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GPT-5 vs Kimi K2.5

GPT-5 leads Kimi K2.5 on coding, 38.8 to 38.6 on the Vibe Coding Index, and Kimi K2.5 costs 3.5x less per output token ($2.85 vs $10.00 per million). Updated October 21, 2026; 82 benchmarks have a result for both.

Replace GPT-5 Replace Kimi K2.5
GPT-5OpenAI
Coding
38.8
Overall
51.5
Output $/M
$10.00
22
Kimi K2.5Moonshot AI
Coding
38.6
Overall
44.8
Output $/M
$2.85
60
vs boards won of 82 shared

GPT-5 or Kimi K2.5: which to pick

GPT-5 and Kimi K2.5 are level for coding, 38.8 against 38.6 on the Vibe Coding Index, so price and context decide.

Pick Kimi K2.5 for output tokens 3.5x cheaper ($2.85 vs $10.00 per million) and a lower cost per solved task ($0.24 against $0.39). Pick GPT-5 for a larger context window (400K against 262K tokens) and work measured by SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.

Model AGPT-5OpenAI38.8/1003 independent benchmarks$0.39 per solved taskModel BKimi K2.5Moonshot AI38.6/1003 independent benchmarks$0.24 per solved task
Shared results
82
GPT-5 only
31
Kimi K2.5 only
17
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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
GPT-5 38.8 from 3 benchmarks, Kimi K2.5 38.6 from 3 benchmarks.
Widest category
Agentic work, 9.4 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: 3D: GPT-5 1056 against Kimi K2.5 1205, 18.6 points apart on a 0 to 100 scale.
Output price
GPT-5 $10.00 against Kimi K2.5 $2.85 per million output tokens, 3.5x apart.
Context window
GPT-5 takes 400K 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
CategoryGPT-5BarsKimi K2.5Ties
Coding76
Intelligence11
Agentic work01
Reasoning310
Math03
Knowledge57
Web design26
General426
All shared boards2260
CodingLike-for-likeSix of these boards feed the Vibe Coding Index76
Coding benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
Aider polyglot88.0%Not measuredOnly GPT-5 measured
SWE-bench Verified74.4%70.8%GPT-5
SWE-bench MultilingualNot measured67.3%Only Kimi K2.5 measured
LMArena WebDev14181437Kimi K2.5
AA Coding Index37.846.8Kimi K2.5
LiveCodeBench (Vals AI)85.9%83.9%GPT-5
SWE-bench Verified (Vals AI)69.0%70.0%Kimi K2.5
Vibe Code Bench (Vals AI)20.1%17.5%GPT-5
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)84.6%Not measuredOnly GPT-5 measured
Terminal-Bench Hard (Artificial Analysis run)37.9%34.8%GPT-5
Terminal-Bench49.6%43.2%GPT-5
GSO-Bench6.9%Not measuredOnly GPT-5 measured
WeirdML60.7%45.6%GPT-5
ALE-Bench807.6821.6Kimi K2.5
LMArena Text: coding14361483Kimi K2.5
LMArena Text: software and IT14201471Kimi K2.5
LMArena WebDev: ReactNot measured1429Only Kimi K2.5 measured
LMArena WebDev: HTML14271426GPT-5
LMArena WebDev: from an imageNot measured1439Only Kimi K2.5 measured
SciCode0.0%Not measuredOnly GPT-5 measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
AA Intelligence Index23.023.5Kimi K2.5
Epoch Capabilities Index150.0148.1GPT-5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi 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 measured1118Only 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 Telecom86.5%95.9%Kimi K2.5
METR time horizons69.6%Not measuredOnly GPT-5 measured
Remote Labor Index1.7%Not measuredOnly GPT-5 measured
GDPval34.8%Not measuredOnly GPT-5 measured
BALROG32.8%Not measuredOnly GPT-5 measured
DeepResearch Bench49.6%Not measuredOnly GPT-5 measured
OSWorldNot measured63.3%Only Kimi K2.5 measured
Vending-Bench 2Not measured$1,198Only Kimi K2.5 measured
BrowseComp-Plus80.0%Not measuredOnly GPT-5 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index310
Reasoning benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
GPQA Diamond85.6%84.1%GPT-5
MMMU Pro (Vals AI)81.5%84.3%Kimi K2.5
GPQA Diamond (Artificial Analysis run)85.4%87.9%Kimi K2.5
Humanity's Last Exam (Artificial Analysis run)28.5%30.7%Kimi K2.5
GPQA Diamond (Epoch AI run)86.2%Not measuredOnly GPT-5 measured
ARC-AGI65.7%65.3%GPT-5
ARC-AGI-29.9%11.8%Kimi K2.5
SimpleBench56.7%46.8%GPT-5
Chess puzzles36.0%Not measuredOnly GPT-5 measured
