Ling-mini-2.0 is an open-source Mixture-of-Experts (MoE) large language model designed to balance strong task performance with high inference efficiency. It has 16B total parameters, with approximately 1.4B activated per token (about 789M non-embedding). Trained on over 20T tokens and refined via multi-stage supervised fine-tuning and reinforcement learning, it is reported to deliver strong results in complex reasoning and instruction following while keeping computational costs low. According to the upstream release, it reaches top-tier performance among sub-10B dense LLMs and in some cases matches or surpasses larger MoE models.
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84.6%$0.07/ M tokens$0.28/ M tokensRead:0.014/ M tokensWrite:-/ M tokens128K1.36s27.6tps
Providers
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ProviderCache Hit RateContext
TboxSunset
theta
Uptime
24hoursDirect request success rate on AI Gateway and per-provider.
May 16, 14:54
Tbox100.00%
Throughput
24hoursP50 throughput on live AI Gateway traffic, in tokens per second (TPS).
May 16, 14:00
Tbox27.6 tps
Latency
24hoursP50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds.
May 16, 14:00
Tbox1.36s
Activity
Token volume and request traffic to this model over time.
Token Consumption
May 16
Tbox8.0K
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
Token Ranking
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