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MoonshotAI: Kimi K2.6
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Kimi K2.6 (Reasoning)
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Released 
April 2026


Kimi K2.6 (Reasoning)

Open-weight trillion-parameter MoE with switchable thinking, image and video input, and a 256K window for long-horizon coding agents.

Modality:
Text
Image
PDF
model ID
moonshotai/kimi-k2.6
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Output Speed *

0.00
tok/s

Intelligence Index *

27
/ 100

Context Window *

262000
tokens

Input price

3.9
Anytoken

Output price

20.46
Anytoken
Kimi K2.6: Open-Weight Thinking Model for Long-Horizon Coding Agents Kimi K2.6 is Moonshot AI's general-purpose Mixture-of-Experts model. It has 1 trillion total parameters, 32 billion active per token, and open weights. Moonshot positions it for long-running code work, instruction following, self-correction and autonomous agent execution. It accepts text, image and video input and returns text. Thinking is on by default and can be turned off. It suits coding agents and tool-calling pipelines that need a 256K window. Newer Kimi models now sit above it in the lineup. Start building with the Kimi K2.6 API on AnyAPI.ai

Performance

Agentic Coding Strength, With Thinking Enabled by Default

Kimi K2.6 is tuned for long-running software engineering and autonomous agent execution. Moonshot reports stronger, more stable long-horizon code writing, better instruction compliance and better self-correction. Third-party model summaries say its reported results lead on agentic and coding benchmarks but trail on pure math reasoning, and that all of them were run in thinking mode. The stated improvement over K2.5 is stability across a 256K context, not a larger window. In production, expect the best results from thinking mode, which adds reasoning tokens and latency before the answer.

Technical Specifications

What the model supports

Kimi K2.6 takes text, image and video input and returns text only. Its context window is 262,144 tokens (256K). The sources reviewed give no separate maximum output figure, so plan output budgets separately from context. Two details matter most in production. Thinking is on by default and sampling is constrained: temperature is fixed at 1.0 with thinking and 0.6 without, and other values return an error. Moonshot's built-in web search tool is also incompatible with thinking mode.
Verified Specifications — 
Kimi K2.6 (Reasoning)
*
Input modalities
Text
Image
PDF
output modalities
Text
Context window
262000
 tokens
Maximum output tokens
262000
Reasoning
Yes
Knowledge cutoff
April 2026
Pricing (standard)
3.9
 AnyTokens in
 / 
20.46
 AnyTokens out

Comparison

Kimi K2.6 or Kimi K2.7 Code for Coding Agents?

Kimi K2.6 and Kimi K2.7 Code are adjacent Moonshot AI models that both offer a 256K context window and tool-driven agent work. Moonshot's documentation says K2.7 Code improves agentic capability over K2.6 by about 10% in external benchmark evaluations. K2.7 Code is specialized for coding. K2.6 is the general-purpose model, with text, image and video input and the option to disable thinking. The decision is whether you want a coding-specialized model with mandatory thinking or a more flexible generalist.

Dimension
Kimi K2.6 (Reasoning)
Kimi K2.7 Code
Context window *
262000
tokens
262144
tokens
Output speed *
0.00
tok/s
74.19
tok/s
Intelligence Index *
27
25.8
Input pricing
3.9
AnyToken
4
AnyToken
Output pricing
20.46
AnyToken
20.4
AnyToken
Knowledge cutoff *
April 2026
June 2026

Choose Kimi K2.6 when you need a general-purpose model that can also handle image and video input, when you want to disable thinking for fast, cheap turns, or when you need open weights you can self-host. Choose Kimi K2.7 Code when the workload is mostly coding or agentic engineering and you accept always-on thinking. K2.7 Code returns an error if thinking is disabled. Neither choice changes the 256K window.

