A 1.0T parameter native multimodal agentic model utilizing MoE architecture to enable 300-agent swarm orchestration, long-horizon codebase overhauls, and 24/7 proactive execution.
A solid 1000B-parameter MoE language model from Moonshot AI. Pulls ahead on competition math (AIME 2026) (96/100), so reach for it when that's the dimension that matters. Newly released, so production-readiness is still being shaken out.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
Copy and paste this command to start running the model locally.
ollama run kimi-k2.6Access model weights, configuration files, and documentation.
See how different quantization levels affect VRAM requirements and quality for this model.
| Format | VRAM Required | Quality | |
|---|---|---|---|
| Q2_K | 79.4 GB | Low | |
| Q4_K_MRecommended | 86.2 GB | Good | |
| Q5_K_M | 89.4 GB | Very Good | |
| Q6_K | 93.2 GB | Excellent | |
| Q8_0 | 101.2 GB | Near Perfect | |
| FP16 | 131.6 GB | Full |
See which devices can run this model and at what quality level.
NVIDIA H200 SXM 141GBNVIDIA | SS | 44.8 tok/s | 86.2 GB | |
| SS | 49.5 tok/s | 86.2 GB | ||
Google TPU v7 (Ironwood)Google | SS | 68.9 tok/s | 86.2 GB | |
NVIDIA B200 GPUNVIDIA | SS | 74.7 tok/s | 86.2 GB | |
| SS | 56.1 tok/s | 86.2 GB | ||
| SS | 74.7 tok/s | 86.2 GB | ||
| SS | 34.6 tok/s | 86.2 GB | ||
| SS | 66.3 tok/s | 86.2 GB | ||
| SS | 66.3 tok/s | 86.2 GB | ||
| SS | 66.3 tok/s | 86.2 GB | ||
| SS | 66.3 tok/s | 86.2 GB | ||
SuperMicro Super AI StationSuperMicro | SS | 66.3 tok/s | 86.2 GB | |
Gigabyte W775-V10-L01Gigabyte | SS | 66.3 tok/s | 86.2 GB | |
| AA | 22.9 tok/s | 86.2 GB | ||
Google Cloud TPU v5pGoogle | AA | 25.8 tok/s | 86.2 GB | |
| BB | 7.5 tok/s | 86.2 GB | ||
| BB | 7.5 tok/s | 86.2 GB | ||
| BB | 5.7 tok/s | 86.2 GB | ||
| BB | 5.7 tok/s | 86.2 GB | ||
| BB | 5.7 tok/s | 86.2 GB | ||
| BB | 5.1 tok/s | 86.2 GB | ||
| BB | 5.1 tok/s | 86.2 GB | ||
| BB | 5.1 tok/s | 86.2 GB | ||
| BB | 5.1 tok/s | 86.2 GB | ||
| BB | 2.6 tok/s | 86.2 GB |
Energy cost on NVIDIA A100 SXM4 80GB (~19 tok/s, Q4_K_M) vs flagship API pricing.
| Source | Cost per 1M tokens |
|---|---|
Local (energy only)Kimi K2.6 on NVIDIA A100 SXM4 80GB · ~19 tok/s · 400W | $0.700 |
GPT-5.5OpenAI · in $5.00 · out $30.00 | $12.50 |
Claude Opus 4.7 ThinkingAnthropic · in $5.00 · out $25.00 | $11.00 |
Gemini 3.1 Flash Lite PreviewGoogle · in $0.250 · out $1.50 | $0.625 |
Grok 4.3 betaxAI · in $3.00 · out $15.00 | $6.60 |
API prices blended at 70% input / 30% output.
Hardware amortisation not included. Run the full ROI calculator for payback math.
Kimi K2.6, developed by Moonshot AI, is a native multimodal Mixture of Experts (MoE) model designed for high-autonomy agentic workflows. With a total parameter count of 1000B (1.0T) and 32B active parameters per token, it occupies the extreme high-end of the open-weight landscape. Unlike models optimized purely for chat, K2.6 is specifically engineered for "long-horizon" tasks—complex, multi-step operations that require sustained reasoning over several hours of execution.
