Kimi K2 Instruct
Version: 1
Fireworks on Foundry
Models available for use with Fireworks on Foundry deliver optimized, best-in-class performance on the Fireworks Inference Cloud. Fireworks on Foundry is a Preview subject to Azure Preview terms and the following supplemental terms: When you use Fireworks on Foundry, data is shared between Microsoft and Fireworks AI, and different compliance and data handling rules will apply. See documentation for details. Customers are responsible for evaluating whether data sharing between Microsoft and Fireworks is appropriate for their organization’s compliance requirements.Key capabilities
About this model
Kimi K2-Instruct-0905 is an updated version of Kimi K2, a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Kimi K2-Instruct-0905 has improved coding abilities, agentic tool use, and a longer (262K) context window.Key model capabilities
- Enhanced agentic coding intelligence: Kimi K2-Instruct-0905 demonstrates significant improvements in performance on public benchmarks and real-world coding agent tasks.
- Improved frontend coding experience: Kimi K2-Instruct-0905 offers advancements in both the aesthetics and practicality of frontend programming.
- Extended context length: Kimi K2-Instruct-0905's context window has been increased from 128k to 256k tokens, providing better support for long-horizon tasks.
Use cases
See Responsible AI for additional considerations for responsible use.Key use cases
- Code assistance and agentic coding tasks
- Frontend programming
- Conversational AI
- Tool-augmented interaction
- Long-context reasoning (256K tokens)
- Enterprise-grade retrieval-augmented generation (RAG)
Out of scope use cases
The provider has not supplied this information.Pricing
Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.Technical specs
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TextOutput formats
TextSupported languages
EnglishSample JSON response
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Kimi K2-Instruct-0905 is a state-of-the-art mixture-of-experts (MoE) language model, featuring 32 billion activated parameters and a total of 1 trillion parameters.| Property | Value |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total Parameters | 1T |
| Activated Parameters | 32B |
| Number of Layers (Dense layer included) | 61 |
| Number of Dense Layers | 1 |
| Attention Hidden Dimension | 7168 |
| MoE Hidden Dimension (per Expert) | 2048 |
| Number of Attention Heads | 64 |
| Number of Experts | 384 |
| Selected Experts per Token | 8 |
| Number of Shared Experts | 1 |
| Vocabulary Size | 160K |
| Context Length | 256K |
| Attention Mechanism | MLA |
| Activation Function | SwiGLU |
Long context
Extended context length: Kimi K2-Instruct-0905's context window has been increased from 128k to 256k tokens, providing better support for long-horizon tasks.Optimizing model performance
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Training, testing and validation
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Distribution channels
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The provider has not supplied this information.Model Specifications
Context Length262144
LicenseOther
Last UpdatedApril 2026
Input TypeText
Output TypeText
ProviderFireworks
Languages1 Language