What is DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash is an open-weight multimodal Mixture-of-Experts model for coding agents, tool use, long-context work and image understanding. DeepSeek released it on September 10, 2026 as the smaller model in a new architecture family designed to reduce inference and cache costs.
DeepSeek V4.1 Flash supports both thinking and non-thinking modes. Through the hosted API, reasoning effort can be set to low, high or max, so production quality, speed and cost depend on the chosen effort level, harness and tools.
QUICK VERDICT: DeepSeek V4.1 Flash is compelling when teams need open weights, million-token context, vision and low API prices in one model. Its efficiency-focused architecture is unusual, but self-hosting a 552-billion-parameter mixture model still requires serious infrastructure and operational expertise.
Field | Verified value |
|---|---|
Provider | DeepSeek |
Release date | September 10, 2026 |
License | MIT License for the released model weights and repository |
API model name | deepseek-flash |
Architecture | 552B-parameter multimodal Mixture-of-Experts with a Causal Encoder-Decoder design |
Active parameters | 8B during input processing and 16B during output generation |
Context window | 1 million tokens |
Maximum output | 384,000 tokens through the DeepSeek API |
Modalities | Text and image input; text output |
Reasoning controls | Non-thinking mode or thinking effort from low to max through the API |
Best fit | Coding agents, long-context workflows, vision tasks and cost-sensitive inference |
Last verified | September 24, 2026 |
What changed from DeepSeek V4 Flash
V4.1 Flash is not only a faster serving profile. DeepSeek changed the architecture, cache design, multimodal training and post-training data pipeline. The model activates fewer parameters during input than during output, which targets workloads that read large contexts and generate comparatively shorter answers.
Area | DeepSeek V4.1 Flash change |
|---|---|
Architecture | A 20-layer causal encoder followed by a 20-layer decoder |
Parameter activation | 8B active parameters per input token and 16B per generated token |
KV cache | About one quarter of the HBM and one eighth of the SSD footprint of V4 Flash |
Multimodality | Native image and text processing through a DeepSeek vision encoder |
Reasoning effort | Continuously controllable in the released model and exposed as low, high and max through the hosted API |
Serving aliases | Legacy V4 Flash and V4 Flash Vision experimental names temporarily route to V4.1 Flash |
The official API name is deepseek-flash. DeepSeek retired the previous V4 Flash and V4 Flash Vision experimental models, although their legacy identifiers remain temporarily accepted. Teams that require reproducibility should record the resolved model version rather than relying indefinitely on a moving alias.
Architecture and efficiency
The released model has 552 billion backbone parameters, but its sparse design does not activate all of them for every token. DeepSeek reports 8 billion active parameters during prefill and 16 billion during decoding. This asymmetry is intended to make input-heavy agent workflows cheaper while preserving more capacity for generation.
Its Causal Encoder-Decoder design projects the decoder cache from the encoder’s final hidden states. Compressed Sparse Attention 2 assigns attention layers to full, reindex or reuse modes, while a hierarchical indexer limits later search to a smaller candidate set. DeepSeek also uses FP4 cache storage, Single-Pass mHC residual mixing, Engram conditional memory and speculative decoding.
Design element | Why it matters |
|---|---|
Causal Encoder-Decoder | Separates input processing from generation and reduces repeated decoder cache work |
Compressed Sparse Attention 2 | Shares cache and sparse-attention decisions across selected layers |
FP4 KV cache | Reduces memory per token for long-context serving |
Engram memory | Adds sparsely accessed token-based memory parameters |
DSpark speculative decoding | Drafts tokens and verifies them to improve generation speed |
Native vision encoder | Processes images jointly with text from the start of language-model pre-training |
Architecture claims describe potential efficiency, not guaranteed end-to-end savings. Actual throughput depends on quantization, hardware, parallelism, batch size, context length, software support and how often an agent reuses cached context.
Capabilities and benchmark evidence
DeepSeek positions V4.1 Flash for agentic coding, tool use, reasoning and multimodal work. The model card reports maximum-effort results of 74.2% resolved on DeepSWE v1.1, 90.6% on Terminal-Bench 2.1, 88.1% on CyberGym, 54.8% on AutomationBench and 31.8% on Agent’s Last Exam. It also reports 78.9% on Chartography with tools and 89.6% on BabyVision with tools.
These results come from DeepSeek’s published evaluation and use specific scaffolds, context limits, sampling settings and maximum reasoning effort. They help identify intended strengths, but they are not a substitute for an independent production test.
