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Superseded.Moonshot AI has newer models in this line:Kimi K2.6

Kimi K2.5

SupersededReleased Feb 1, 2026Open WeightNon-Reasoning256K context

Released Feb 1, 2026 see all recent releases

Decision reading
Kimi K2.5 scores 58.9 out of 100 and ranks #87 of 230. This profile shows 45 source-displayable benchmark rows; its strongest eligible category is Mathematics at #5. API pricing is $0.6 input and $3 output per million tokens.

Data as of September 2, 2026 · How the score is built

Strongest published evidence

Mathematics ranks #5. Particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Validate before choosing

45 published rows leave some tracked benchmark slots empty. Agentic is its lowest eligible category at #118.

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.

Capability

58.9/100

field median 59.1

#87 of 230 ranked models

Price

$0.60input / $3 output

input median $1

blended $1.80

Speed

82tok/s

field median 90 tok/s

First token 38.77 s

Context

256Ktokens

field median 256,000

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 K2.5 category percentile values

  • Agentic18th percentile
  • Coding70th percentile
  • ReasoningNot eligible
  • Knowledge14th percentile
  • Math33rd percentile
  • Multilingual36th percentile
  • MultimodalNot eligible
  • Instruction following81st percentile

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#118/143
  2. Coding#45/148
  3. ReasoningNot ranked
  4. Knowledge#49/57
  5. Math#5/7
  6. Multilingual#8/12
  7. MultimodalNot ranked
  8. Inst. Following#9/43
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.

Explore all models

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

Current modelKimi K2.5 · 58.9 score · $1.80 blended per million tokens
405060708090$0.50$1$5$10$25↘ frontierKimi K2.5

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.

  1. Agentic10/14 verified
  2. Coding7/8 verified
  3. Reasoning1/1 verified
  4. Knowledge4/6 verified
  5. Math8/9 verified
  6. Multilingual0/2 verified
  7. Multimodal1/4 verified
  8. Inst. Following0/1 verified
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 K2.5 needs ~320GB VRAM (4× NVIDIA A100 (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 K2.5
API / mo$2,700
Self-host / mo$5,221
Break-even132M/day
Model the full break-even

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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #118 of 143Percentile 18thWeight 22%14 benchmarksMixed sources39.6
CodingRank #45 of 148Percentile 70thWeight 20%8 benchmarksMixed sources57.0
ReasoningRank Not rankedWeight 17%1 benchmarkVerified44.9
KnowledgeRank #49 of 57Percentile 14thWeight 12%6 benchmarksMixed sources54.6
MathRank #5 of 7Percentile 33rdWeight 5%9 benchmarksMixed sources62.5
MultilingualRank #8 of 12Percentile 36thWeight 7%2 benchmarksReported38.2
MultimodalRank Not rankedWeight 12%4 benchmarksMixed sources61.2
Inst. FollowingRank #9 of 43Percentile 81stWeight 5%1 benchmarkReported91.2

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
Coding benchmark values, best verified comparison, weight, and source status
SWE-RebenchScore58.5%Versus best verified row

Best verified: Claude Opus 4.6 · 65.3%

Gap6.8 behindWeightWeighted 20%
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore76.8%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap19.2 behindWeightWeighted 16%
SciCodeScientific Code BenchmarkScore48.7%Versus best verified row

Best verified: Sakana Fugu · 60.1%

Gap11.4 behindWeightWeighted 16%
SWE-bench ProScore50.7%Versus best verified row

Best verified: Claude Fable 5.1 · 81.2%

Gap30.5 behindWeightWeighted 10%
SWE-bench Verified*SWE-bench Verified (mini-swe-agent-v2)Score70.8%Versus best verified rowGapNo verified comparatorWeightDisplay only
LiveCodeBench v6Score85.0%Versus best verified row

Best verified: Sakana Fugu-Ultra · 93.2%

Gap8.2 behindWeightDisplay only
SWE MultilingualScore73%Versus best verified row

Best verified: Claude Opus 5 · 89.5%

Gap16.5 behindWeightDisplay only
React Native EvalsScore77.2%Versus best verified row

Best verified: Composer 2 · 96.1%

Gap18.9 behindWeightDisplay only
Agentic14 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.0Score50.8%Versus best verified row

