Throughput (tokens / sec)
avg. 48.6tokens/s
Marathon coding agents with visual context
Run instantly. Pay only for what you use.
Kimi K2.6 is Moonshot AI's open-weight agentic flagship under a Modified MIT license: a 1T-parameter MoE with 32B active per token across 384 experts, a 262K-token context window, and native vision through its 400M-parameter MoonViT encoder, with thinking and instant modes.
It is built for marathon autonomy: runs sustain 4,000+ coordinated tool calls over 12+ hours, and its Agent Swarm architecture scales to 300 parallel sub-agents — the model keeps a plan where others lose the thread.
It posts 80.2% on SWE-bench Verified, 58.6 on SWE-bench Pro, and 66.7 on Terminal-Bench 2.0, and scores 44 on the Artificial Analysis Intelligence Index — the highest of any model in the Entrim catalog.
1Ttotal parameters, 32B active
A 384-expert MoE — frontier scale billed at 32B-active compute.
12+hours of autonomous running
Sustains 4,000+ coordinated tool calls in a single run without losing the plan.
300parallel sub-agents
Agent Swarm coordination can produce 100+ files from a single prompt.
Kimi K2.6 is strongest where agents run longest — overnight refactors, multi-agent pipelines, and UI work that needs a model to hold a plan for hours, not minutes.
80.2% on SWE-bench Verified and 66.7% on Terminal-Bench 2.0 — plans hold across thousands of steps.
300 sub-agents and 4,000 steps per run — parallel decomposition is part of the model, not your framework.
The 400M-parameter MoonViT encoder reads screenshots and design files — front-end work grounded in real UI.
Deep reasoning for the hard steps, instant replies for quick ones — switchable per request.
Marathon coding agents
Sustains 4,000+ tool calls across 12+ hour autonomous runs without losing the plan.
Multi-agent orchestration
Agent Swarm scales to 300 parallel sub-agents and 4,000 steps — one prompt can produce 100+ files.
Frontier-adjacent engineering
80.2% on SWE-bench Verified — open-weight results within a point of closed frontier models.
Front-end work from screenshots
MoonViT vision grounds UI tasks in real screenshots and design images — visual context stays in the loop.
Cost-sensitive, high-volume fleets
Premium 1T-class pricing. For always-on loops where per-token cost dominates, use Qwen 3.6 35B-A3B.
Latency-critical chat
Throughput is modest for its class and thinking traces run long — about 1.7× the median output tokens in Artificial Analysis testing. Use GPT OSS 120B for fast replies.
Audio input or speech understanding
Inputs are text and image. Transcribe audio upstream before sending it to the model.
avg. 48.6tokens/s
avg. 980ms
80.2%SWE-bench Verified
Resolves real GitHub issues end-to-end in agentic coding runs.
58.6SWE-bench Pro
Harder, contamination-resistant software engineering tasks.
66.7%Terminal-Bench 2.0
Autonomous work in a real terminal — builds, debugging, ops tasks.
Code examples
curl -X POST "https://api.entrim.ai/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $ENTRIM_API_KEY" \ -d '{ "model": "moonshotai/Kimi-K2.6", "messages": [ { "role": "user", "content": "Why do they call it a building if it is already built?" } ] }'SDKs & docs
Use familiar SDK patterns with quickstart examples for common production setups.
OpenAI-compatible
Keep the request format your team already knows.
No cold starts
Production requests are served without model spin-up delays.
High-throughput agentic work with 1M context
Tool-heavy coding at 3B-active cost
Long-horizon engineering with planning depth