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THE MODEL, IN DETAIL / Moonshot

Kimi K2.6

moonshotai/kimi-k2.6Open weightsReasoningUnspecified source configuration configuration

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...

OpenRouter
Source-linked model intelligenceCatalog added 20 Apr 2026Specifications observed 28 Oct 2026

Specifications and providers describe MoonshotAI: Kimi K2.6. Evaluation scores retain the exact Unspecified source configuration configuration.

GPQA Diamond

90.8%

Source-reported

Context window

262.1K

235.9K max output tokens

Input price

$0.65

USD / million input tokens

Output price

$3.41

USD / million output tokens

Inference providers

17

17 provider routes observed

Measured output speed

—

No model-level measurement

01 / PERFORMANCE

A clearer view of capability.

Read the score, understand the context, and explore where this model sits among its peers.

Performance fingerprint

Individual published accuracy scores · not a composite index

Individual benchmark accuracy. Axes represent different tasks, not a composite index.FrontierMath Ti…FrontierMath Ti…GPQA DiamondOTIS Mock AIME …SimpleQA VerifiedFrontierMath Tier 4 v2: 25.6FrontierMath Tiers 1–3 v2: 57.2GPQA Diamond: 90.8OTIS Mock AIME 2024–2025: 96.1SimpleQA Verified: 34.9Each axis: 0–100% · Individual evaluations

Performance meets price

GPQA Diamond · same published source and version

Higher score · lower cost80$085$2.87590$5.7595$8.625100$11.5Gemini 4 Argon (high) GPQA Diamond: 96.4 Input: $2/M tokensGPT-6 Astra (max) GPQA Diamond: 95.8 Input: $10/M tokensClaude Sonnet 5.5 (max) GPQA Diamond: 95.6 Input: $2/M tokensKimi K3 (max) GPQA Diamond: 93.1 Input: $0.192/M tokensKimi K3 (high) GPQA Diamond: 91.9 Input: $0.192/M tokensClaude Opus 4.8 GPQA Diamond: 91 Input: $5/M tokensDeepSeek V4 Flash 0731 (max) GPQA Diamond: 91 Input: $0.119/M tokensGLM-5.3 (max) GPQA Diamond: 90.9 Input: $0.0699/M tokensMiniMax-M3 GPQA Diamond: 90.9 Input: $0.3/M tokensQwen3.7 Max GPQA Diamond: 90.9 Input: $1.475/M tokensKimi K2.6 GPQA Diamond: 90.8 Input: $0.65/M tokensKimi K2.6GPT-5.5 (low) GPQA Diamond: 90.7 Input: $5/M tokensClaude Opus 5.5 (max) GPQA Diamond: 90.6 Input: $4/M tokensClaude Sonnet 5 (xhigh) GPQA Diamond: 90.5 Input: $2/M tokensGPT-6 Luna (max) GPQA Diamond: 90.5 Input: $0.1/M tokensClaude Opus 4.7 (xhigh) GPQA Diamond: 90.2 Input: $5/M tokensInput price · USD / million tokens
Google DeepMindOpenAIAnthropicMoonshotDeepSeekZ.ai (Zhipu AI)MiniMaxAlibaba

In good company

GPQA Diamond · Epoch AI · benchmark data

96.4
Google DeepMind
95.8
OpenAI
95.6
Anthropic
93.1
Moonshot
91.9
Moonshot
91
Anthropic
91
DeepSeek
90.9
DeepSeek
90.9
Z.ai (Zhipu AI)
90.8
This model

Rank #33 in this published cohort. 325 models in the source cohort. Showing selected nearby models. Evaluation settings can differ.

Evidence, by task.

Coverage is not a capability score.

5published results
1 evaluation source
Reasoning5 evaluations
CodingAwaiting evidence
Mathematics3 evaluations
VisionAwaiting evidence
AgenticAwaiting evidence
Long contextAwaiting evidence

Documented features, task evaluations, and preference rankings describe different things. Missing coverage does not imply a model cannot perform a task.

Explore our methodology

02 / BENCHMARK EXPLORER

The numbers behind the model.

Original results, exact versions, and a direct path to the evidence behind every score.

