DeepSeek-V3.2-Exp is an experimental model version, serving as an intermediate step toward the next-generation architecture. Built on the foundation of V3.1-Terminus, it introduces the DeepSeek Sparse Attention (DSA) mechanism—a sparse attention mechanism designed to explore and validate the optimization of training and inference efficiency in long-context scenarios. This experimental version represents the team's continuous research on more efficient Transformer architectures, with a specific focus on improving computational efficiency when processing long text sequences. For the first time, DSA enables fine-grained sparse attention, which significantly enhances the efficiency of long-context training and inference while maintaining almost unchanged model output quality.
Providers
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Uptime
24hoursDirect request success rate on AI Gateway and per-provider.
Throughput
24hoursP50 throughput on live AI Gateway traffic, in tokens per second (TPS).
Latency
24hoursP50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds.
Activity
Token volume and request traffic to this model over time.
Token volume broken down by the provider that served each request. Larger segments indicate providers handling more of this model's traffic.
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis
Artificial Analysis
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for. View All
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