Limitations & Trade-offs

Where Kimi K2.6 (Reasoning) falls short

1
256K ceiling versus newer Kimi models. Kimi K2.6 supports 262,144 tokens, while Moonshot's documentation lists Kimi K3 with a 1M-token window. Whole-repository analysis, very long agent traces and large document sets can exceed K2.6's window. For those workloads, K3 is the better fit. K2.6 suits tasks that stay within 256K.
2
Thinking-mode constraints. Thinking is on by default, and Moonshot says its built-in web search tool is temporarily incompatible with it. Temperature is also fixed: 1.0 with thinking, 0.6 without. Other values return an error. Search-augmented agents must disable thinking or supply their own search tool, and sampling-sensitive pipelines lose tuning room.
3
Latency depends on the host and on reasoning. In thinking mode the model produces reasoning tokens before the final answer, so time to the first answer token is longer than time to the first token. DeepInfra's comparison of K2.6 hosts shows output speeds from about 21 to 157 tokens per second and time to first token from 0.71 to 2.61 seconds. For voice or live UI use, disable thinking or choose a faster model.
4
Text-only output and a superseded position. K2.6 accepts images and video but returns only text, so it cannot generate images or audio. Moonshot's documentation also lists K2.7 Code and K3 above it, and K2.7 Code is reported to be about 10% stronger on agentic benchmarks. New agent builds should compare against those models first.

Best-Fit Workloads

Where this model earns its place

01

Long-horizon coding agents


Moonshot positions K2.6 around stable long-term code writing, better self-correction and complex software engineering. It supports multi-step tool loops inside a 256K window. That suits agents that read files, run tools, fix errors and iterate across many turns. Keep thinking enabled for the hardest steps. If the work is purely coding and you want the strongest agentic results, compare it with Kimi K2.7 Code.

02

Image and video understanding with tools


K2.6 accepts text, image and video input through Moonshot's API, and its model description highlights multi-turn function calling with images between tool calls. This fits UI-debugging agents, screenshot-driven workflows and visual inspection pipelines that call external functions. Output is text only, so image or audio generation needs a separate model.

03

Self-hosted and open-weight deployments


The weights are public as moonshotai/Kimi-K2.6 under a Modified MIT license, with native INT4 quantization and support for vLLM and SGLang. Teams with data-residency needs or an in-house inference stack can run the same model they prototype against over the API. Hosting a 1-trillion-parameter MoE needs substantial GPU capacity, so API access is the practical starting point.

04

Switchable reasoning for mixed-complexity agents


One model ID covers thinking and non-thinking turns, selected per request. A pipeline can use thinking for planning, debugging and multi-file reasoning, then turn it off for short tool-routing or formatting turns. This avoids running two separate models for different step types. The cost is fixed temperatures in each mode and the need to preserve reasoning content across tool turns.

Pricing in anytokens via AnyAPI
Input
3.9
₳
Output
20.46
₳
Cache write
—
₳
Cache read
1.2
₳
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PerformanceTechnical SpecificationsComparisonLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access Kimi K2.6 (Reasoning) via AnyAPI.ai

Access Kimi K2.6 (Reasoning) through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate Kimi K2.6 (Reasoning) without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access Kimi K2.6 (Reasoning) and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test Kimi K2.6 (Reasoning) against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use Kimi K2.6 (Reasoning) from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use Kimi K2.6 (Reasoning) for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

Kimi K2.6 has a 256K-token context window, listed precisely as 262,144 tokens. Moonshot's documentation says K2.6 and K2.7 Code share this size, while Kimi K3 is listed at 1M tokens. The sources reviewed give no separate maximum output figure, so treat context window and output length as different limits.

Yes. Moonshot AI's API documentation says Kimi K2.6 supports text, image and video input. Output is text only. The model description highlights multi-turn function calling with images interleaved between tool calls, which suits visual agent workflows. It does not generate images or audio.

In Moonshot's native API, set the thinking parameter to type disabled. Thinking is on by default. Temperature is fixed at 1.0 with thinking and 0.6 without, and other values return an error. Parameter names may differ on other platforms, so check this page's AnyAPI.ai documentation for the exact request format.

Kimi K2.6 is open-weight, not strictly open source. The weights are published as moonshotai/Kimi-K2.6 on Hugging Face under a Modified MIT license. It is a Mixture-of-Experts model with 1 trillion total and 32 billion active parameters, and it can be served with vLLM or SGLang. Check the license terms before commercial use.

Not with Moonshot's built-in web search tool. Moonshot's documentation says that tool is temporarily incompatible with K2.6 thinking mode. You can disable thinking before using it, or define your own search function through standard tool calling. Check current documentation, since this restriction is described as temporary.

* Benchmark data source: Artificial Analysis artificialanalysis.ai