Capability
60.1/100
field median 58.3
#69 of 226 ranked models
Kimi K2.6
Released Apr 20, 2026 — see all recent releases
Decision reading
Kimi K2.6 scores 60.1 out of 100 and ranks #69 of 226. This profile shows 32 source-displayable benchmark rows; its strongest eligible category is Mathematics at #1. API pricing is $0.95 input and $4 output per million tokens.
Data as of August 27, 2026 · How the score is built
Strongest published evidence
Mathematics ranks #1. Particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Validate before choosing
32 published rows leave some tracked benchmark slots empty. Agentic is its lowest eligible category at #103.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
$0.95input / $4 output
input median $1
blended $2.48
Speed
46tok/s
field median 97 tok/s
First token 2.65 s
Context
256Ktokens
field median 203,000
Reported for this model; direct source link not stored
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
Kimi K2.6 category percentile values
- Agentic26th percentile
- Coding51st percentile
- ReasoningNot eligible
- Knowledge8th percentile
- Math100th percentile
- MultilingualNot eligible
- Multimodal47th percentile
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#103/138
- Coding#71/144
- ReasoningNot ranked
- Knowledge#55/60
- Math#1/7
- MultilingualNot ranked
- Multimodal#19/35
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Current modelKimi K2.6 · 60.1 score · $2.48 blended per million tokens
Horizontal: blended price per million tokens, log scale · Vertical: public score
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- Agentic11/11 verified
- Coding8/8 verified
- ReasoningNot measured
- Knowledge3/3 verified
- Math5/5 verified
- MultilingualNot measured
- Multimodal5/5 verified
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 256K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Superseded
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Kimi K2.6 needs ~640GB VRAM (8× NVIDIA H100 (80GB)).
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Kimi K2.6
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Category score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #103 of 138Percentile 26thWeight 22%11 benchmarksVerified | 41.4 | #103 of 138 | 26th | 22% | 11 benchmarks | Verified |
| CodingRank #71 of 144Percentile 51stWeight 20%8 benchmarksVerified | 51.0 | #71 of 144 | 51st | 20% | 8 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank #55 of 60Percentile 8thWeight 12%3 benchmarksVerified | 51.3 | #55 of 60 | 8th | 12% | 3 benchmarks | Verified |
| MathRank #1 of 7Percentile 100thWeight 5%5 benchmarksVerified | 71.7 | #1 of 7 | 100th | 5% | 5 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalRank #19 of 35Percentile 47thWeight 12%5 benchmarksVerified | 60.5 | #19 of 35 | 47th | 12% | 5 benchmarks | Verified |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Coding8 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score80.2% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap15.8 behind | WeightWeighted 16% | Provider exact |
| SciCodeScientific Code Benchmark | Score52.2% | Versus best verified row Best verified: Sakana Fugu · 60.1% | Gap7.9 behind | WeightWeighted 16% | Provider exact |
| SWE-bench Pro | Score58.6% | Versus best verified row Best verified: Claude Mythos 5 · 80.3% | Gap21.7 behind | WeightWeighted 10% | Provider exact |
| LiveCodeBench v6 | Score89.6% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.2% | Gap3.6 behind | WeightDisplay only | Provider exact |
| SWE Multilingual | Score76.7% | Versus best verified row Best verified: Claude Opus 5 · 89.5% | Gap12.8 behind | WeightDisplay only | Provider exact |
| Terminal-Bench 2.0 | Score66.7% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap25.2 behind | WeightDisplay only | Provider exact |
| Vibe Code BenchVibe Code Bench v1.1 | Score37.89% | Versus best verified row Best verified: Claude Opus 4.7 · 71.00% | Gap33.1 behind | WeightDisplay only | Benchmark exact |
| cursorBench31 | Score47.6% | Versus best verified row Best verified: Claude Fable 5 · 70.6% | Gap23 behind | WeightDisplay only | Benchmark exact |
Agentic11 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score66.7% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap25.2 behind | WeightWeighted 38% | Provider exact |
| OSWorld-Verified | Score73.1% | Versus best verified row Best verified: Qwen3.8 Max · 86.1% | Gap13 behind | WeightWeighted 34% | Provider exact |
| BrowseComp | Score83.2% | Versus best verified row Best verified: GPT-5.6 Sol · 92.2% | Gap9 behind | WeightWeighted 28% | Provider exact |
| Toolathlon | Score50% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap25.6 behind | WeightDisplay only | Provider exact |
| MCP Atlas | Score55.9% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap32.2 behind | WeightDisplay only | Provider exact |
| Claw-Eval | Score62.3% | Versus best verified row Best verified: Ornith-1.5-397B · 81.4% | Gap19.1 behind | WeightDisplay only | Benchmark exact |
| DeepSearchQA | Score92.5% | Versus best verified row Best verified: Claude Opus 5 · 95.0% | Gap2.5 behind | WeightDisplay only | Provider exact |
| WideResearch | Score80.8% | Versus best verified row Best verified: Qwen3.8 Max · 81.9% | Gap1.1 behind | WeightDisplay only | Provider exact |
| Gert LabsGert Labs Composite Game Benchmark | Score56.82% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap16.2 behind | WeightDisplay only | Benchmark exact |
| ResearchClawBench | Score18.0% | Versus best verified row Best verified: Claude Opus 4.8 · 21.1% | Gap3.1 behind | WeightDisplay only | Benchmark exact |
