MiniMax M3 represents a breakthrough in open-source AI by combining elite coding capabilities (59% on SWE-Bench Pro, 74.2% on MCP Atlas), a 1-million token context window, and native multimodal functionality, while introducing MiniMax Sparse Attention (MSA) that processes large-scale data up to 15 times faster at reduced computational cost by bypassing hardware bottlenecks.
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MiniMax M3: The Open-Source Model That Beats Closed AI?Added:
The open source AI community just got its hands on something massive and closed source models should be worried.
MiniMax just officially announced the MiniMax M3 making it the first open weights model to combine true elite coding, a massive 1 million token context window and native multimodal power right out of the box. Looking at the benchmarks shared in the official post, the coding metrics are staggering.
It scored 59% on SWE bench pro and a massive 74.2% on the MCP Atlas benchmark putting its agentic reasoning right on par with top-tier commercial models. But, the real breakthrough is how it handles memory. Instead of using brute force compute that slows down your machine, M3 introduces MiniMax sparse attention. It bypasses hardware bottlenecks to process entire code bases or hours of video up to 15 times faster at a fraction of the traditional cost. To get value from this right now, you don't even have to wait for the open weights download [music] in 10 days. You can access the live API at platform.minimax.io or start building custom developer agents right away at code.minimax.io.
>> [music] >> We just went from relying on expensive corporate APIs to running a top-tier Hollywood-ready video and coding engine completely locally. This changes the math for open source AI development forever. For more quick updates like this, follow or subscribe to my channel.
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