5 published results
020406080100FrontierMath Tier 4 v2: 25.6% · Epoch AI · independent runs · Epoch AI · benchmark data25.6%FrontierMath Tie…Higher is betterFrontierMath Tiers 1–3 v2: 57.2% · Epoch AI · independent runs · Epoch AI · benchmark data57.2%FrontierMath Tie…Higher is betterGPQA Diamond: 90.8% · Epoch AI · independent runs · Epoch AI · benchmark data90.8%GPQA DiamondHigher is betterOTIS Mock AIME 2024–2025: 96.1% · Epoch AI · independent runs · Epoch AI · benchmark data96.1%OTIS Mock AIME 2…Higher is betterSimpleQA Verified: 34.9% · Epoch AI · independent runs · Epoch AI · benchmark data34.9%SimpleQA VerifiedHigher is better

5 percentage-based evaluations. Each benchmark measures a different task; compare results within the same evaluation and protocol.

Published benchmark results for Kimi K2.6
BenchmarkScoreVersion & metricSourcePublishedEvidence
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available

03 / COST & VALUE

Know what your workload costs.

Explore token prices, see where the spend goes, and model a workload of your own.

Input$0.65per million tokens
Output$3.41per million tokens
Cache read$0.15per million tokens
Cache write—per million tokens

Workload cost calculator

Change the numbers. See the trade-offs.

Token estimate only. Excludes cache writes, tools, and other provider fees. Actual token usage and provider limits vary.

Your estimated spend

1,000 requests · 11M tokens

$9.91estimated total
Uncached input$6.5
Cached input$0
Output$3.41
Per request$0.0099
Without cache discount$9.91
Estimated cache savings$0

04 / INFERENCE NETWORK

One model. Different ways to run it.

Compare inference routes by price, context, quantization, and reported availability.

17 providers · 17 routes
Provider routes for Kimi K2.6, observed 28 Oct 2026
Provider / routeInput / MOutput / MCache / MContextOutput limitSpeedLatencyAvailability · 1dRates
CChutesINT4$0.39$1.8$0.039262.1K65.5K——100%
DDigitalOceanQuantization not disclosed$0.57$2.4$0.114262.1K235.9K——100%
SStreamLakeFP8$0.5985$2.52$0.1008256K230.4K——100%
IInceptronINT4$0.624$3.5$0.1248262.1K235.9K——100%
CCoreWeaveFP4$0.65$3.41$0.15262.1K235.9K——99.17%
CCrusoeBF16$0.7$3.5$0.35262.1K235.9K——98.64%
DDeepInfraFP4$0.75$3.5$0.15262.1K16.4K——99.41%
VVeniceINT4$0.75$3.5$0.16256K65.5K——96.24%
PParasailINT4$0.75$3.5$0.16262.1K235.9K——100%
SSiliconFlowFP8$0.77$3.4$0.14262.1K235.9K——100%
NNovitaQuantization not disclosed$0.8$3.4$0.16262.1K235.9K——100%
GGMICloudFP8$0.855$3.6$0.144262.1K235.9K——0%
BBaiduFP4$0.95$4$0.16262.1K235.9K——99.79%
AAtlasCloudINT4$0.95$4$0.16262.1K235.9K——100%
CCloudflareQuantization not disclosed$0.95$4$0.16262.1K235.9K——100%
MMoonshot AIQuantization not disclosed$0.95$4$0.16262.1K235.9K——96.77%
PPhalaQuantization not disclosed$1.09$4.6$0.37262.1K235.9K——89.47%

Snapshot observed 28 Oct 2026. Speed and latency are the reported 30-minute median when available. This is a dated snapshot, not live monitoring.

