Model detail
deepseek-flash
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental [V4 Flash Vision Exp] It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds [V4 Pro] on performance, speed, and task completion time.
Model specs
- Context length
- –
- Max output
- –
- I/O modalities
- –
- Released
- 2026-09
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API endpoints
One MixRoute gateway, OpenAI-compatible
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OpenAI-compatible
/v1/chat/completionsPOST
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Pricing
Flat rate, unit: /1M Tokens
Input
$0.3000 /1M Tokens
Completion
$1.2000 /1M Tokens
Cache read
$0.0060 /1M Tokens
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Token cost estimator
Live estimate from this page's pricing, not an actual bill
Share of the same prefix read repeatedly, up to 100%
One endpoint, a testable decision
Test this model and alternate routes with the same request format
Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.