Originally published on AI Tech Connect.
What you need to know One checkpoint, three capabilities. M3 is the first open-weight model to put frontier coding, a 1M-token context and native multimodality together in a single release — not three separate models. Coding parity with a closed flagship. 59.0% on SWE-Bench Pro (on par with GPT-5.5) and 66.0% on Terminal-Bench 2.1. It tops the open-weight SWE-Bench Pro leaderboard. The 1M context is affordable, not a stunt. MiniMax Sparse Attention (MSA) cuts per-token compute to roughly 1/20 of the previous generation at 1M context, with about 9x faster prefill and 15x faster decode. Pricing is the headline. $0.60 input / $2.40 output per million tokens — roughly 5 to 10 percent of closed-source models in its class. A launch promotion halved that to $0.30 / $1.20. It is open weights. You…
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