about
Sharing the same 1T-parameter, 32B-active MoE backbone as K2.5 but without the vision encoder, K2-Instruct was trained on 15.5T tokens using the Muon optimizer with MuonClip, achieving zero training instability at trillion-parameter scale. It scores 65.8% on Tau2 Telecom and 76.5% on AceBench for tool use, and ships under a modified MIT license with block-FP8 quantized weights.
Licensegroq
Context window(in thousands)131,072
Use cases for Kimi-K2-Instruct
- Scalable agentic tool use: Scoring 76.5% on AceBench and 70.6% on Tau2 Retail, it handles complex multi-step function calling sequences across APIs, databases, and external services.
- Large-scale stable training reference: Trained with the Muon optimizer across 15.5T tokens at 1T parameter scale with zero instability, it serves as a validated architecture for organizations training their own large MoE models.
- Multilingual enterprise chat: With 89.5% on MMLU and 92.7% on MMLU-Redux, it provides strong general knowledge across languages for customer-facing applications that require broad domain coverage.
Quality
Arena EloN/A
MMLU89.5
MT Bench51.8
Kimi K2 Instruct scores 89.5% on MMLU and 92.7% on MMLU-Redux, placing it near GPT-4.1 (90.2% MMLU) on the same sheet. On tool-use benchmarks it reaches 76.5% on AceBench and 70.6% on Tau2 Retail, reflecting its optimization for agentic function calling. With 32B active parameters from a 1T total, it achieves frontier-tier knowledge scores at efficient inference cost.
pricing
Running Kimi K2 Instruct through Telnyx Inference costs $0.55 per million input tokens and $2.20 per million output tokens. Processing 1,000,000 function-calling tasks at 1,500 tokens each would cost approximately $1,650, comparable to Qwen3 235B ($1,750) with stronger tool-use benchmark scores.
What's Twitter saying?
- Developers praise Kimi K2 Instruct's superior coding performance, outperforming benchmarks and showing better tool calling than o1 or Claude Sonnet, with positive real-world sentiment.
- It's significantly cheaper than competitors like Claude Sonnet 4 ($0.15/M input vs. $3/M), making it a budget favorite despite slightly better code quality in tests.
- Common complaints include slow response times (34 tokens/sec vs. 91 for Sonnet) and small context window, though core intelligence is called a "rough diamond."
Explore Our LLM Library
Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.
| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| deepseek-ai | DeepSeek-R1-Distill-Qwen-14B | text generation | English | 43,000 | 14.8B | medium | deepseek |
| fixie-ai | ultravox-v0_4_1-llama-3_1-8b | audio text-to-text | Multilingual | 8,000 | 8.7B | small | mit |
| gemma-2b-it | text generation | English | 8,192 | 2.5B | small | gemma | |
| gemma-7b-it | text generation | English | 8,192 | 8.5B | small | gemma | |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 |
| meta-llama | Llama-Guard-3-1B | safety classification | Multilingual | 128,000 | 1.5B | small | llama3.3 |
| meta-llama | Meta-Llama-3.1-70B-Instruct | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 |
| meta-llama | Meta-Llama-3.1-8B-Instruct | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 |
| minimaxai | MiniMax-M2.5 | text generation | English | 2,000,000 | 0 | large | minimaxai |
| minimaxai | MiniMax-M2.7 | text generation | English | 200,000 | 0 | large | minimaxai |
| mistralai | Mistral-7B-Instruct-v0.1 | text generation | English | 8,192 | 7.2B | small | apache-2.0 |
| mistralai | Mistral-7B-Instruct-v0.2 | text generation | English | 32,768 | 7.2B | small | apache-2.0 |
| mistralai | Mixtral-8x7B-Instruct-v0.1 | text generation | Multilingual | 32,768 | 46.7B | medium | apache-2.0 |
| moonshotai | Kimi-K2.5 | text generation | English | 256,000 | 1.0T | large | modified-mit |
| Qwen | Qwen3-235B-A22B | text generation | English | 32,768 | 235.1B | large | apache-2.0 |
| zai-org | GLM-5.1-FP8 | text generation | English | 202,752 | 753.9B | large | mit |
| anthropic | claude-3-7-sonnet-latest | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-haiku-4-5 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-opus-4-6 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| anthropic | claude-sonnet-4-20250514 | text generation | Multilingual | 200,000 | 0 | large | anthropic |
| gemini-2.0-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash | text generation | Multilingual | 1,048,576 | 0 | large | ||
| gemini-2.5-flash-lite | text generation | Multilingual | 1,048,576 | 0 | large | ||
| groq | gpt-oss-120b | text generation | English | 131,072 | 117.0B | large | groq |
| groq | kimi-k2-instruct | text generation | English | 131,072 | 1.0T | large | groq |
| groq | llama-3.3-70b-versatile | text generation | Multilingual | 131,072 | 70.6B | large | llama3.3 |
| groq | llama-4-maverick-17b-128e-instruct | text generation | Multilingual | 1,000,000 | 400.0B | large | llama4 |
| groq | llama-4-scout-17b-16e-instruct | text generation | Multilingual | 128,000 | 109.0B | large | llama4 |
| openai | gpt-3.5-turbo | text generation | Multilingual | 4,096 | 0 | large | openai |
| openai | gpt-4 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0125-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-0613 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-1106-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-32k-0314 | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4-turbo-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4.1 | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4.1-mini | text generation | Multilingual | 1,047,576 | 0 | large | openai |
| openai | gpt-4o | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-4o-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5-mini | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | gpt-5.2 | text generation | Multilingual | 400,000 | 0 | large | openai |
| openai | o1-mini | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o1-preview | text generation | Multilingual | 128,000 | 0 | large | openai |
| openai | o3-mini | text generation | Multilingual | 200,000 | 0 | large | openai |
| xai-org | grok-2 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-2-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3 | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-beta | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-fast-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-latest | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini | text generation | Multilingual | 131,072 | 0 | large | xai |
| xai-org | grok-3-mini-fast | text generation | Multilingual | 131,072 | 0 | large | xai |
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faqs
What is Kimi K2 Instruct?
Kimi K2 Instruct is Moonshot AI's general-purpose chat model, designed for drop-in conversational and agentic experiences without extended thinking. It features strong tool-calling capabilities and autonomously decides when and how to invoke available tools.
How does Kimi K2 differ from K2.5?
Kimi K2 Instruct is a reflex-grade model without long thinking, optimized for fast responses. K2.5 adds multimodal vision capabilities, thinking modes, and agent swarm technology for coordinated multi-agent execution on complex tasks.
Is Kimi K2 Instruct free?
Yes, Kimi K2 Instruct is open-source and available on Hugging Face under a permissive license. It is also accessible through hosted inference on Moonshot's platform and third-party providers.
What is Kimi K2 good for?
Kimi K2 Instruct is designed for code generation, complex problem-solving, tool use, and multilingual chat applications. Its OpenAI and Anthropic-compatible API makes it easy to integrate as a drop-in replacement in existing workflows.