Moonshot AI's Kimi K3.1 Pro offers a massive 1M-token context and competitive performance at lower costs, challenging top proprietary models while promising open weights by late 2026.
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Moonshot AI has just released Kimi K3.1 Pro, the first model in its K3.1 series. Kimi K3.1 Pro is a 3T-class open-weight* model with a 1-million-token context window. Moonshot team positions it against the strongest proprietary models on coding, agentic and knowledge benchmarks. For developers, it is cheaper API pricing than K3 and open weights yet not available. *open weights are expected to release in November 2026. TL;DR Size: 2.8T total, 104B active parameters (MoE), 1M-token context. Runs on: Kimi API today. For K3, Moonshot recommends supernodes of 64+ accelerators, with MXFP4 weights and MXFP8 activations. Not a single-GPU model. Performance: Near the proprietary frontier on terminal coding; behind it on scientific terminal tasks. Best: SWE-Marathon 51.7, ahead of every model in Kimi’s chart (self-reported). Worst: Terminal-Bench Science 0.1 at 38.1, versus 68.1 for GPT-6 Astra in Kimi’s chart. What is Kimi K3.1 Pro? Kimi K3.1 Pro is a post-trained version of Kimi K3 with much larger post-training compute. Moonshot team ran joint reinforcement learning across hundreds of downstream domains. To support this, the Moonshot team optimized the RL framework, long-context inference, sandbox infrastructure and rollout scheduling. The base K3 architecture uses Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). It activates 16 of 896 experts through a Stable LatentMoE framework. The model accepts text, images and video through the API, per the quickstart docs. Moonshot team also shipped Kimi K3.1 Pro Highspeed. According to the official Moonshot blog, it runs 2 to 3x faster. How does Kimi K3.1 Pro perform on benchmarks? All numbers below are self-reported by Moonshot at max reasoning effort, mostly with the Claude Code harness. Coding: On Terminal-Bench 4.0, K3.1 Pro scores 65.7%. That is just behind Claude Opus 5.5 at 66.4% in Kimi’s chart. On SWE-Marathon it leads at 51.7, ahead of Opus 5.5 at 46.3. On Kimi Code Bench 3.0 Long-horizon, an int
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