Best verified: GPT-5.6 Sol · 91.9%

Gap41.1 behindWeightWeighted 38%
BrowseCompScore60.6%Versus best verified row

Best verified: GPT-5.6 Sol · 92.2%

Gap31.6 behindWeightWeighted 28%
Claw-EvalScore52.3%Versus best verified row

Best verified: Ornith-1.5-397B · 81.4%

Gap29.1 behindWeightDisplay only
Benchmark exact
QwenClawBenchScore54.3%Versus best verified row

Best verified: Qwen3.7 Max · 64.3%

Gap10 behindWeightDisplay only
τ³-bench resultsτ³-Bench Tool-Agent-User EvaluationScore65.7%Versus best verified row

Best verified: Mistral Medium 3.5 128B · 91.4%

Gap25.7 behindWeightDisplay only
DeepSearchQAScore77.1%Versus best verified row

Best verified: Claude Opus 5 · 95.0%

Gap17.9 behindWeightDisplay only
DeepPlanningScore14.4%Versus best verified row

Best verified: Qwen3.7 Plus · 62.3%

Gap47.9 behindWeightDisplay only
ToolathlonScore27.8%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap47.8 behindWeightDisplay only
MCP AtlasScore29.5%Versus best verified row

Best verified: Muse Spark 1.1 · 88.1%

Gap58.6 behindWeightDisplay only
MCP-TasksScore59.1%Versus best verified row

Best verified: Qwen3.5 397B · 74.2%

Gap15.1 behindWeightDisplay only
WideResearchScore72.7%Versus best verified row

Best verified: Hy4 preview · 83.9%

Gap11.2 behindWeightDisplay only
Gert LabsGert Labs Composite Game BenchmarkScore45.88%Versus best verified row

Best verified: Claude Opus 4.8 · 72.97%

Gap27.1 behindWeightDisplay only
Benchmark exact
ResearchClawBenchScore14.0%Versus best verified row

Best verified: Claude Opus 4.8 · 21.1%

Gap7.1 behindWeightDisplay only
JobBenchScore8.7%Versus best verified row

Best verified: Muse Spark 1.3 · 64.9%

Gap56.2 behindWeightDisplay only
Benchmark exact
Reasoning1 row
Reasoning benchmark values, best verified comparison, weight, and source status
LongBench v2Score61%Versus best verified row

Best verified: Qwen3.8 Max · 66.3%

Gap5.3 behindWeightWeighted 38%
Knowledge6 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore30.1%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap34.9 behindWeightWeighted 45%
MMLU-ProMassive Multitask Language Understanding ProfessionalScore87.1%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap2.5 behindWeightWeighted 30%
GPQAGraduate-Level Google-Proof Q&AScore87.6%Versus best verified row

Best verified: Sakana Fugu-Ultra · 95.5%

Gap7.9 behindWeightWeighted 7%
SuperGPQASuperGPQA: Scaling LLM Evaluation Across 285 Graduate DisciplinesScore69.2%Versus best verified row

Best verified: Qwen 3.6 Max (preview) · 73.9%

Gap4.7 behindWeightWeighted 7%
GPQA-DGPQA DiamondScore87.6%Versus best verified row

Best verified: Sakana Fugu-Ultra · 95.5%

Gap7.9 behindWeightDisplay only
MMLU-Pro (Arcee)MMLU-Pro first-party comparison snapshotScore87.1%Versus best verified row

Best verified: Trinity-Large-Preview · 75.2%

Gap11.9 behindWeightDisplay only
Math9 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score27.900%Versus best verified row

Best verified: GPT-5.6 Sol · 89.000%

Gap61.1 behindWeightWeighted 30%
AIME26AIME 2026Score95.8%Versus best verified row

Best verified: GLM-5.2 · 99.2%

Gap3.4 behindWeightWeighted 25%
HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026Score87.1%Versus best verified row

Best verified: Qwen3.7 Max · 97.1%

Gap10 behindWeightWeighted 25%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score4.200%Versus best verified row

Best verified: GPT-5.6 Sol · 83.000%

Gap78.8 behindWeightWeighted 10%
AIME 2025American Invitational Mathematics Examination 2025Score96.1%Versus best verified row

Best verified: MAI-Thinking-1 · 97%

Gap0.9 behindWeightDisplay only
AIME25 (Arcee)AIME25 first-party comparison snapshotScore96.3%Versus best verified row

Best verified: Trinity-Large-Preview · 24.0%

Gap72.3 behindWeightDisplay only
HMMT Feb 2025Harvard-MIT Mathematics Tournament February 2025Score95.4%Versus best verified row

