MoonshotAI: Kimi K2 0905 (exacto)✓ Catalog verified
Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k. This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
Overview
Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k. This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
Access: available through the official Moonshotai API. Context window: 262K.
Specifications
| API identifier | moonshotai/kimi-k2-0905:exacto |
| Provider | Moonshotai |
| Model type | Text LLM |
| Context window | 262K catalog |
| Input modalities | text |
| Output modalities | text |
| Released | Sep 4, 2025 |
| Moderated | No |
| Architecture modality | text->text |
Pricing
Live pricing components for moonshotai/kimi-k2-0905:exacto as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $6e-7 | $0.60 / 1M | Per input token |
| Completion tokens | $0.0000025 | $2.50 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.8Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | Not advertised | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | ✓ Supported | Supports tools / function calling |
| Self-Hosting | Not advertised | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call MoonshotAI: Kimi K2 0905 (exacto) through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="moonshotai/kimi-k2-0905:exacto",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| ByteDance Seed: Seed 1.6 | Bytedance Seed | 262K | $0.25 | View → |
| ByteDance Seed: Seed 1.6 Flash | Bytedance Seed | 262K | $0.07 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does MoonshotAI: Kimi K2 0905 (exacto) cost?
$0.60 per 1M input tokens and $2.50 per 1M output tokens on the official Moonshotai API.
What is the context window of MoonshotAI: Kimi K2 0905 (exacto)?
MoonshotAI: Kimi K2 0905 (exacto) supports up to 262K tokens of context.
Does MoonshotAI: Kimi K2 0905 (exacto) support tool / function calling?
Yes, MoonshotAI: Kimi K2 0905 (exacto) supports tool / function calling — you can register tools and the model will emit structured tool calls.
How do I access MoonshotAI: Kimi K2 0905 (exacto)?
Use the official Moonshotai API with the model id `moonshotai/kimi-k2-0905:exacto`. See the Quick start section above for code examples.