Provider price comparison

USD per million tokens · input and output rates

InputOutput
Chutesint4
Input
$0.39
Output
$1.8
DigitalOcean
Input
$0.57
Output
$2.4
StreamLakefp8
Input
$0.5985
Output
$2.52
Inceptronint4
Input
$0.624
Output
$3.5
CoreWeavefp4
Input
$0.65
Output
$3.41
Crusoebf16
Input
$0.7
Output
$3.5
DeepInfrafp4
Input
$0.75
Output
$3.5
Veniceint4
Input
$0.75
Output
$3.5
Parasailint4
Input
$0.75
Output
$3.5
SiliconFlowfp8
Input
$0.77
Output
$3.4

Reported availability

Provider availability over the selected observation window

Chutes · INT4100%
DigitalOcean100%
StreamLake · FP8100%
Inceptron · INT4100%
CoreWeave · FP499.17%
Crusoe · BF1698.64%
DeepInfra · FP499.41%
Venice · INT496.24%
Parasail · INT4100%
SiliconFlow · FP8100%

Output speed

Reported 30-minute p50 · tokens per second

No provider throughput measurements were returned in this snapshot.

Response latency

Reported 30-minute p50 · seconds

No provider latency measurements were returned in this snapshot.

Context by provider

Actual route limits can differ from the catalog maximum

Chutes · INT4262.1K
DigitalOcean262.1K
StreamLake · FP8256K
Inceptron · INT4262.1K
CoreWeave · FP4262.1K
Crusoe · BF16262.1K
DeepInfra · FP4262.1K
Venice · INT4256K
Parasail · INT4262.1K
SiliconFlow · FP8262.1K

05 / CAPABILITIES & MODALITIES

What goes in. What comes out.

A map of documented interfaces and API features, separate from benchmark performance.

INPUT MODALITIES

TextImage
Kimi K2.6Reasoning supported

OUTPUT MODALITIES

Text

Feature support

Listed catalog API parameters and documented pricing features

ReasoningSupported
Tool callingSupported
Tool choiceSupported
Structured outputsSupported
Response formatSupported
Reasoning detailsSupported
Seed controlSupported
Stop sequencesSupported
Prompt cachingSupported
TemperatureSupported

Room to work

Context capacity and completion limits

262.1Ktotal context tokens
Context window
235.9Kmax output

Output tokens share the total context budget. The maximum completion length is a separate limit, and individual provider routes may have smaller limits.

Catalog context
262,144 tokens
Maximum output
235,929 tokens
Top-provider context
262,144 tokens

Features vary by route

Check the provider before depending on a specific API feature.

API feature support by provider
Provider routeReasoningToolsStructured outputResponse formatImplicit caching
Chutes · INT4SupportedSupportedSupportedNot listedSupported
DigitalOceanSupportedSupportedSupportedSupportedSupported
StreamLake · FP8SupportedSupportedSupportedSupportedSupported
Inceptron · INT4SupportedSupportedSupportedSupportedSupported
CoreWeave · FP4SupportedSupportedSupportedSupportedSupported
Crusoe · BF16SupportedSupportedSupportedSupportedSupported
DeepInfra · FP4SupportedSupportedSupportedSupportedSupported
Venice · INT4SupportedSupportedSupportedSupportedSupported
Parasail · INT4SupportedSupportedSupportedSupportedSupported
SiliconFlow · FP8SupportedSupportedSupportedSupportedSupported
NovitaSupportedSupportedSupportedSupportedSupported
GMICloud · FP8SupportedSupportedSupportedSupportedSupported
Baidu · FP4SupportedSupportedSupportedSupportedSupported
AtlasCloud · INT4SupportedSupportedSupportedSupportedSupported
CloudflareSupportedSupportedSupportedSupportedSupported
Moonshot AISupportedSupportedSupportedSupportedSupported
PhalaSupportedSupportedSupportedSupportedSupported

06 / UNDER THE HOOD

The complete specification sheet.

Exact identities, model architecture, limits, and the defaults that shape a request.

