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Kimi K3
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Kimi K3 by Moonshot now available on Modal
Moonshot released Kimi K3, a 2.8 trillion parameter multimodal mixture-of-experts transformer with a 1 million token context window and native vision, now on Modal. Modal runs the model at 460 tokens per second on release day through a partnership with Moonshot and vLLM, with token-based pricing on a Shared API or dedicated Auto Endpoint. K3 activates 16 of 896 experts per token and uses Kimi Delta Attention along with Attention Residuals to achieve roughly 2.5 times the scaling efficiency of its predecessor K2. Moonshot employed quantization-aware training from the SFT stage with MXFP4 weights and MXFP8 activations, and contributed a new prefix caching implementation to vLLM to address challenges from KDA. Modal deployed a custom DFlash speculative decoding drafter tuned to K3’s architecture, yielding 360 percent faster interactivity and 88 percent higher throughput on agentic workloads.
Kimi K3 vs GPT-5.6 Sol on DeepSWE: Cost, Coding, and Routing
GPT-5.6 Sol and Kimi K3 were compared on the DeepSWE software engineering benchmark using 904 graded rollouts across 113 tasks. Sol wins single-shot pass@1 (72.7% vs 68.5%) and reliability (84.5% tasks solved four-for-four versus 76.6%), while Kimi K3 leads pass@4 (89.4% vs 85.8%) with wider coverage at a substantially lower cost—$4.65 per rollout versus Sol’s $8.37, delivering 14.7 solves per $100 against Sol’s 5.3. The two models have a low task correlation of 0.46 and disagree on 18 tasks, enabling a cascade routing strategy that runs Kimi first and escalates to Sol when the test suite rejects the result, achieving 85.6% accuracy, beating both models individually and a perfect one-shot oracle router. Kimi K3 is open-weight and available on Together AI.
Kimi K3 vs Claude Fable 5 on DeepSWE: Cost and Coding
On DeepSWE, Kimi K3 trails Claude Fable 5 by 1.4 points at pass@1 (68.5% vs 69.9%) but leads at pass@2 (82.0 vs 80.2) and pass@4 (89.4% vs 88.5%) across 452 rollouts. Kimi K3 costs $4.65 per rollout versus $13.41 for Fable, delivering 2.8x more solved tasks per dollar. It covers 89.4% of the benchmark, solving 101 of 113 tasks, while Fable solves 100, though Fable is more reliable with 58 tasks solved four-for-four versus Kimi’s 45. The models share the highest cross-vendor similarity (0.72) and fail on the same tasks, with Kimi leading in Go and Fable leading in Python, JavaScript, TypeScript, and Rust. As an open-weight model, Kimi K3 offers a cost-effective alternative for retry-tolerant workloads.
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