Kimi K3 AI Model: Price, Specs, Benchmarks and Complete 2026 Guide
Moonshot AI launched Kimi K3, one of the world's largest open AI models. It packs 2.8 trillion parameters, a 1M-token context window, native text/image/video understanding, and strong long-horizon coding and agent capabilities.
🟢 Specs: 2.8T parameters — Mixture of Experts (896 experts, 16 active per token), 1,048,576-token context, model ID kimi-k3
🟢 Pricing (per M tokens): $0.30 cache-hit input, $3.00 cache-miss input, $15.00 output — below GPT-5.6 Sol, especially with Moonshot's reported 90%+ cache-hit rate on coding workloads
🟢 Architecture: Moonshot's Delta Attention and Attention Residuals deliver ~2.5× scaling efficiency over Kimi K2, keeping inference costs manageable despite the massive parameter count
🟢 Coding benchmarks (Moonshot reported): Terminal Bench 2.1: 88.3 (vs GPT-5.6 Sol 88.8), FrontierSWE: 81.2 (vs Claude Fable 5 86.6), SWE Marathon: 42.0 (ahead of both), BrowseComp: 91.2, Automation Bench: 30.8 (highest in table). Comparisons use different agent harnesses — benchmark on your own workload for reliable results
🟢 Capabilities: Long-horizon software engineering (repo navigation, CLI tools, test debugging), visual development (inspect screenshots, modify code, review output), research and document analysis, and native multimodal processing
🟢 vs GPT-5.6 Sol & Claude Fable 5: Moonshot openly states K3 trails the top proprietary models overall. K3 wins specific benchmarks and is best suited when open weights, 1M context, lower pricing, and long-running agent tasks are priorities
🟢 UAE business relevance: Contract review, customer-service agents, app development, market research, dashboard generation. Test Arabic performance on local terminology and dialects, and review data privacy policies before production use
🟢 Limitations: Model-switching instability, thinking-history requirements for compatible agents, excessive proactiveness with ambiguous instructions, noticeable UX gap vs leading models, demanding self-hosting (64+ accelerators recommended), full weights releasing July 27, 2026
🟢 Access: Kimi.com (direct), Kimi Work 3.1.0+ (desktop), Kimi Code (/model command), or API (kimi-k3). Start with a real-world task, compare against alternatives, and decide based on accuracy, cost, and completion time rather than benchmarks alone
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