Open-weight MoE model posting frontier agentic-coding scores at Flash pricing, with native vision and 1M context.
Input price
Output price
Performance
Why V4.1 Flash Punches Above Its Active Parameter Count
Benchmarks
Independent Reads: Fast, Capable, and Notably Verbose
Technical Specifications
What the model supports
Comparison
DeepSeek V4.1 Flash vs DeepSeek V4 Pro: Which One Should You Run?
These two are the realistic decision within DeepSeek's lineup. V4 Pro is the larger 1.6T-parameter (49B active) flagship; V4.1 Flash is the smaller, newer model built on the V4.1 Causal Encoder-Decoder architecture. Both share a 1M-token context and MIT-licensed open weights. DeepSeek reports that V4.1 Flash surpasses V4 Pro on most agentic benchmarks it publishes, while costing less, running faster, and completing tasks in less total runtime. DeepSeek briefly planned to route V4 Pro traffic to Flash before reversing that decision, so V4 Pro remains available at its own rates.
Choose DeepSeek V4.1 Flash when your workload is agentic coding at volume, when you need native image input, or when throughput and low per-call cost matter more than raw world knowledge. Choose DeepSeek V4 Pro when factuality and broad knowledge are critical: V4 Pro scores materially higher on knowledge tasks like SimpleQA, and V4.1 Flash's world knowledge regressed relative to V4 Pro. If your agents lean on recall of facts rather than tool-driven coding, Pro is the safer default until V4.1 Pro ships.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Agentic coding at volume
This is the model's strongest fit. Leading provider-reported Terminal-Bench 2.1 (90.6) and DeepSWE v1.1 (74.2%) scores, combined with high throughput and low per-call cost from the 8B/16B active design, make V4.1 Flash well suited to coding agents that iterate over files and tools across many turns. The compressed KV cache reduces cache-hit charges that dominate long agent sessions. Validate the harness-specific scores under your own scaffold first.
High-throughput tool-calling pipelines
With native tool calling, JSON output and ~207 tokens/second measured output speed, V4.1 Flash fits automation and orchestration loops that call functions repeatedly. AutomationBench results (54.8) reported by DeepSeek support agentic tool use. The economics favor systems that fire many small requests rather than a few large ones; watch verbosity so long reasoning traces don't inflate cost per completed action.
Long-context repository and document work
The 1M-token context lets V4.1 Flash ingest whole-repository traces or large document sets in a single prompt, and the compressed KV cache keeps that practical. Native image input additionally supports document-with-figures and chart understanding (Chartography 78.9, DocVQA 95.6 reported). Because maximum output is separately capped around 384K tokens, structure very long generations accordingly.
Multimodal developer workflows
V4.1 Flash is the first GA DeepSeek Flash model with native vision, removing the need for a separate vision endpoint. It can interleave images and text for UI-to-code, chart/diagram interpretation, and screenshot-driven agent tasks. It only accepts images as input and outputs text, so it does not replace an image generation model. Verify visual-task accuracy for your domain, as vision benchmarks vary by scaffold.