Mystery game puzzles23.0%Not measuredOnly GPT-5 measured
EBR-bench12.7%Not measuredOnly GPT-5 measured
VPCT66.0%Not measuredOnly GPT-5 measured
LMArena Text: hard prompts14151453Kimi K2.5
LMArena Text: expert14191466Kimi K2.5
LMArena Text: hard prompts in English14191460Kimi K2.5
LMArena Vision: diagrams12521282Kimi K2.5
LMArena Vision: homework12781297Kimi K2.5
Kagi LLM Benchmark72.7%78.5%Kimi K2.5
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
AIME93.4%95.6%Kimi K2.5
MATH 500 (Vals AI)96.0%Not measuredOnly GPT-5 measured
MGSM (Vals AI)92.8%Not measuredOnly GPT-5 measured
AA Math Index94.3Not measuredOnly GPT-5 measured
AIME 2025 (Artificial Analysis run)94.3%Not measuredOnly GPT-5 measured
OTIS Mock AIME91.4%Not measuredOnly GPT-5 measured
FrontierMath, tiers 1 to 355.4%Not measuredOnly GPT-5 measured
FrontierMath, tier 422.0%Not measuredOnly GPT-5 measured
ProofBench18.0%Not measuredOnly GPT-5 measured
MATH level 598.1%Not measuredOnly GPT-5 measured
LMArena Text: math14071470Kimi K2.5
LMArena Text: mathematical work14051473Kimi K2.5
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index57
Knowledge benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
MMLU Pro86.5%85.9%GPT-5
LegalBench (Vals AI)86.0%Not measuredOnly GPT-5 measured
CaseLaw (Vals AI)66.5%58.7%GPT-5
CorpFin (Vals AI)61.1%68.3%Kimi K2.5
TaxEval (Vals AI)73.4%74.2%Kimi K2.5
MortgageTax (Vals AI)65.5%66.5%Kimi K2.5
MedQA (Vals AI)96.3%94.4%GPT-5
MedCode (Vals AI)49.6%39.3%GPT-5
MMLU Pro (Artificial Analysis run)87.1%Not measuredOnly GPT-5 measured
SimpleQA Verified50.1%34.3%GPT-5
GeoBench81.0%Not measuredOnly GPT-5 measured
LMArena Text: science14161459Kimi K2.5
LMArena Text: medicine and healthcare14131448Kimi K2.5
LMArena Text: legal and government14311441Kimi K2.5
LMArena Text: business and finance14001427Kimi K2.5
FACTS Benchmark Suite56.5%Not measuredOnly GPT-5 measured
Neither model has a result on the other 2 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
Design Arena: websites11841248Kimi K2.5
Design Arena: UI components11801236Kimi K2.5
Design Arena: game dev11851207Kimi K2.5
Design Arena: data viz12211219GPT-5
Design Arena: 3D10561205Kimi K2.5
Design Arena: all code categories11681236Kimi K2.5
Design Arena: SVG11891149GPT-5
Design Arena: ASCII art11411166Kimi K2.5
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index426
General benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi K2.5Leader
SAGE (Vals AI)43.7%49.9%Kimi K2.5
MedScribe (Vals AI)83.7%76.4%GPT-5
IFBench73.1%70.2%GPT-5
Long context reasoning (AA-LCR)78.2%78.0%GPT-5
DTBench90.7%Not measuredOnly GPT-5 measured
LMCA40.0%Not measuredOnly GPT-5 measured
Fiction.LiveBench97.2%86.1%GPT-5
Creative writing (Lech Mazur)86.0%Not measuredOnly GPT-5 measured
CL-benchNot measured19.3%Only Kimi K2.5 measured
CL-bench LifeNot measured13.2%Only Kimi K2.5 measured
LMArena Text14061445Kimi K2.5
LMArena Text: instruction following13881431Kimi K2.5
LMArena Text: creative writing13651422Kimi K2.5
LMArena Vision12321269Kimi K2.5
LMArena Text: English14091453Kimi K2.5
LMArena Text: non-English13971433Kimi K2.5
LMArena Text: Chinese14221494Kimi K2.5
LMArena Text: German14161441Kimi K2.5
LMArena Text: Japanese14091423Kimi K2.5
LMArena Text: Korean13601412Kimi K2.5
LMArena Text: French14091454Kimi K2.5
LMArena Text: Spanish13991450Kimi K2.5
LMArena Text: Russian14071435Kimi K2.5
LMArena Text: Polish14311445Kimi K2.5
LMArena Text: multi-turn14261444Kimi K2.5
LMArena Text: longer queries13991445Kimi K2.5
LMArena Text: writing and language13841425Kimi K2.5
LMArena Text: entertainment and media13791417Kimi K2.5
LMArena Vision: English12311274Kimi K2.5
LMArena Vision: Chinese12671328Kimi K2.5
LMArena Vision: reading text12461276Kimi K2.5
LMArena Vision: humor12191284Kimi K2.5
LMArena Vision: captioning12121257Kimi K2.5
LMArena Vision: recognizing things12631285Kimi K2.5
LMArena Vision: creative writing12101269Kimi K2.5
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5 and Kimi K2.5
BenchmarkGPT-5Kimi 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

