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DeepSeek V3.2 Exp vs Kimi K2.5

Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Exp Replace Kimi K2.5
DeepSeek V3.2 ExpDeepSeek
Coding
55.0
Overall
16.6
Output $/M
$0.41
1
Kimi K2.5Moonshot AI
Coding
58.5
Overall
23.5
Output $/M
$2.50
48
vs boards won of 49 shared

Decision reading

Pick Kimi K2.5 for coding: it scores 58.5 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp.

Pick DeepSeek V3.2 Exp when output tokens cost 6.1x less, or when the benchmark that matters is LMArena Text: medicine and healthcare, where it leads.

Model ADeepSeek V3.2 ExpDeepSeek55.0/1002 independent benchmarksNo cost per solved taskModel BKimi K2.5Moonshot AI58.5/1002 independent benchmarks$0.24 per solved task
Shared results
49
DeepSeek V3.2 Exp only
8
Kimi K2.5 only
49
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
DeepSeek V3.2 Exp 55.0 from 2 benchmarks, Kimi K2.5 58.5 from 2 benchmarks.
Widest category
Agentic work, 62.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against Kimi K2.5 95.9%, 62.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Kimi K2.5 $2.50 per million output tokens, 6.1x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Kimi K2.5 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryDeepSeek V3.2 ExpBarsKimi K2.5Ties
Coding06
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge13
Web design07
General022
All shared boards148
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured70.8%Only Kimi K2.5 measured
SWE-bench MultilingualNot measured67.3%Only Kimi K2.5 measured
LMArena WebDev12721437Kimi K2.5
AA Coding IndexNot measured46.8Only Kimi K2.5 measured
LiveCodeBench (Vals AI)Not measured83.9%Only Kimi K2.5 measured
SWE-bench Verified (Vals AI)Not measured70.0%Only Kimi K2.5 measured
Vibe Code Bench (Vals AI)Not measured17.5%Only Kimi K2.5 measured
Code migration (Vals AI)Not measured7.0%Only Kimi K2.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured42.0%Only Kimi K2.5 measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly DeepSeek V3.2 Exp measured
Terminal-Bench Hard (Artificial Analysis run)31.1%34.8%Kimi K2.5
Terminal-BenchNot measured43.2%Only Kimi K2.5 measured
WeirdML39.5%45.6%Kimi K2.5
ALE-BenchNot measured821.6Only Kimi K2.5 measured
LMArena Text: coding14381483Kimi K2.5
LMArena Text: software and IT14381471Kimi K2.5
LMArena WebDev: ReactNot measured1429Only Kimi K2.5 measured
LMArena WebDev: HTML12811426Kimi K2.5
LMArena WebDev: from an imageNot measured1439Only Kimi K2.5 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
AA Intelligence Index16.623.5Kimi K2.5
Epoch Capabilities Index144.9148.1Kimi K2.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
Design Arena: agents building web appsNot measured1136Only Kimi K2.5 measured
Design Arena: agents building full-stack appsNot measured1114Only Kimi K2.5 measured
Design Arena: agents building mobile appsNot measured1147Only Kimi K2.5 measured
Design Arena: agents building Android appsNot measured1124Only Kimi K2.5 measured
Design Arena: agents building Godot gamesNot measured1219Only Kimi K2.5 measured
Finance Agent (Vals AI)Not measured35.8%Only Kimi K2.5 measured
Legal Research Bench (Vals AI)Not measured15.9%Only Kimi K2.5 measured
Excel Modeling Benchmark (Vals AI)Not measured28.5%Only Kimi K2.5 measured
τ²-Bench Telecom33.9%95.9%Kimi K2.5
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
OSWorldNot measured63.3%Only Kimi K2.5 measured
Vending-Bench 2Not measured$1,198Only Kimi K2.5 measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
GPQA DiamondNot measured84.1%Only Kimi K2.5 measured
MMMU Pro (Vals AI)Not measured84.3%Only Kimi K2.5 measured
GPQA Diamond (Artificial Analysis run)79.7%87.9%Kimi K2.5
Humanity's Last Exam (Artificial Analysis run)14.9%30.7%Kimi K2.5
ARC-AGINot measured65.3%Only Kimi K2.5 measured
ARC-AGI-2Not measured11.8%Only Kimi K2.5 measured
SimpleBenchNot measured46.8%Only Kimi K2.5 measured
LMArena Text: hard prompts14291453Kimi K2.5
LMArena Text: expert14221466Kimi K2.5
LMArena Text: hard prompts in English14441460Kimi K2.5
LMArena Vision: diagramsNot measured1282Only Kimi K2.5 measured
LMArena Vision: homeworkNot measured1297Only Kimi K2.5 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
AIMENot measured95.6%Only Kimi K2.5 measured
AA Math Index87.7Not measuredOnly DeepSeek V3.2 Exp measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: math14221470Kimi K2.5
LMArena Text: mathematical work14331473Kimi K2.5
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
