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GLM 4.7 Flash vs Kimi K2.5

Kimi K2.5 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 23.5 to 14.9, and GLM 4.7 Flash costs 7.1x less per output token ($0.40 vs $2.85 per million). Updated October 21, 2026; 41 benchmarks have a result for both.

Replace GLM 4.7 Flash Replace Kimi K2.5
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
1
Kimi K2.5Moonshot AI
Coding
38.6
Overall
44.8
Output $/M
$2.85
40
vs boards won of 41 shared

GLM 4.7 Flash or Kimi K2.5: which to pick

Only Kimi K2.5 has a coding score, 38.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

Pick GLM 4.7 Flash for output tokens 7.1x cheaper ($0.40 vs $2.85 per million) and work measured by τ²-Bench Telecom, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BKimi K2.5Moonshot AI38.6/1003 independent benchmarks$0.24 per solved task
Shared results
41
GLM 4.7 Flash only
2
Kimi K2.5 only
58
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
GLM 4.7 Flash not benchmarked, Kimi K2.5 38.6 from 3 benchmarks.
Widest category
Reasoning, 20.5 points apart on average over 5 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against Kimi K2.5 78.0%, 36.3 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Kimi K2.5 $2.85 per million output tokens, 7.1x apart.
Context window
GLM 4.7 Flash takes 200K 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
CategoryGLM 4.7 FlashBarsKimi K2.5Ties
Coding03
Intelligence01
Agentic work10
Reasoning05
Math02
Knowledge04
Web design07
General018
All shared boards140
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi K2.5Leader
SWE-bench VerifiedNot measured70.8%Only Kimi K2.5 measured
SWE-bench MultilingualNot measured67.3%Only Kimi K2.5 measured
LMArena WebDevNot measured1437Only Kimi K2.5 measured
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
Terminal-Bench Hard (Artificial Analysis run)22.0%34.8%Kimi K2.5
Terminal-BenchNot measured43.2%Only Kimi K2.5 measured
WeirdMLNot measured45.6%Only Kimi K2.5 measured
ALE-BenchNot measured821.6Only Kimi K2.5 measured
LMArena Text: coding13821483Kimi K2.5
LMArena Text: software and IT13891471Kimi K2.5
LMArena WebDev: ReactNot measured1429Only Kimi K2.5 measured
LMArena WebDev: HTMLNot measured1426Only Kimi K2.5 measured
LMArena WebDev: from an imageNot measured1439Only Kimi K2.5 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi K2.5Leader
AA Intelligence Index14.923.5Kimi K2.5
Epoch Capabilities IndexNot measured148.1Only Kimi K2.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi 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 Telecom98.8%95.9%GLM 4.7 Flash
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 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi 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)58.1%87.9%Kimi K2.5
Humanity's Last Exam (Artificial Analysis run)7.6%30.7%Kimi K2.5
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
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 prompts13561453Kimi K2.5
LMArena Text: expert13571466Kimi K2.5
LMArena Text: hard prompts in English13691460Kimi K2.5
LMArena Vision: diagramsNot measured1282Only Kimi K2.5 measured
LMArena Vision: homeworkNot measured1297Only Kimi K2.5 measured
Kagi LLM BenchmarkNot measured78.5%Only Kimi K2.5 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi K2.5Leader
AIMENot measured95.6%Only Kimi K2.5 measured
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: math13551470Kimi K2.5
LMArena Text: mathematical work13691473Kimi K2.5
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi 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
SimpleQA VerifiedNot measured34.3%Only Kimi K2.5 measured
LMArena Text: science13581459Kimi K2.5
LMArena Text: medicine and healthcare13431448Kimi K2.5
LMArena Text: legal and government13571441Kimi K2.5
LMArena Text: business and finance13431427Kimi K2.5
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi K2.5Leader
Design Arena: websites11941248Kimi K2.5
Design Arena: UI components12081236Kimi K2.5
Design Arena: game dev11351207Kimi K2.5
Design Arena: data viz11231219Kimi K2.5
Design Arena: 3D11211205Kimi K2.5
Design Arena: all code categories11781236Kimi K2.5
Design Arena: SVG10381149Kimi K2.5
Design Arena: ASCII artNot measured1166Only 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 index018
General benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi K2.5Leader
SAGE (Vals AI)Not measured49.9%Only Kimi K2.5 measured
MedScribe (Vals AI)Not measured76.4%Only Kimi K2.5 measured
IFBench60.8%70.2%Kimi K2.5
Long context reasoning (AA-LCR)41.7%78.0%Kimi K2.5
Fiction.LiveBenchNot measured86.1%Only Kimi K2.5 measured
CL-benchNot measured19.3%Only Kimi K2.5 measured
CL-bench LifeNot measured13.2%Only Kimi K2.5 measured
LMArena Text13511445Kimi K2.5
LMArena Text: instruction following13271431Kimi K2.5
LMArena Text: creative writing12961422Kimi K2.5
LMArena VisionNot measured1269Only Kimi K2.5 measured
LMArena Text: English13731453Kimi K2.5
LMArena Text: non-English13301433Kimi K2.5
LMArena Text: Chinese14031494Kimi K2.5
LMArena Text: German13361441Kimi K2.5
LMArena Text: JapaneseNot measured1423Only Kimi K2.5 measured
LMArena Text: Korean12831412Kimi K2.5
LMArena Text: French13321454Kimi K2.5
LMArena Text: Spanish13501450Kimi K2.5
LMArena Text: Russian13331435Kimi K2.5
LMArena Text: Polish12911445Kimi K2.5
LMArena Text: multi-turn13421444Kimi K2.5
LMArena Text: longer queries13451445Kimi K2.5
LMArena Text: writing and language13201425Kimi K2.5
LMArena Text: entertainment and media13041417Kimi 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 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 Flash and Kimi K2.5
BenchmarkGLM 4.7 FlashKimi 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

GLM 4.7 Flash$0.0003Fits in one request
Kimi K2.5$0.0020Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
Kimi K2.5$0.035Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
Kimi K2.5$0.135Fits in one request

GLM 4.7 Flash 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 GLM 4.7 Flash and Kimi K2.5
SpecificationZ.ai GLM 4.7 FlashMoonshotAI Kimi K2.5
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026January 27, 2026
Context window200K262K
Input $/Mtok$0.0653$0.532
Output $/Mtok$0.40$2.85
Coding indexNot benchmarked38.6
Independent measurements behind it03
Cost per solved taskNot computable$0.24
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Kimi K2.5 for coding?

Only Kimi K2.5 has a coding score, 38.6 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 7.1x cheaper ($0.40 vs $2.85 per million) and work measured by τ²-Bench Telecom, where it leads.

Which is cheaper, GLM 4.7 Flash or Kimi K2.5?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Kimi K2.5 costs $0.532 and $2.85. Cost per solved task is known for Kimi K2.5 only.

Which has the bigger context window, GLM 4.7 Flash or Kimi K2.5?

Kimi K2.5, with 262K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs Kimi K2.5 comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 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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