Evidence | Reported result | Interpretation |
|---|---|---|
DeepSWE v1.1 | 74.2% resolved | Maximum-effort coding-agent result with the mini-SWE scaffold |
Terminal-Bench 2.1 | 90.6% pass@1 | Maximum-effort terminal-agent result using DeepSeek Harness Minimal |
CyberGym | 88.1% pass@1 | Security benchmark result that does not authorize unsupervised offensive use |
AutomationBench | 54.8% pass@1 | Tool-using workflow result under the benchmark’s official scaffold |
Chartography with tools | 78.9% pass@1 | Visual chart reasoning with a tool-enabled harness |
Pricing and access
DeepSeek offers V4.1 Flash through an OpenAI-compatible endpoint and an Anthropic-format endpoint. The hosted service uses peak and off-peak rates. Off-peak pricing is half of peak pricing, and DeepSeek defines separate rates for cache hits, cache misses and output tokens.
Usage per 1M tokens | Off-peak | Peak |
|---|---|---|
Input, cache hit | $0.003 | $0.006 |
Input, cache miss | $0.15 | $0.30 |
Output | $0.60 | $1.20 |
Peak hours are 01:00 to 04:00 UTC and 06:00 to 10:00 UTC on weekdays, excluding Chinese public holidays. All other hours are off-peak under the pricing page checked for this guide. Pricing can change, so confirm the current schedule before budgeting.
Open weights add another route: organizations can host the model themselves under the MIT License. That provides deployment control but transfers infrastructure, security, availability, optimization and update responsibility to the operator. DeepSeek’s own announcement frames large-scale deployment around thousands of GPUs plus a storage cluster, so “open weights” should not be confused with inexpensive local use.
Limitations and deployment risks
- The 552B-parameter model is costly to host even though only a subset of parameters is active per token.
- A one-million-token context window does not guarantee perfect retrieval, instruction retention or citation accuracy.
- Maximum-effort benchmark results can cost more and run slower than typical API settings.
- The hosted deepseek-flash name is an alias, so applications should monitor version changes and rerun regressions.
- Vision support expands the attack surface to malicious instructions embedded in screenshots, documents and interfaces.
- Open weights shift patching, access control, abuse prevention and safety evaluation to the deployer.
- Provider benchmark tables and model-card claims need independent validation on real workloads.
Use least-privilege tools, sandbox code execution, separate untrusted content from system instructions and require approval before destructive or externally visible actions. High-stakes legal, medical, financial, security and scientific outputs need qualified review.
How to evaluate DeepSeek V4.1 Flash
Build a frozen set of 20 to 50 tasks from the intended workload. Include ordinary cases, long-context retrieval, image inputs, tool failures, adversarial documents and tasks where the correct response is to stop. Compare the hosted API and self-hosted route only if the same model version, reasoning effort and acceptance rubric can be maintained.
Test area | Record |
|---|---|
Task completion | Pass or fail plus a written quality rubric |
Reliability | Repeated-run success rate and variance |
Long context | Retrieval accuracy and instruction retention at realistic lengths |
Vision | OCR, chart understanding, spatial reasoning and embedded prompt-injection behavior |
Tool use | Wrong calls, retries, recovery and approval-boundary failures |
Efficiency | Wall time, cache hits, input, output, hardware and review cost |
Operations | Memory footprint, throughput, availability, upgrades and incident response |
Choose the least expensive configuration that clears the acceptance threshold with a safety margin. Re-run the suite after changing the model version, reasoning effort, prompt format, inference stack, retrieval layer or tool definitions.
Frequently asked questions
Is DeepSeek V4.1 Flash open source?
Its released repository and model weights use the MIT License, so “open weights” is the most precise label. The hosted API, training data and complete production stack are not all reproduced merely by downloading the weights.
What is the DeepSeek V4.1 Flash API model name?
Use deepseek-flash. Older deepseek-v4-flash and deepseek-v4-flash-vision-exp identifiers are temporarily accepted but route to V4.1 Flash.
Does DeepSeek V4.1 Flash support images?
Yes. The model natively accepts text and image input and produces text output. Evaluate charts, screenshots, documents and adversarial image content separately.
How much does DeepSeek V4.1 Flash cost?
At the time of verification, hosted peak rates were $0.006 per million cached input tokens, $0.30 per million uncached input tokens and $1.20 per million output tokens. Off-peak rates were half those amounts.
Is DeepSeek V4.1 Flash the best AI model?
No model is best for every workload. DeepSeek V4.1 Flash’s open weights, long context, native vision and low hosted prices make it worth testing, but the right choice depends on quality, latency, deployment constraints, safety controls and total accepted-task cost.
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Official sources and update policy
DeepSeek V4.1 Flash announcement.
DeepSeek V4.1 Flash API release notes.
DeepSeek V4.1 Flash model card and weights.
Checked September 24, 2026. We update this guide when DeepSeek changes the model version, specifications, pricing, license, access or publishes material benchmark and deployment updates. Provider benchmarks are attributed and are not presented as independent testing. No vendor payment or affiliate relationship determined inclusion.