GPT-5$0.0063Fits in one request
Kimi K2.5$0.0020Fits in one request

Kimi K2.5 costs less

Repository review

50K input + 3K output tokens

GPT-5$0.092Fits in one request
Kimi K2.5$0.035Fits in one request

Kimi K2.5 costs less

Agent loop

200K input + 10K output tokens

GPT-5$0.350Fits in one request
Kimi K2.5$0.135Fits in one request

Kimi K2.5 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 GPT-5 and Kimi K2.5
SpecificationOpenAI GPT-5MoonshotAI Kimi K2.5
MakerOpenAIMoonshot AI
WeightsNo public weights listedOpen weights
ReleasedAugust 7, 2025January 27, 2026
Context window400K262K
Input $/Mtok$1.25$0.532
Output $/Mtok$10.00$2.85
Coding index38.838.6
Independent measurements behind it33
Cost per solved task$0.39$0.24
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 better than Kimi K2.5 for coding?

GPT-5 and Kimi K2.5 are level for coding, 38.8 against 38.6 on the Vibe Coding Index, so price and context decide. Pick Kimi K2.5 for output tokens 3.5x cheaper ($2.85 vs $10.00 per million) and a lower cost per solved task ($0.24 against $0.39). Pick GPT-5 for a larger context window (400K against 262K tokens) and work measured by SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GPT-5 or Kimi K2.5?

GPT-5 costs $1.25 per million input tokens and $10.00 per million output tokens; Kimi K2.5 costs $0.532 and $2.85. Per solved SWE-bench Verified task, GPT-5 costs $0.39 and Kimi K2.5 $0.24.

Which has the bigger context window, GPT-5 or Kimi K2.5?

GPT-5, with 400K tokens against 262K.

How many benchmarks is the GPT-5 vs Kimi K2.5 comparison based on?

82 benchmarks have a result for both models as of October 21, 2026. GPT-5's coding index rests on 3 independent benchmarks and Kimi K2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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