Is Kimi K2.6 available with Zero Data Retention on Elosia?

Kimi K2.6 runs on Elosia's standard backend; ZDR is available on many other models in the catalog.

How much does Kimi K2.6 cost on Elosia?

Kimi K2.6 is billed by usage at $0.5605 per 1M input tokens and $2.36 per 1M output tokens, within your Elosia plan.

What is Kimi K2.6's context window?

Kimi K2.6 supports a context window of up to 262,144 tokens.

Is Kimi K2.6 hosted in the EU?

Yes, Kimi K2.6 is available on Elosia's EU-hosted (EdenAI) backend for enterprise organizations.

When should you choose Kimi K2.6?

Kimi K2.6 is a balanced model: a strong quality/cost trade-off for everyday production work.

Kimi K2.6

MoonshotAiBalanced

ThinkingTool UseVisionStructured Output

About this model

Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...

Performance Tier

Balanced

Kimi K2.6 is a balanced model from MoonshotAi : strong performance at a reasonable price.

Strong cost-performance ratio. Reliable for most professional use cases without premium pricing.

Pricing

This model is included in Elosia plans

Affordable

Low cost. Suitable for sustained use and high-volume interactions.

Type	per 1M tokens
Input (prompt)	$0.560
Output (completion)	$2.36
Cache read	$0.094

Capabilities

Context Length262K

Max Output Tokens262K

TokenizerOther

Inputtext, image

Outputtext

Release DateApril 20, 2026

Benchmarks

General Intelligence

MMLU

Not reported

GPQA Diamond

90.5%

Mathematics

MATH-500

Not reported

AIME 2026

96.4%

Programming

HumanEval

Not reported

SWE-bench Verified

80.2%

SWE-bench Multilingual

76.7%

Reasoning

Humanity's Last Exam

54%

Multimodal

MMMU-Pro

79.4%

Agentic

SWE-bench Pro

58.6%

Terminal-Bench 2.0

66.7%

Where does Kimi K2.6 stand?

Compare its performance index against every other model.

View the performance leaderboard

Recommended Use Cases

CodingAnalysisResearchGeneral Chat

Strengths

Top-tier agentic coding — SWE-bench Verified 80.2%
Open-weight 1T MoE (32B active) with 256K context window
Excellent graduate-level reasoning (GPQA Diamond 90.5%)
Strong long-horizon task orchestration via agent swarm

Limitations

Moonshot official evaluation focused on agentic and reasoning benchmarks — MMLU and MATH-500 are not part of the reported suite
Smaller Western ecosystem than Claude or GPT families
Superseded by Kimi K3 as Moonshot's flagship