DeepSeek V4.1 Flash
DeepSeek V4.1 Flash is DeepSeek's September 10, 2026 multimodal model for text, image, coding, reasoning, and agent workflows. The hosted model ID is `deepseek-flash`; it supports 1M context, up to 384K output, thinking and non-thinking modes, tools, JSON, Responses, Anthropic-compatible Messages, and native image input. DeepSeek also publishes MIT-licensed weights and a technical report.
The release changes both model selection and compatibility. DeepSeek retired V4 Flash and V4 Flash Vision Exp, temporarily routes their legacy IDs to V4.1 Flash, and lowered Flash prices. Its Causal Encoder-Decoder architecture activates different amounts of the 552B backbone for input and output, targeting the cache and throughput costs that dominate long agent sessions.
DeepSeek's release, changelog, pricing page, model card, and technical report control identity, date, API ID, modalities, limits, prices, architecture, weights, and predecessor retirement. Benchmark claims are vendor-reported. Hacker News and Reddit show strong demand around cost, local hardware, model size, alias behavior, and real task efficiency, but anecdotal runs do not establish a universal quality or cost winner.
- Run low-cost long-context coding and agent workflows through the DeepSeek API.
- Analyze screenshots, charts, documents, and other supported images alongside text.
- Connect Responses- or Anthropic-compatible agent clients to one multimodal endpoint.
- Evaluate an MIT-licensed open model on suitably provisioned inference infrastructure.
- Migrate from the retired V4 Flash and Vision Exp endpoints with explicit regression checks.
Use `deepseek-flash` for the current hosted model. DeepSeek retired the original V4 Flash and V4 Flash Vision Exp models; their IDs remain accepted only as temporary compatibility aliases to V4.1 Flash. That means a successful request under an old name no longer reproduces the old model.
- Current hosted ID: `deepseek-flash`.
- Legacy aliases: `deepseek-v4-flash` and `deepseek-v4-flash-vision-exp`.
- Open checkpoint: `deepseek-ai/DeepSeek-V4.1-Flash` under the MIT License.
DeepSeek describes a 552B-parameter backbone using a Causal Encoder-Decoder architecture. Prefill activates about 8B parameters per input token and decode activates about 16B per output token. The company says the design uses one quarter of the previous generation's HBM for KV cache and one eighth of its SSD storage. These are architectural and vendor-measured claims, not proof of the same savings in every serving stack.
The hosted API lists 1M context, 384K maximum output, image understanding, thinking and non-thinking modes, JSON output, tool calls, Responses, Anthropic-compatible Messages, prefix completion beta, and FIM in non-thinking mode. Off peak, one million tokens cost $0.003 for cache-hit input, $0.15 for cache-miss input, and $0.60 for output; peak rates are double. Peak hours are 01:00–04:00 and 06:00–10:00 UTC on weekdays.
The model card publishes weights, a technical report, and serving guidance, but the checkpoint is still very large. DeepSeek's own deployment note invites clusters on the order of thousands of GPUs for large-scale service. Quantization and hybrid loading can change the hardware boundary, but local anecdotes should report exact format, hardware, speed, context, and quality rather than calling the model laptop-friendly.
DeepSeek reports results across science, mathematics, coding, cybersecurity, agents, vision, and tool use, including comparisons above its earlier V4 Pro. Treat those numbers as launch evidence tied to the published harnesses. Run the same repository, tools, effort, stopping rules, and acceptance checks before replacing a working route.
Hacker News and Reddit interest is unusually strong. Readers focus on the old-ID redirect, actual parameter accounting, local serving feasibility, output speed, reasoning-token use, and whether a cheaper model finishes a whole coding task. Those are useful evaluation questions; individual screenshots and one-off runs remain anecdotal.
The company operating the hosted API and publishing the model weights and research.
The retired 0731 predecessor and legacy API alias.
The retired experimental vision predecessor and legacy alias.
The Pro endpoint whose current routing notices conflict across official pages.
Source confidence
DeepSeek API Docs
DeepSeek API Docs
DeepSeek API Docs
DeepSeek on Hugging Face
Hacker News
Reddit r/DeepSeek
DeepSeek V4.1 Flash FAQ
Common questions about DeepSeek V4.1 Flash.
What is the API model ID for DeepSeek V4.1 Flash?+
Use `deepseek-flash`. The older `deepseek-v4-flash` and `deepseek-v4-flash-vision-exp` names are temporary compatibility aliases that now route to V4.1 Flash.
Does DeepSeek V4.1 Flash accept images?+
Yes. DeepSeek documents native image understanding alongside text input and text output. Verify the supported content-block format in the active API and client adapter.
Can I run DeepSeek V4.1 Flash locally?+
The weights are MIT-licensed and public, but the model is large. Local feasibility depends on quantization, memory, storage, context target, serving runtime, and acceptable speed; a successful small-hardware demo does not establish production throughput.
Is V4.1 Flash the same as the old V4 Flash?+
No. It uses a new architecture, adds native image input, has a different open checkpoint and price schedule, and replaces the older hosted Flash models. Legacy IDs route to it only for compatibility.
Is V4.1 Flash better than V4 Pro?+
DeepSeek says it outperforms V4 Pro across performance, cost, speed, and total time, but that is a provider claim. Official pages also conflict about the current V4 Pro route, so compare observed endpoints on your own accepted tasks.