Best verified: Qwen3.6 Plus · 96.7%

Gap1.3 behindWeightDisplay only
HMMT Nov 2025Harvard-MIT Mathematics Tournament November 2025Score91.1%Versus best verified row

Best verified: Qwen3.6 Plus · 94.6%

Gap3.5 behindWeightDisplay only
MMAnswerBenchScore81.8%Versus best verified row

Best verified: GLM-5.2 · 91.0%

Gap9.2 behindWeightDisplay only
Multilingual2 rows
Multilingual benchmark values, best verified comparison, weight, and source status
MMLU-ProXScore82.3%Versus best verified row

Best verified: Qwen3.7 Max · 87%

Gap4.7 behindWeightWeighted 100%
NOVA-63Score56.0%Versus best verified row

Best verified: Qwen3.5 397B · 59.1%

Gap3.1 behindWeightDisplay only
Multimodal4 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMU-ProMassive Multi-discipline Multimodal Understanding ProScore78.5%Versus best verified row

Best verified: GPT-5.4 Pro · 94%

Gap15.5 behindWeightWeighted 45%
Video-MMEScore87.4%Versus best verified rowGapNo verified comparatorWeightDisplay only
MMVUMultimodal Multi-disciplinary Video UnderstandingScore80.4%Versus best verified row

Best verified: Qwen3.8 Max · 82.4%

Gap2 behindWeightDisplay only
VideoMMMUScore86.6%Versus best verified row

Best verified: Qwen3.8 Max · 88.7%

Gap2.1 behindWeightDisplay only
Inst. Following1 row
Inst. Following benchmark values, best verified comparison, weight, and source status
IFEvalInstruction-Following EvalScore93.9%Versus best verified row

Best verified: Qwen3.5-27B · 95%

Gap1.1 behindWeightWeighted 35%

Lineage

The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Feb 1, 2026 · you are here

    Kimi K2.5

    Score 58.9 · $0.6 / $3

  2. Apr 20, 2026

    Kimi K2.6

    Score 59.2 · $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 K2.5 ranks #87 of 230 on the public leaderboard with a score of 58.87/100. Its source-verified position is #46 of 105.

Kimi K2.5 is a open weight model with a 256K context window. No explicit reasoning mode is documented in this profile.

Kimi K2.5 sits in the Kimi K2.5 family with Kimi K2.5 (Reasoning). 45 of 416 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Mathematics at #5, while its lowest eligible position is Agentic at #118. particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Frequently asked questions

How does Kimi K2.5 perform overall in AI benchmarks?

Kimi K2.5 ranks #87 out of 230 models on the public BenchAlign leaderboard, with a score of 58.87/100. Its evidence status is Supported, and this profile shows 45 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is Kimi K2.5 good for knowledge and understanding?

Kimi K2.5 ranks #49 out of 57 eligible models for knowledge and understanding, with a public category score of 54.6/100. 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 K2.5 good for coding and programming?

Kimi K2.5 ranks #45 out of 148 eligible models for coding and programming, with a public category score of 57/100. 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 K2.5 good for mathematics?

Kimi K2.5 ranks #5 out of 7 eligible models for mathematics, with a public category score of 62.5/100. 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 K2.5 good for reasoning and logic?

Kimi K2.5 has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Kimi K2.5 good for agentic tool use and computer tasks?

Kimi K2.5 ranks #118 out of 143 eligible models for agentic tool use and computer tasks, with a public category score of 39.6/100. 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 K2.5 good for multimodal and grounded tasks?

Kimi K2.5 has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Kimi K2.5 good for instruction following?

Kimi K2.5 ranks #9 out of 43 eligible models for instruction following, with a public category score of 91.2/100. 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 K2.5 good for multilingual tasks?

Kimi K2.5 ranks #8 out of 12 eligible models for multilingual tasks, with a public category score of 38.2/100. 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 K2.5 open source?

Kimi K2.5 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.

Which sibling models are related to Kimi K2.5?

Kimi K2.5 belongs to the Kimi K2.5 family. Related tracked variants include Kimi K2.5 (Reasoning). A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does Kimi K2.5 have full benchmark coverage on BenchLM?

No. Kimi K2.5 currently has 63 source-displayable rows across 416 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 K2.5?

Kimi K2.5 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 K2.5 with every tracked model409 comparisons

Last updated September 2, 2026. Runtime fields remain blank until a sourced snapshot exists.

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