Kimi K2.5
Open-weight multimodal agentic model with Agent Swarm, vision-to-code, and 256K context — private on Venice with zero retention.
Overview
What is Kimi K2.5
Kimi K2.5 is an open-weight, native multimodal agentic model released by Moonshot AI in January 2026. It features a 1T-parameter MoE architecture, 256K context window, and excels in vision-to-code, cross-modal reasoning, and self-directed Agent Swarm workflows that execute complex tasks in parallel.
Running it privately on Venice
On Venice, Kimi K2.5 runs with full zero retention — your prompts are never stored or profiled. This means you get uncensored, private access to a powerful open agentic model, ideal for sensitive development, research, or enterprise workflows. The combination of open weights, vision, and tool use makes it a sovereign AI option for code generation and visual reasoning without surveillance.
Assessment
Strengths and limitations
- State-of-the-art vision-to-code capabilities: generates production-ready front-end code from images, UI designs, and video workflows.
- Agent Swarm enables self-directed, parallel execution of up to 100 sub-agents, reducing task latency by up to 4.5×.
- Native multimodal architecture trained on 15 trillion visual and text tokens enables strong cross-modal reasoning.
- Open weights allow for self-hosting, auditing, and customization under a permissive license with commercial use conditions.
- Supports tool use, web search, and structured JSON output for agentic workflows.
- High hallucination rate (AA-Omniscience score of -11) undermines reliability for fact-critical applications.
- Slower inference speed compared to rivals like DeepSeek V4 Flash, affecting real-time responsiveness.
- Not end-to-end encrypted or running in a TEE on Venice, so metadata-level privacy is limited.
- Modified MIT license requires attribution in high-revenue or high-traffic commercial products.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- Moonshot AI
- Released
- January 2026
- Architecture
- Mixture-of-Experts (MoE)
- Total Parameters
- 1T
- Activated Parameters
- 32B
- Open weights
- Yes — Apache 2.0 / Modified MIT
- Context window
- 256K tokens
- Max output
- 65.536K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Jan 2026
- License
- Modified MIT
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kimi-k2-5",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.56 per 1M input tokens and $3.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Max output | Strongest at | Open weights | Price (Venice) |
|---|---|---|---|---|
| Kimi K2.5 | 65.536K tokens | Vision-to-code, Agent Swarm | Yes | $0.56 in · $3.50 out / 1M |
| DeepSeek V4 Flash 0731 | 128K | Speed, cost efficiency | No | $0.17 in · $0.35 out / 1M |
| GLM 5.1 | 32K | Multilingual, reasoning | Yes | $1.10 in · $4.15 out / 1M |
| Claude Sonnet 4.6 | 128K | Balanced reasoning, safety | No | $3.60 in · $18 out / 1M |
Open-weight multimodal agentic model with strong vision and parallel agent execution.
Use cases
What it is good for
- 01Visual-to-code development: turn design mockups into HTML/CSS/JS with high fidelity.
- 02Automated research with Agent Swarm: parallelize web searches, data extraction, and summarization.
- 03Interactive agent workflows: build AI assistants that use vision, tools, and code execution.
- 04Academic and mathematical reasoning: strong performance on AIME and HMMT benchmarks.
- 05Private code generation: leverage open weights and zero retention for secure development.
Prompting
Getting better results
Use clear visual references — upload images or describe layouts precisely to maximize vision-to-code accuracy.
Break complex tasks into steps; let Agent Swarm handle orchestration rather than micromanaging.
Include explicit instructions for tool use (e.g., 'search the web for X') to trigger web search or code execution.
For code output, request JSON schema or specific frameworks (React, Tailwind) to improve fidelity.
Avoid ambiguous terms; specificity reduces hallucination risk in reasoning and vision tasks.
Version history
Predecessor model without Agent Swarm or full vision integration.
Current — adds vision, Agent Swarm, and open weights.
FAQ
Frequently asked questions
Kimi K2.5 is an open-weight, native multimodal agentic model developed by Moonshot AI and released in January 2026. It features a 1T-parameter MoE architecture, 256K context window, and excels in vision-to-code, cross-modal reasoning, and Agent Swarm — a self-directed parallel agent framework.
Kimi K2.5 is open-weight under a Modified MIT license, meaning the model weights are publicly available for use, modification, and redistribution. However, commercial products with over 100M monthly active users or $20M monthly revenue must display 'Kimi K2.5' in their UI.
On Venice, Kimi K2.5 costs $0.56 per 1M input tokens and $3.50 per 1M output tokens, with cached input at $0.22 per 1M. Billing is per token, with no subscription required.
Yes, Kimi K2.5 is natively multimodal and supports image input. It can reason over visual content, generate code from UI mockups, and perform visual debugging, making it ideal for vision-to-code workflows.
Yes, Kimi K2.5 supports function calling, web search, and code execution. It can autonomously orchestrate tools as part of its agentic workflows, especially within the Agent Swarm framework.
Kimi K3 is a newer, more expensive model with a 1M context window and higher output cost ($18.75/1M), while Kimi K2.5 offers 256K context at a lower price point. K3 may offer improved reasoning or tool use, but K2.5 remains competitive with open weights and vision capabilities.
Kimi K2.5 has a 256K token context window, allowing it to process long documents, codebases, or multi-image conversations in a single session.
Run Kimi K2.5 privately
No prompt logging. No data used for training.