Kimi K2.6
Moonshot AI's 1T-parameter open-weight MoE built for agentic coding, long-horizon execution, and parallel agent swarms.
Overview
What is Kimi K2.6
Kimi K2.6 is Moonshot AI's open-weight, 1-trillion-parameter Mixture-of-Experts model released in April 2026. It specializes in long-horizon coding, autonomous agent swarms with up to 300 sub-agents, and native multimodal reasoning, running efficiently with 32 billion active parameters per forward pass.
Running it privately on Venice
On Venice, Kimi K2.6 runs under a private, zero-retention privacy tier — your prompts are not stored or used for training. You get the full open-weight model with vision, tool use, reasoning, and web search capabilities, quantized to int4 for efficient inference without Big-Tech surveillance.
Assessment
Strengths and limitations
- Open-weight 1T-parameter MoE with 32B active parameters, delivering efficient inference for complex agentic tasks.
- State-of-the-art long-horizon coding across Rust, Go, Python, front-end, and DevOps with end-to-end project reliability.
- Native multimodal capabilities on Venice: vision, tool use / function calling, reasoning, web search, and structured JSON output.
- Agent Swarm scales horizontally to 300 parallel sub-agents and 4,000+ coordinated steps for autonomous deliverables.
- Modified MIT license allows self-hosting and modification, with weights available on Hugging Face.
- Pure math reasoning lags behind dedicated reasoning models.
- Modified MIT license requires prominent 'Kimi K2.6' branding for products exceeding 100M MAU or $20M monthly revenue.
- Not uncensored: retains safety alignment and will decline certain harmful requests.
- Runs int4-quantized on Venice; extreme precision workloads may differ slightly from full-precision inference.
- No native desktop computer-use; automation is API- and tool-based rather than direct OS control.
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
- License
- Modified MIT
- Context window
- 256K tokens
- Released
- April 20, 2026
- Architecture
- Mixture-of-Experts (MoE)
- Total parameters
- 1 trillion (1T)
- Active parameters
- 32 billion (32B)
- Layers
- 61 (1 dense + 60 MoE)
- Attention heads
- 64
- Max output
- 64K tokens
- Capabilities
- Vision, Function calling, Reasoning, Web search, Code-optimized
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Apr 2026
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-k2-6",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.75 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 | Best for | Context window | Open weights | Price (Venice) |
|---|---|---|---|---|
| Kimi K2.6 | Agentic coding & swarms | 256K tokens | Yes | $0.75 in · $3.50 out / 1M |
| DeepSeek V3.2 | General reasoning & coding | 160K tokens | Yes | $0.33 in · $0.48 out / 1M |
| Claude Opus 5 | Deep reasoning & analysis | 1M tokens | No | $6 in · $30 out / 1M |
| Grok 4.5 | Long-context & real-time | 500K tokens | No | $2.27 in · $6.80 out / 1M |
The open-weight choice for long-horizon coding and parallel agent execution at a fraction of closed-model cost.
Use cases
What it is good for
- 01Autonomous software engineering: building, deploying, and optimizing full-stack apps over long sessions.
- 02Agent swarm orchestration: decomposing research, writing, or data tasks across hundreds of parallel sub-agents.
- 03Multimodal document analysis: interpreting charts, diagrams, and images alongside text for reports and spreadsheets.
- 04Coding-driven design: turning prompts and visual references into production-ready front-end interfaces and animations.
- 05Background automation: persistent agents that manage schedules, execute code, and orchestrate cross-platform workflows.
Prompting
Getting better results
Break complex builds into explicit milestones; K2.6 excels at long-horizon execution when given clear sub-goals.
Upload UI mockups or diagrams as image inputs to guide coding-driven design tasks.
Enable tool use and web search for agent tasks that require live data or external API orchestration.
For Agent Swarm, define deliverable formats upfront so parallel sub-agents align outputs.
Version history
Earlier 1T / 32B active parameter model with initial agent capabilities.
Improved Office skills and agent capabilities; 256K context.
Current — SOTA coding, 300-agent swarm, open-sourced under Modified MIT.
Successor 3T-class model with 1M context and native vision; open weights released July 27, 2026.
FAQ
Frequently asked questions
Kimi K2.6 is Moonshot AI's open-weight, 1-trillion-parameter Mixture-of-Experts model released in April 2026. It is built for long-horizon coding, autonomous agent swarms, and native multimodal reasoning with vision and tool use.
Venice charges $0.75 per 1M input tokens and $3.50 per 1M output tokens. Cached input is billed at $0.16 per 1M tokens. There is no subscription required.
Yes. Kimi K2.6 is released under Moonshot AI's Modified MIT license with open weights on Hugging Face. You can self-host and modify it, though commercial use above 100M MAU or $20M monthly revenue requires prominent attribution.
Yes. On Venice, Kimi K2.6 supports tool use and function calling, vision with multiple image inputs, reasoning, web search, and structured JSON output.
No. Kimi K2.6 is not uncensored; it retains safety alignment and content filters. On Venice it runs privately with zero retention, but the model itself will still decline harmful requests.
Choose Kimi K2.6 for long-horizon agentic coding and parallel swarm workflows. Choose DeepSeek V3.2 for lower-cost general reasoning and coding tasks where you do not need 300-agent orchestration or 256K context.
Yes. On Venice, Kimi K2.6 runs under a private, zero-retention tier — your prompts are not stored, profiled, or used for training. You can also self-host the open weights independently.
Kimi K3 is Moonshot's later 3T-class model with a 1M-token context and native vision, generally more capable but more expensive. K2.6 remains the efficient open-weight workhorse for agentic coding at lower cost.
Run Kimi K2.6 privately
No prompt logging. No data used for training.