While many trillion-parameter models are cumbersome for local deployment, the MoE architecture of K2.6 makes it a viable candidate for high-end local workstations. It competes directly with other massive-scale open models like DeepSeek-V3 or Llama 3.1 405B, but distinguishes itself through its "Agent Swarm" orchestration. This capability allows the model to coordinate up to 300 sub-agents to perform parallelized tasks, such as performing a codebase-wide refactor or generating a full-stack application from a single vision-based prompt.
Kimi K2.6 utilizes a sophisticated Mixture of Experts (MoE) framework. While the model contains 1000B total parameters, only 32B are activated during any single forward pass. This sparsity is the key to running Kimi K2.6 locally; it provides the reasoning depth of a trillion-parameter model with the inference latency more typical of a mid-sized dense model.
The model features a 262,144 (256K) token context window, which is essential for its primary use case: long-horizon coding. This allows practitioners to feed entire repositories or massive documentation sets into the prompt without losing coherence. Its native multimodality is handled via the MoonViT encoder, enabling the model to "see" UI layouts, diagrams, and video files directly rather than relying on a separate vision-to-text bridge.
K2.6 is not a general-purpose chatbot; it is a tool for autonomous execution. Moonshot AI has optimized the model for "proactive execution," meaning it is designed to use tools and call functions with minimal human steering over runs lasting up to 12 hours.
K2.6 excels at end-to-end codebase overhauls. It can navigate complex directory structures in languages like Rust, Go, and Python to implement features or optimize performance across multiple files. For local developers, this means the model can act as a localized "junior engineer" that handles repetitive refactoring or unit test generation across a large local context.
Because the model is natively multimodal, it can take a screenshot of a legacy UI or a hand-drawn wireframe and generate production-ready code. It supports generating structured layouts, interactive CSS animations, and full-stack logic, effectively bridging the gap between design and implementation in a single step.
The "Agent Swarm" feature allows K2.6 to decompose a massive objective—such as "build a data visualization dashboard for this CSV"—into 300 sub-tasks. It then orchestrates these agents to handle data cleaning, backend logic, and frontend styling simultaneously. This makes it particularly effective for local researchers who need to process large-scale datasets through complex, multi-step pipelines.
Running a 1000B parameter model locally is a significant hardware challenge, even with MoE efficiency. The primary bottleneck is VRAM. While the active parameters (32B) dictate the inference speed, the total parameters (1000B) must still be addressed in memory, though quantization significantly lowers the barrier.
To run Kimi K2.6 locally, you must account for the weights and the KV cache for that 256K context window.
The quickest way to deploy is via Ollama, using the ollama run kimi-k2.6 command. For practitioners looking for maximum performance, using llama.cpp with GGUF or EXL2 quantizations allows for more granular control over layer offloading.
Kimi K2.6 sits in a specialized niche between general-purpose LLMs and specialized coding assistants.
Llama 3.1 405B is a dense model, meaning every parameter is used for every token. While Llama may have higher "raw" knowledge density, K2.6 is significantly faster during inference due to its MoE architecture. For coding and agentic tasks, K2.6’s 256K context window and native vision give it a distinct advantage over Llama’s text-heavy focus.
Both models utilize MoE architectures and are strong in coding and math. However, K2.6 is specifically tuned for "proactive" agentic behavior—the ability to run for 12 hours and 4,000 steps without human intervention. While DeepSeek-V3 often wins on pure code benchmarks (like HumanEval), K2.6 is generally superior for "Agent Swarm" tasks where multi-step orchestration is required.
The jump from 2.5 to 2.6 focuses almost entirely on the "Agentic" pillar. While K2.5 introduced the multimodal capabilities, K2.6 optimizes the logic for tool use and long-duration autonomous runs. If your workflow involves simple chat or single-file code edits, K2.5 remains a lighter, more efficient choice. For full-stack generation and swarm-based tasks, K2.6 is the necessary upgrade.
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