| OSWorld 2.0 | Score4.6% | Versus best verified row Best verified: Claude Opus 5 · 70.6% | Gap66 behind | WeightDisplay only | Benchmark exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score34.7% | Versus best verified row Best verified: Claude Opus 5 · 64.7% | Gap30 behind | WeightWeighted 45% | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score90.5% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap5 behind | WeightWeighted 7% | Provider exact |
| GPQA-DGPQA Diamond | Score90.5% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap5 behind | WeightDisplay only | Provider exact |
Math5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score38.966% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap50 behind | WeightWeighted 30% | Benchmark exact |
| AIME26AIME 2026 | Score96.4% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap2.8 behind | WeightWeighted 25% | Provider exact |
| HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026 | Score92.7% | Versus best verified row Best verified: Qwen3.7 Max · 97.1% | Gap4.4 behind | WeightWeighted 25% | Provider exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score14.580% | Versus best verified row Best verified: GPT-5.6 Sol · 83.000% | Gap68.4 behind | WeightWeighted 10% | Benchmark exact |
| MMAnswerBench | Score86.0% | Versus best verified row Best verified: GLM-5.2 · 91.0% | Gap5 behind | WeightDisplay only | Provider exact |
Multimodal5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score79.4% | Versus best verified row Best verified: GPT-5.4 Pro · 94% | Gap14.6 behind | WeightWeighted 45% | Provider exact |
| CharXivCharXiv Reasoning | Score80.4% | Versus best verified row Best verified: Claude Mythos 5 · 93.5% | Gap13.1 behind | WeightWeighted 25% | Provider exact |
| MMMU-Pro w/ PythonMMMU-Pro with Python | Score80.1% | Versus best verified row Best verified: GPT-5.6 Sol · 84.6% | Gap4.5 behind | WeightDisplay only | Provider exact |
| MathVision | Score87.4% | Versus best verified row Best verified: Qwen3.8 Max · 95.2% | Gap7.8 behind | WeightDisplay only | Provider exact |
| V* | Score96.9% | Versus best verified row Best verified: Kimi K2.6 · 96.9% | GapBest verified | WeightDisplay only | Provider exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Feb 1, 2026
Kimi K2.5Score 59.0 · $0.6 / $3
Apr 20, 2026 · you are here
Kimi K2.6Score 60.1 · $0.95 / $4
Jul 16, 2026
Kimi K3Score 80.5 · $3 / $15
Jun 12, 2026
Kimi K2.7 CodeScore 54.3 · $0.95 / $4
Base entry
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
Kimi K2.6 ranks #69 of 226 on the public leaderboard with a score of 60.14/100. It does not yet have enough sourced coverage for a verified position.
Kimi K2.6 is a open weight model with a 256K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value snapshot from MoonshotAI's April 20, 2026 Kimi K2.6 launch sources, supplemented by CursorBench v3.1. BenchLM maps directly comparable rows from the Hugging Face model card and Moonshot's Kimi tech blog. Unsupported internal showcase metrics remain excluded.
Its explicit predecessor is Kimi K2.5. 32 of 406 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Mathematics at #1, while its lowest eligible position is Agentic at #103. particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Radar
Full release historyKimi K2.6 release history
Kimi 2.6 released
Moonshot AI · Model release
Source confirmed
Radar confirmed these at the source. Start the free Radar Brief
Frequently asked questions
How does Kimi K2.6 perform overall in AI benchmarks?
Kimi K2.6 ranks #69 out of 226 models on the public BenchAlign leaderboard, with a score of 60.14/100. Its evidence status is Estimated, and this profile shows 32 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is Kimi K2.6 good for knowledge and understanding?
Kimi K2.6 ranks #55 out of 60 eligible models for knowledge and understanding, with a public category score of 51.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Kimi K2.6 good for coding and programming?
Kimi K2.6 ranks #71 out of 144 eligible models for coding and programming, with a public category score of 51/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Kimi K2.6 good for mathematics?
Kimi K2.6 ranks #1 out of 7 eligible models for mathematics, with a public category score of 71.7/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Kimi K2.6 good for agentic tool use and computer tasks?
Kimi K2.6 ranks #103 out of 138 eligible models for agentic tool use and computer tasks, with a public category score of 41.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Kimi K2.6 good for multimodal and grounded tasks?
Kimi K2.6 ranks #19 out of 35 eligible models for multimodal and grounded tasks, with a public category score of 60.5/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is Kimi K2.6 open source?
Kimi K2.6 is an open-weight model from Moonshot AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Does Kimi K2.6 have full benchmark coverage on BenchLM?
No. Kimi K2.6 currently has 50 source-displayable rows across 406 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of Kimi K2.6?
Kimi K2.6 has a reported context window of 256K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Compare Kimi K2.6 with every tracked model396 comparisons
Last updated August 27, 2026. Runtime fields remain blank until a sourced snapshot exists.
Watch Kimi K2.6 in the weekly brief
Get one weekly email when material rank, price, availability, or benchmark evidence changes are worth revisiting.
Read a sample issueJoin 2,000+ readers.