Identity & architecture

Developer
Moonshot
Exact identity
kimi-k2.6
Subject
Model Configuration
Access
Open weights
License
Not disclosed
Parameters
Not disclosed
Tokenizer
Other
Instruction format
Not disclosed

Reasoning & controls

Reasoning
Supported
Mandatory reasoning
No
Reasoning by default
Enabled
Published configuration
Unspecified source configuration
Supported reasoning efforts
Not documented
Default reasoning effort
Not disclosed
API parameters
20
Provider moderation
No
Quantization variants
int4, fp8, fp4, bf16

Dates & limits

Catalog added
20 Apr 2026
Knowledge cutoff
Not disclosed
Catalog expiration
Not documented
Specifications observed
28 Oct 2026
Providers observed
28 Oct 2026
Request limits
Provider-specific / not supplied
Canonical catalog ID
moonshotai/kimi-k2.6-20260420

API parameters & defaults

Availability is catalog-wide. Defaults can vary across providers.

frequency_penaltySupported · default not supplied
include_reasoningSupported · default not supplied
logit_biasSupported · default not supplied
logprobsSupported · default not supplied
max_tokensSupported · default not supplied
min_pSupported · default not supplied
parallel_tool_callsSupported · default not supplied
presence_penaltySupported · default not supplied
reasoningSupported · default not supplied
repetition_penaltySupported · default not supplied
response_formatSupported · default not supplied
seedSupported · default not supplied
stopSupported · default not supplied
structured_outputsSupported · default not supplied
temperatureSupported · default not supplied
tool_choiceSupported · default not supplied
toolsSupported · default not supplied
top_kSupported · default not supplied
top_logprobsSupported · default not supplied
top_pSupported · default not supplied

07 / BUILD WITH THIS MODEL

From exploration to your first request.

Use the documented catalog identifier and keep your API key in an environment variable.

Make an API request

const response = await fetch(
  "https://openrouter.ai/api/v1/chat/completions",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      "model": "moonshotai/kimi-k2.6",
      "messages": [
        {
          "role": "user",
          "content": "Explain the trade-offs in this design."
        }
      ],
      "reasoning": {
        "effort": "unspecified source configuration"
      }
    }),
  },
);
if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();
console.log(data.choices[0].message.content);

08 / PROVENANCE & HISTORY

Every number has a story.

Follow the source, check the observation date, and distinguish published results from model specifications.

The observation trail

Recorded events, in chronological order

Added to the model catalog

Catalog creation date, as recorded by OpenRouter.

Provider routes observed

17 inference routes in this snapshot.

Model specifications observed

Catalog pricing, limits, and supported parameters captured.

Observation dates describe when data was captured. They do not represent newly run evaluations or continuous performance monitoring.

Published observation ledger

Historical evidence, with source and version preserved.

Published observation history for Kimi K2.6
ObservedBenchmarkScoreVersionSourceVerification
28 Oct 2026SimpleQA VerifiedEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tier 4 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tiers 1–3 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026OTIS Mock AIME 2024–2025Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026GPQA DiamondEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026SimpleQA VerifiedEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tier 4 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tiers 1–3 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026OTIS Mock AIME 2024–2025Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026GPQA DiamondEpoch AI · independent runsEpoch AI · benchmark dataSource-reported

09 / A FEW MORE DETAILS

Questions worth asking.

Quick answers, drawn from this model’s connected specifications.

How much does Kimi K2.6 cost?

Catalog starting rates are $0.65 per million input tokens and $3.41 per million output tokens. Cache-read pricing is $0.15 per million tokens. Compare provider routes and service tiers above for their specific prices.

How much context can it handle?

262,144 tokens in the catalog context window. The catalog lists a maximum of 235,929 completion tokens. Provider limits can differ.

Does it support tools and structured output?

Tool calling: listed as supported. Structured outputs: listed as supported. See the provider feature matrix for route-specific differences.

How should I interpret these benchmark results?

Read the benchmark name, exact version, metric, source, and model configuration together. Different evaluation settings can change the result. Preference ratings and source-defined composite indices remain separate from task accuracy.

Are these prices and provider measurements live?

This page uses dated source snapshots. Catalog data was observed on 28 Oct 2026. Provider routes were observed on 28 Oct 2026. Missing measurements are not estimated or treated as zero.

KEEP EXPLORING

More from Moonshot.

Explore related identities from the same developer, or take them into a side-by-side comparison.

THE RIGHT MODEL STARTS WITH THE RIGHT CONTEXT.

Put the details side by side.

Explore the trade-offs in a direct model comparison.

Compare Kimi K2.6
Source-backed evidence. Different tasks. A clearer picture.
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