DeepSeek-V4.1-Flash

What it is. DeepSeek-V4.1-Flash is DeepSeek’s current standard API model (deepseek-flash), released 2026-09-10 with MIT-licensed open weights. It is the smallest member of the V4.1 “asymmetric architecture” family: a 552B-parameter MoE that activates only 8B parameters on input and 16B on output. Why it matters. It is the model most DeepSeek API callers will actually touch, and it now carries the 1M-token context window at flash-tier pricing — long context no longer costs a premium at the entry tier. Key characteristics. 1,048,576-token context, 393,216-token output, thinking (default) and non-thinking modes, native vision, JSON output and tool calling. What a professional should know. Pricing is peak/off-peak (off-peak $0.15 input / $0.60 output per 1M tokens, peak 2x), and the benchmark scores are vendor-reported via the DeepSeek Harness, not independently re-measured.

Architecture and parameters

V4.1-Flash is the most aggressively sparse model in the database: 552B total parameters but only 8B active on input and 16B on output — a roughly 1.4% / 2.9% activation split, the only published input/output activation asymmetry in the collection. DeepSeek describes it as a Causal Encoder-Decoder MoE and claims 1/4 the HBM and 1/8 the SSD KV-cache storage of the previous generation. China AI Hub analysis: the architectural logic is cost — at $0.15 input, only a model this sparse can sustain margin. See Mixture-of-Experts and the MoE architectures research.

What the context window actually means

The 1M-token context at flash price is the notable fact here: V4.1-Flash is one of three flash-tier models (with Qwen3.8-Flash and GLM-5.3-Flash) that deliver the 1M window at the $0.15 floor. Practically this means long-document analysis, whole-repo context and extended agent traces are now available at entry pricing. The 393,216-token output is generous but still a fraction of input — treat it as long-input analysis, not long-form generation. See the context-window research.

Pricing implications

Off-peak $0.15 input / $0.60 output, cache hits at $0.003, peak 2x during the documented weekday windows (01:00–04:00 and 06:00–10:00 UTC). The 4x input-to-output ratio and near-zero cache pricing reward agent workloads that re-read a large context across many turns. This is the price point the rest of the market converged on; see pricing-changed research.

API, coding, and agent implications

V4.1-Flash is the agentic-coding workhorse of the current DeepSeek lineup: vendor-reported Terminal-Bench 2.1 (90.6), Codeforces 3471, and DeepSWE v1.1 (74.2) are strong signals for terminal, competitive-programming and repository-scale agent tasks. Native vision and tool calling make it usable in multimodal agent loops, not just text. Legacy API names deepseek-v4-flash and deepseek-v4-flash-vision-exp route here, so existing integrations continue to work. See the DeepSeek-V4.1-Flash vs GLM-5.3-Flash comparison.

Open weights and license

MIT-licensed and self-hostable. The same caveat as other large open models applies: weights exist, but running a 552B MoE (even sparse) requires substantial GPU memory, and the sparse/encoder-decoder design is non-trivial to serve efficiently outside DeepSeek’s own stack. See open weight vs API.

Benchmark interpretation

All five rows are vendor-reported. GPQA Diamond 90.9 signals strong graduate-level science QA; HLE 36.8 (39.1 on the pure-text subset) is a modest absolute score on the hardest knowledge exam; Codeforces 3471 is a competitive-programming rating, not an accuracy percentage, so it is not directly comparable to the other rows. The vendor notes these were produced with DeepSeek Harness in minimal mode at max effort.

What the benchmarks do not prove

Vendor-reported scores under a specific harness do not generalize to your workload and are not comparable to other vendors’ numbers without matching harness, benchmark version and tool setup. The HLE row illustrates the pitfall directly: a 36.8 “full” vs 39.1 “pure-text subset” gap that can be cited either way. See how to read vendor-reported benchmarks.

Suitable and less suitable workloads

Well-suited: high-volume API workloads, agentic coding, long-context analysis at low cost, multimodal (vision) tasks, and anything where cache-friendly re-reading dominates. Less suited: peak-time latency-sensitive or budget-critical jobs that cannot avoid the 2x peak window, and self-hosters without the GPU capacity for a 552B sparse model.

Where this model fits

Workload	Relevance
Long-context analysis	High
Coding	High
Structured API workflows	High
Agent orchestration	High
Local self-hosted deployment	High
GUI automation	No evidence
Video generation	No evidence
Enterprise cloud	Moderate

Relevance judgments are China AI Hub analysis based on documented capabilities, not vendor claims.

China AI Hub analysis

China AI Hub analysis indicates that V4.1-Flash is the clearest expression of the market’s price-per-token economics: a 1.4%-activation MoE that delivers frontier-adjacent coding scores at the $0.15 floor. It inherits the V4-Pro’s agentic-coding strengths while cutting active compute roughly 6x. The main risk is verification — every headline number is vendor-reported — and the peak/off-peak structure, which quietly doubles cost outside off-peak hours. As the successor to the now-deprecated V4-Pro API, it is the model DeepSeek’s platform is standardizing on.

Market position and outlook

China AI Hub analysis indicates V4.1-Flash is where DeepSeek’s price-per-token strategy becomes explicit: at 1.4% activation it is the most aggressively sparse model in the collection, and its $0.15 off-peak input price is part of a three-way convergence the database documents — DeepSeek, Alibaba (Qwen3.8-Flash) and Zhipu (GLM-5.3-Flash) all list the same $0.15 flash-tier input. What distinguishes V4.1-Flash in that trio is the combination of 1M context, native vision and a 393K output ceiling at the floor price, which is why it has become the default entry point for DeepSeek’s API. Legacy routing (deepseek-v4-flash and deepseek-v4-flash-vision-exp → V4.1-Flash) further consolidates that position. See the state of China’s AI models and context-window research for the cross-lab comparison.

What is uncertain

• Benchmark scores are vendor-reported via the DeepSeek Harness and not independently verified.
• The HLE score differs between the full set (36.8) and the pure-text subset (39.1).
• Serving regions are not disclosed.

Sources

• DeepSeek API docs — Models & Pricing
• DeepSeek API Change Log
• DeepSeek-V4.1-Flash release announcement
• Hugging Face model card — DeepSeek-V4.1-Flash

Labels used above: Official fact (from primary sources), Vendor-reported claim (benchmark scores published by DeepSeek), and China AI Hub analysis (our synthesis, always introduced as such). No third-party benchmark evidence is currently recorded for this model.