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 FlashinclusionAI/Ling-flash-2.0DeepSeek V4 Flash 0423 is DeepSeek's efficiency-optimized Mixture-of-Experts text model, designed for fast inference and workflows requiring tool use, function calling, and structured outputs. It features 284B total parameters with 13B activated parameters and supports a 1M-token context window with cost-effective pricing.
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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LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.
Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.
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inclusionAI/Ling-flash-2.0: $0.544/M output tokens; DeepSeek V4 Flash: $0.928/M. Use the cost calculator above to estimate your own workload.
DeepSeek V4 Flash: 1452; inclusionAI/Ling-flash-2.0: 1391 (LMArena coding leaderboard).
DeepSeek V4 Flash accepts text input and supports 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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