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How Kimi-K2-Thinking compares to MiniMax M2? 1/n Both support interleaved tool calling MiniMax M2 will give it a tough fight - it has made its own niche. It features a context window of 196.6K tokens. The model costs $0.15 per million tokens for input and $0.45 per million tokens for output. Comparing with below chart, MiniMax M2 is literally 75-80% cheaper compared to Kimi-K2-Thinking. But things become interesting when you use Kimi-K2-Thinking with caching & you have long context and multiple cache hits. Then Kimi K2 price for input tokens drops to MinMax M2 level.
How Kimi-K2-Thinking compares to MiniMax M2 on size? 2/n 1. MiniMax M2 has 10B active and 230B total parameters with full attention. 2. Kimi K2 has 35B active and 1 trillion total parameters. Both have most of their weights in 8bits. That mean M2 will be much easier to host and its KV cache will be much more compact. MiniMax M2 uses full attention, would be interesting to see if Kimi-M2 has done something interesting to the attention layer. (for this calculations I am assuming Kimi-K2-Thinking is based on Kimi-K2-Base)
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A fat poet (32 billion activated parameters and 1 trillion total parameters) with impressive tool calling abilities will be in the town. Kimi-K2-Thinking supports Interleaved Tool calling
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