DeepSeek V4.1 Flash
DeepSeek V4.1 Flash is a multimodal Mixture-of-Experts (MoE) language model with 552B backbone parameters and support for contexts of up to one million tokens. It natively processes images and text and generates text. The model activates only 8B parameters per token during prefill and 16B during decode, which substantially improves cost efficiency for input-heavy agentic workloads.
Architecture
DeepSeek V4.1 Flash adopts a Causal Encoder-Decoder design: a 40-layer Transformer organized as a 20-layer causal encoder followed by a 20-layer decoder. The decoder's global KV cache is projected from the final encoder hidden states rather than computed at every decoder layer, which is where the split between prefill and decode activation comes from.
Attention uses Compressed Sparse Attention 2 (CSA2), which assigns each layer a static mode that shares the main KV and indexer keys across layers and reuses sparse-attention indices. Combined with FP4 KV caching, this brings the global KV cache to about 890 bytes per token, roughly a quarter of DeepSeek V4 Flash. Further components include Engram conditional memory (196B parameters accessed sparsely through token-based lookup), a revised single-pass residual mixing scheme, and a DSpark speculative decoding module. Each MoE layer has one shared expert and 384 routed experts, with 6 routed experts active per token.
A vision encoder trained from scratch and a two-layer projector turn images into visual embeddings, which the model processes jointly with text from the start of pre-training. The model was trained from scratch on a 45T-token multimodal corpus, with context extended to one million tokens.
Key Features
- Multimodal MoE model with 552B backbone parameters, 8B active per token in prefill and 16B in decode
- Context window of up to one million tokens with a very small KV cache footprint
- Native image understanding alongside text
- Continuously controllable reasoning effort from 1 to 100, trading inference cost for accuracy
- Native tool calling and reasoning parsing for agentic applications
- Open-source under the MIT License
Use Cases
- Coding agents and software engineering tasks
- Terminal and tool-using agents that run long multi-step tasks
- Long-document analysis and retrieval over very large inputs
- Visual question answering, chart and document understanding
- Mathematical problem-solving and step-by-step reasoning
Benchmarks
At maximum reasoning effort, DeepSeek reports 90.6 on Terminal-Bench 2.1, 74.2 resolved on DeepSWE v1.1, 64.0 on NL2Repo-Bench, 88.1 on CyberGym, 63.9 on HLE with tools, 54.8 on AutomationBench and 31.8 on Agent's Last Exam. On reasoning benchmarks it scores 90.9 on GPQA Diamond, a Codeforces rating of 3471, and 65.6 on MathArena Apex.
These agentic results are well ahead of DeepSeek V4 Flash, which scored 82.7 on Terminal-Bench 2.1 and 54.4 on DeepSWE v1.1. With image input, the model scores 78.9 on Chartography and 89.6 on BabyVision, both with tools.
For full model details and the complete evaluation tables, see the model card on Hugging Face.
