Compare DeepSeek V4 Flash from DeepSeek 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.
DeepSeek V4 Flash
inclusionAI/Ling-flash-2.0(This model currently points to the older 0423 version; if you need to request the latest version, you can choose the model deepseek-v4-flash-0731)DeepSeek-V4 features an ultra-long context of one million characters and achieves leading performance domestically and in the open-source domain in agent capabilities, world knowledge, and reasoning.
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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DeepSeek V4 Flash: $0.284/M output tokens; inclusionAI/Ling-flash-2.0: $0.544/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.
DeepSeek V4 Flash at 66.5 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.
DeepSeek V4 Flash accepts text input and supports tool calling, function calling, structured outputs and thinking; inclusionAI/Ling-flash-2.0 accepts text input and supports tool calling, function calling and structured outputs.
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