseek-recipe libraries; no Jinja chat template). API: the model name is now deepseek-flash ($0.30 peak / $0.15 off-peak input, $1.20 / $0.60 output per 1M tokens; cache-hit $0.006 peak); V4-Flash and V4-Flash-Vision-Exp are retired from the API with legacy names temporarily routed to V4.1-Flash; V4 Pro API service continues after 2026-09-14 with unchanged billing; reasoning effort is a controllable integer 1-100. 75,774 downloads + 1,741 likes within ~1 day; #1 on HF trending at capture.
This is the first same-tier answer to the GLM-5.3-Flash cohort from DeepSeek, and by Terminal-Bench 2.1 (90.6 vs GLM-5.3's 88.2) it claims the top open-weight agentic-coding spot. The architecture matters as much as the scores: encoder-decoder KV projection + FP4 KV + conditional memory attacks the serving-cost side that hybrid linear attention attacks elsewhere — and the aggressive API price ($0.30/$1.20 peak) resets the cheap tier. Watch for serving friction: the deepseek-recipe runtime requirement is a new integration cost for existing stacks.
| Release | HF repo created 2026-09-10T02:17:58Z; MIT; image-text-to-text; 75,774 dl + 1,741 likes in ~1 day (captured 2026-09-11T18:34Z) |
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| Architecture | 552B backbone / 763B total safetensors (incl. 196B Engram conditional memory); 8B prefill / 16B decode activated; MoE 1 shared + 384 routed, 6 active; CED = 20-layer causal encoder + 20-layer decoder (KV projected from encoder final hidden states); CSA2 static attention modes Full/Reindex/Reuse + hierarchical sparse indexer; FP4 E2M1 main KV, 890 B/token (~1/4 V4-Flash; ~1/8 with SWA bounded replay); Single-Pass mHC; DSpark speculative decoding; DeepSeek-ViT vision (2D-RoPE, 3x3 pixel-unshuffle) |
| Training | 45T multimodal tokens pre-training; sparse attention at 64K; context extended to 1M at 34T; SFT -> RL -> on-policy distillation; reasoning_effort integer 1-100 |
| Benchmarks | TB 2.1 90.6 / TB 3.0 30.0 / TB 4.0 31.2; DeepSWE v1.1 74.2 (V4-Flash 54.4); GPQA Diamond 90.9; Codeforces 3471; HLE 36.8 (39.1 text-only); HLE w/tools 63.9; MathArena Apex 65.6; CyberGym 88.1; SEC-Bench Pro 62.8; ExploitGym 15.3; MMMU-Pro 56.5; DocVQA 95.6 |
| API | model name deepseek-flash; input $0.30 peak / $0.15 off-peak, output $1.20 / $0.60 per 1M; cache-hit $0.006 peak; concurrency 2500; max output 384K; V4-Flash + V4-Flash-Vision-Exp retired (legacy names temporarily routed); V4 Pro continues past 2026-09-14 unchanged billing |
| Serving | day-0 vLLM / SGLang / Docker Model Runner / Transformers; deepseek-recipe Rust libs + Python reference; no Jinja chat template |