Everything about Kimi K2.6

Kimi K2.6 at a glance

Moonshot AI released Kimi K2.6 on April 20, 2026. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. Its weights are published: you can download it and run it on your own servers, or use one of the 67 API providers that list it on models.dev. That makes it 6 months old as of October 22, 2026.

We file it under open-weight models (68 models in this category on the site).

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), Kimi K2.6 has a capability index of 84.4 out of 100, computed on 4 benchmarks from our basket.

That puts it at #19 in our weekly model ranking.

• GPQA Diamond (PhD-level science questions): 90.8% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 96.1% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 57.2% — the best tracked model, GPT-6 Astra, reaches 93.7%
• SWE-bench Verified (fixing real software bugs): 76.7% — the best tracked model, Claude Opus 4.7, reaches 83.5%

What it can do

Its context window reaches 262,144 tokens, roughly 393 pages of text at 500 words per page: enough for a long report or several contracts at once. A single answer can run to 262,144 tokens.

It accepts as input: text, image and video. Its knowledge stops at January 2025: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (moonshotai/Kimi-K2.6). See also our list of open-source models.

What it costs

Kimi K2.6 costs $0.95 per million input tokens and $4 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.027; a thousand of those tasks, $27.

That is 4 times the median output price of open-weight models ($1).

Tokens read back from cache cost $0.16 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

When to choose it

Given its category, Kimi K2.6 is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Its SWE-bench Verified result (76.7%, above the median of tracked models) makes it a serious candidate for coding agents.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to Kimi K2.6 are:

• Kimi K3 (Moonshot AI, index 80.7) — see the comparison
• GLM-5.3 (Zhipu AI (Z.ai), index 78.8) — see the comparison
• DeepSeek V4.1 Flash 1017 (DeepSeek, index 90.6) — see the comparison
• Kimi K2.5 (Moonshot AI, index 78.1) — see the comparison