Compare GLM 5.2 from Z.AI and inclusionAI/Ling-flash-2.0 from InclusionAI on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.
GLM 5.2
inclusionAI/Ling-flash-2.0GLM-5.2 is Z.ai’s flagship model for the era of long-horizon tasks. With a truly usable 1M-token context window, it can handle project-level engineering context, execute long-running tasks more reliably, follow engineering standards more consistently, and complete the full development workflow from requirements to multi-platform deployment in a single task.
Ling-flash-2.0 is a language model from inclusionAI with a total of 100 billion parameters, of which 6.1 billion are activated per token (4.8 billion non-embedding). As part of the Ling 2.0 architecture series, it is designed as a lightweight yet powerful Mixture-of-Experts (MoE) model. It aims to deliver performance comparable to or even exceeding that of 40B-level dense models and other larger MoE models, but with a significantly smaller active parameter count. The model represents a strategy focused on achieving high performance and efficiency through extreme architectural design and training methods.
Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.
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Daily traffic served through AIHubMix — how demand for each model is trending.
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inclusionAI/Ling-flash-2.0: $0.544/M output tokens; GLM 5.2: $2.524/M. Use the cost calculator above to estimate your own workload.
inclusionAI/Ling-flash-2.0: 0.5s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.
GLM 5.2 at 35.9 tok/s and inclusionAI/Ling-flash-2.0 at 9.2 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.
GLM 5.2 accepts text input and supports thinking, tool calling, function calling and structured outputs; inclusionAI/Ling-flash-2.0 accepts text input and supports tool calling, function calling and structured outputs.
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