Compare DeepSeek V4 Flash from DeepSeek and inclusionAI/Ring-1T 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/Ring-1TDeepSeek 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.
Ring-1T is an open-source idea model with a trillion parameters released by the Bailing team. It is based on the Ling 2.0 architecture and the Ling-1T-base foundational model for training, with a total parameter count of 1 trillion, an active parameter count of 50 billion, and supports up to a 128K context window. The model is trained via large-scale verifiable reward reinforcement learning (RLVR), combined with the self-developed Icepop reinforcement learning stabilization method and the efficient ASystem reinforcement learning system, significantly improving the model’s deep reasoning and natural language reasoning capabilities. Ring-1T achieves leading performance among open-source models on high-difficulty reasoning benchmarks such as mathematics competitions (e.g., IMO 2025), code generation (e.g., ICPC World Finals 2025), and logical reasoning.
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DeepSeek V4 Flash: $0.928/M output tokens; inclusionAI/Ring-1T: $2.192/M. Use the cost calculator above to estimate your own workload.
DeepSeek V4 Flash accepts text input and supports tool calling, function calling and structured outputs; inclusionAI/Ring-1T accepts text input and supports thinking, tool calling, function calling and structured outputs.
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