MMLU ProNot measured85.9%Only Kimi K2.5 measured
CaseLaw (Vals AI)Not measured58.7%Only Kimi K2.5 measured
CorpFin (Vals AI)Not measured68.3%Only Kimi K2.5 measured
TaxEval (Vals AI)Not measured74.2%Only Kimi K2.5 measured
MortgageTax (Vals AI)Not measured66.5%Only Kimi K2.5 measured
MedQA (Vals AI)Not measured94.4%Only Kimi K2.5 measured
MedCode (Vals AI)Not measured39.3%Only Kimi K2.5 measured
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
SimpleQA VerifiedNot measured34.3%Only Kimi K2.5 measured
LMArena Text: science14431459Kimi K2.5
LMArena Text: medicine and healthcare14681448DeepSeek V3.2 Exp
LMArena Text: legal and government14191441Kimi K2.5
LMArena Text: business and finance14121427Kimi K2.5
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
Design Arena: websites11791249Kimi K2.5
Design Arena: UI components11661236Kimi K2.5
Design Arena: game dev11451207Kimi K2.5
Design Arena: data viz11521219Kimi K2.5
Design Arena: 3D11481205Kimi K2.5
Design Arena: all code categories11711236Kimi K2.5
Design Arena: SVG10311149Kimi K2.5
Design Arena: ASCII artNot measured1165Only Kimi K2.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index022
General benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
SAGE (Vals AI)Not measured49.9%Only Kimi K2.5 measured
MedScribe (Vals AI)Not measured76.4%Only Kimi K2.5 measured
IFBench54.1%70.2%Kimi K2.5
Long context reasoning (AA-LCR)72.3%78.0%Kimi K2.5
DTBench87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMCA29.1%Not measuredOnly DeepSeek V3.2 Exp measured
Fiction.LiveBench83.3%86.1%Kimi K2.5
CL-bench13.2%19.3%Kimi K2.5
CL-bench Life9.5%13.2%Kimi K2.5
LMArena Text14251445Kimi K2.5
LMArena Text: instruction following14011431Kimi K2.5
LMArena Text: creative writing14041422Kimi K2.5
LMArena VisionNot measured1269Only Kimi K2.5 measured
LMArena Text: English14411453Kimi K2.5
LMArena Text: non-English14041433Kimi K2.5
LMArena Text: Chinese14611494Kimi K2.5
LMArena Text: German14401441Kimi K2.5
LMArena Text: Japanese13311423Kimi K2.5
LMArena Text: Korean13721412Kimi K2.5
LMArena Text: French14341454Kimi K2.5
LMArena Text: Spanish14411450Kimi K2.5
LMArena Text: Russian14121435Kimi K2.5
LMArena Text: Polish14351445Kimi K2.5
LMArena Text: multi-turn14221444Kimi K2.5
LMArena Text: longer queries14181445Kimi K2.5
LMArena Text: writing and language14071425Kimi K2.5
LMArena Text: entertainment and media14071417Kimi K2.5
LMArena Vision: EnglishNot measured1274Only Kimi K2.5 measured
LMArena Vision: ChineseNot measured1328Only Kimi K2.5 measured
LMArena Vision: reading textNot measured1276Only Kimi K2.5 measured
LMArena Vision: humorNot measured1284Only Kimi K2.5 measured
LMArena Vision: captioningNot measured1257Only Kimi K2.5 measured
LMArena Vision: recognizing thingsNot measured1285Only Kimi K2.5 measured
LMArena Vision: creative writingNot measured1269Only Kimi K2.5 measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and Kimi K2.5
BenchmarkDeepSeek V3.2 ExpKimi K2.5Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

DeepSeek V3.2 Exp$0.0005Fits in one request
Kimi K2.5$0.0018Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Kimi K2.5$0.033Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Kimi K2.5$0.125Fits in one request

DeepSeek V3.2 Exp costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of DeepSeek V3.2 Exp and Kimi K2.5
SpecificationDeepSeek V3.2 ExpMoonshotAI Kimi K2.5
MakerDeepSeekMoonshot AI
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025January 27, 2026
Context window164K262K
Input $/Mtok$0.27$0.50
Output $/Mtok$0.41$2.50
Coding index55.058.5
Independent measurements behind it22
Cost per solved taskNot computable$0.24
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Kimi K2.5 for coding?

Pick Kimi K2.5 for coding: it scores 58.5 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp. Pick DeepSeek V3.2 Exp when output tokens cost 6.1x less, or when the benchmark that matters is LMArena Text: medicine and healthcare, where it leads.

Which is cheaper, DeepSeek V3.2 Exp or Kimi K2.5?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Kimi K2.5 costs $0.50 and $2.50. Cost per solved task is known for Kimi K2.5 only.

Which has the bigger context window, DeepSeek V3.2 Exp or Kimi K2.5?

Kimi K2.5, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs Kimi K2.5 comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and Kimi K2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.

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