DeepSeek-V4.1-Flash

DeepSeek-V4.1-Flash is DeepSeek's 2026-09-10 multimodal MoE. The Hugging Face card is MIT, 552B backbone, 8B activated at prefill and 16B at decode, 1M context. On 2026-09-10 the card showed about 307 likes. X For You carried Tianyi Cui's DeepSeek Harness v0.1.5 note that the model was trained against that harness. r/LocalLLaMA hot the same day linked the card (about 211 upvotes) and a WeChat-sourced write-up.

Compare DeepSeek V4 Flash if you wanted the 284B/13B predecessor, DeepSeek Harness if you wanted the agent loop it was scored in, or mlx-serve if you only needed a Mac local server.

Key Features

• Causal Encoder-Decoder: a 40-layer Transformer as a 20-layer causal encoder plus a 20-layer decoder. The card says the decoder's global KV is projected from the encoder, so prefill stays at 8B active parameters.
• CSA2 plus FP4 main KV: Compressed Sparse Attention 2 with Full/Reindex/Reuse modes. Combined with FP4 main KV caching, global KV is 890 bytes per token, about 1/4 of V4-Flash.
• Native image and text: DeepSeek-ViT plus a two-layer MLP projector, trained from scratch on 45T multimodal tokens. Context extended to 1M at 34T tokens.
• Controllable effort: integer reasoning_effort from 1 to 100. Instruct numbers below use 100.
• DSpark and Engram: speculative decoding plus 196B Engram conditional memory accessed by token lookup. MoE: 1 shared expert, 384 routed, 6 routed active.

Limitation: API list prices were not on the card on 2026-09-10. Do not copy V4-Flash's $0.14/$0.28 here. 307 likes is a same-day heat signal, not an audit. First-party agent scores use max effort, temperature=1.0, top_p=0.95.

Model Specifications

Pricing

First-party agent scores (max effort)

From the card, not a third-party lab:

• Terminal Bench 2.1: 90.6 (V4-Flash 82.7)
• DeepSWE v1.1: 74.2
• AutomationBench: 54.8
• Agent's Last Exam: 31.8

Getting Started

1. Read the model card and the bundled encoding/ README. There is no Jinja chat template in this release.
2. Serve with vLLM: vllm serve deepseek-ai/DeepSeek-V4.1-Flash, or SGLang python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V4.1-Flash.
3. For production prompt encoding, the card points at deepseek-recipe.
4. If you want the harness used in the tweet, open DeepSeek Harness.

First-party resource: the technical report PDF on the same repo.

Frequently Asked Questions

Is this an update of V4-Flash or a new model?

A new architecture. V4-Flash is 284B/13B. V4.1-Flash is 552B with 8B/16B activation and a smaller KV cache.

Can I run it on a laptop?

The card lists vLLM, SGLang, and Docker. 552B MoE is not a laptop default. Use an API or a quantized build if one appears, and confirm VRAM yourself.

Does it replace V4-Pro?

The card compares scores. It does not say V4-Pro is withdrawn. r/LocalLLaMA separately discussed a V4-Pro soft retirement; treat that as community talk until DeepSeek writes it.

Alternatives

• DeepSeek V4 Flash: the 284B/13B MIT agent model with published API prices.
• DeepSeek Harness: the agent runtime this checkpoint was trained and scored against.
• mlx-serve: local Apple Silicon serving if you are not hosting 552B.

Tips

1. Quote MIT, 552B, 8B/16B, 890 bytes/token, and 1M context from the card checked 2026-09-10.
2. Leave API dollars blank until DeepSeek publishes them.
3. Use reasoning_effort as an integer 1-100, not the old low/high/max labels from V4-Flash.

Conclusion

DeepSeek-V4.1-Flash is the 10 September 2026 MIT multimodal Flash: smaller KV, native vision, and first-party agent numbers above V4-Flash. Start at the Hugging Face card. If you needed the cheaper published API of the previous Flash, stay on DeepSeek V4 Flash.