moonshotai/Kimi-K2-Thinking
Kimi K2 Thinking is the first open-weights model to achieve SOTA performance against leading closed-source models (GPT-5, Claude 4.5 Sonnet) across major benchmarks including HLE (44.9%), BrowseComp (60.2%), and SWE-Bench Verified (71.3%). Built on a 1T parameter MoE architecture with 32B active per token and native INT4 quantization via QAT, it maintains stable tool-use across 200–300 sequential calls within a 256K context window.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| stream | boolean | true | Enable streaming responses for real-time output. |
| temperature | number | 1 | Recommended temperature is 1.0 for Kimi-K2-Thinking. |
| max_tokens | number | 16384 | Maximum number of tokens to generate. |
| top_p | number | 0.95 | Controls nucleus sampling. |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| First open-source model to beat closed frontier models (HLE, BrowseComp, SWE-bench) 1T MoE with only 32B active per token Native INT4 via QAT — 2x speed vs FP8 Interleaved chain-of-thought with dynamic tool calling Stable across 200-300 sequential tool calls 256K context window | Requires 512GB+ RAM for full deployment ~600GB model size (large infrastructure needed) Thinking mode means higher latency than non-reasoning models Temperature must be set to 1.0 for recommended performance |
Use cases
Recommended applications for this model
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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