Ling-flash-2.0 is an open-source Mixture-of-Experts (MoE) language model developed under the Ling 2.0 architecture. It features 100 billion total parameters, with 6.1 billion activated during inference (4.8B non-embedding).
Trained on over 20 trillion tokens and refined with supervised fine-tuning and multi-stage reinforcement learning, the model demonstrates strong performance against dense models up to 40B parameters. It excels in complex reasoning, code generation, and frontend development.
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Direct request success rate on AI Gateway and per-provider.
P50 throughput on live AI Gateway traffic, in tokens per second (TPS).
P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds.
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.
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis
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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Artificial Analysis
