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GLM 4.7 Flash vs Kimi K2 0905

Kimi K2 0905 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 15.3 to 14.9, and GLM 4.7 Flash costs 6.3x less per output token ($0.40 vs $2.50 per million). Updated October 22, 2026; 9 benchmarks have a result for both.

GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
5
Kimi K2 0905Moonshot AI
Coding
No data
Overall
No data
Output $/M
$2.50
4
vs boards won of 9 shared

GLM 4.7 Flash or Kimi K2 0905: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.7 Flash for output tokens 6.3x cheaper ($0.40 vs $2.50 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Shared results
9
GLM 4.7 Flash only
34
Kimi K2 0905 only
8
Like-for-like categories
6 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 0905 not benchmarked.
Widest category
Agentic work, 25.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.7 Flash 98.8% against Kimi K2 0905 73.4%, 25.4 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Kimi K2 0905 $2.50 per million output tokens, 6.3x apart.
Context window
GLM 4.7 Flash takes 200K tokens, Kimi K2 0905 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 0905Ties
Coding01
Intelligence01
Agentic work10
Reasoning11
Web design20
General11
All shared boards54
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
LiveCodeBench (Artificial Analysis run)Not measured61.0%Only Kimi K2 0905 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%23.5%Kimi K2 0905
WeirdMLNot measured36.7%Only Kimi K2 0905 measured
ALE-BenchNot measured267.1Only Kimi K2 0905 measured
LMArena Text: coding1382Not measuredOnly GLM 4.7 Flash measured
LMArena Text: software and IT1389Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
AA Intelligence Index14.915.3Kimi K2 0905
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
τ²-Bench Telecom98.8%73.4%GLM 4.7 Flash
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
Reasoning benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
GPQA Diamond (Artificial Analysis run)58.1%76.7%Kimi K2 0905
Humanity's Last Exam (Artificial Analysis run)7.6%6.4%GLM 4.7 Flash
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts1356Not measuredOnly GLM 4.7 Flash measured
LMArena Text: expert1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts in English1369Not measuredOnly GLM 4.7 Flash measured
Kagi LLM BenchmarkNot measured52.2%Only Kimi K2 0905 measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Math benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
AA Math IndexNot measured57.3Only Kimi K2 0905 measured
AIME 2025 (Artificial Analysis run)Not measured57.3%Only Kimi K2 0905 measured
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: math1355Not measuredOnly GLM 4.7 Flash measured
LMArena Text: mathematical work1369Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Knowledge benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
MMLU Pro (Artificial Analysis run)Not measured81.9%Only Kimi K2 0905 measured
LMArena Text: science1358Not measuredOnly GLM 4.7 Flash measured
LMArena Text: medicine and healthcare1343Not measuredOnly GLM 4.7 Flash measured
LMArena Text: legal and government1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: business and finance1343Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index20
Web design benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
Design Arena: websites11941088GLM 4.7 Flash
Design Arena: UI components1205Not measuredOnly GLM 4.7 Flash measured
Design Arena: game dev1134Not measuredOnly GLM 4.7 Flash measured
Design Arena: data viz1123Not measuredOnly GLM 4.7 Flash measured
Design Arena: 3D1120Not measuredOnly GLM 4.7 Flash measured
Design Arena: all code categories11771098GLM 4.7 Flash
Design Arena: SVG1037Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
General benchmarks for GLM 4.7 Flash and Kimi K2 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
IFBench60.8%41.7%GLM 4.7 Flash
Long context reasoning (AA-LCR)41.7%53.0%Kimi K2 0905
Fiction.LiveBenchNot measured66.7%Only Kimi K2 0905 measured
LMArena Text1351Not measuredOnly GLM 4.7 Flash measured
LMArena Text: instruction following1327Not measuredOnly GLM 4.7 Flash measured
LMArena Text: creative writing1296Not measuredOnly GLM 4.7 Flash measured
LMArena Text: English1373Not measuredOnly GLM 4.7 Flash measured
LMArena Text: non-English1330Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Chinese1403Not measuredOnly GLM 4.7 Flash measured
LMArena Text: German1336Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Korean1283Not measuredOnly GLM 4.7 Flash measured
LMArena Text: French1332Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Spanish1350Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Russian1333Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn1342Not measuredOnly GLM 4.7 Flash measured
LMArena Text: longer queries1345Not measuredOnly GLM 4.7 Flash measured
LMArena Text: writing and language1320Not measuredOnly GLM 4.7 Flash measured
LMArena Text: entertainment and media1304Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 27 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 0905
BenchmarkGLM 4.7 FlashKimi K2 0905Leader
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 0905$0.0019Fits 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 0905$0.037Fits 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 0905$0.145Fits 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 0905
SpecificationZ.ai GLM 4.7 FlashMoonshotAI Kimi K2 0905
MakerZ.aiMoonshot AI
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026September 4, 2025
Context window200K262K
Input $/Mtok$0.0653$0.60
Output $/Mtok$0.40$2.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Kimi K2 0905 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 6.3x cheaper ($0.40 vs $2.50 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Which is cheaper, GLM 4.7 Flash or Kimi K2 0905?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Kimi K2 0905?

Kimi K2 0905, with 262K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs Kimi K2 0905 comparison based on?

9 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.

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