GLM 4.7 Flash vs Kimi K2 0711

Updated October 21, 2026. 12 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace Kimi K2 0711

GLM 4.7 FlashZ.ai

Coding: No data
Overall: 14.9
Output $/M: $0.40

Kimi K2 0711Moonshot AI

Coding: No data
Overall: 12.7
Output $/M: $2.30

vs boards won of 12 shared

Decision reading

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

GLM 4.7 Flash has the edge where output tokens cost 5.7x less, and where the context window is larger, 200K against 131K tokens.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task Model BKimi K2 0711Moonshot AINot benchmarkedNo coding benchmarkNo cost per solved task

123124

Shared results: 12
GLM 4.7 Flash only: 31
Kimi K2 0711 only: 24
Like-for-like categories: 6 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.

Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 against 12.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2 0711 has a GPQA Diamond result, 71.5%.
Directional only
Math
AIME
No call
Only Kimi K2 0711 has a AIME result, 62.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 0711 has a MMLU Pro result, 79.4%.
Directional only
Web design
Design Arena: websites
GLM 4.7 Flash
GLM 4.7 Flash leads, 1194 against 1050.
Same board, both measured
Long documents
Listed context window
GLM 4.7 Flash
GLM 4.7 Flash takes 200K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, Kimi K2 0711 $0.0017 per turn.
List prices

At a glance

Coding index: GLM 4.7 Flash not benchmarked, Kimi K2 0711 not benchmarked.
Widest category: Agentic work, 37.7 points apart on average over 1 shared benchmark.
Widest single gap: τ²-Bench Telecom: GLM 4.7 Flash 98.8% against Kimi K2 0711 61.1%, 37.7 points apart on a 0 to 100 scale.
Output price: GLM 4.7 Flash $0.40 against Kimi K2 0711 $2.30 per million output tokens, 5.7x apart.
Context window: GLM 4.7 Flash takes 200K tokens, Kimi K2 0711 131K.

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
Category	GLM 4.7 Flash	Kimi K2 0711
Coding	1	0
Intelligence	1	0
Agentic work	1	0
Reasoning	1	1
Web design	5	0
General	1	1
All shared boards	10	2

CodingLike-for-likeSix of these boards feed the Vibe Coding Index10

Coding benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
Aider polyglot	Not measured	59.1%	Only Kimi K2 0711 measured
SWE-bench Verified	Not measured	71.2%	Only Kimi K2 0711 measured
LiveCodeBench (Vals AI)	Not measured	70.5%	Only Kimi K2 0711 measured
LiveCodeBench (Artificial Analysis run)	Not measured	55.6%	Only Kimi K2 0711 measured
Terminal-Bench Hard (Artificial Analysis run)	22.0%	15.9%	GLM 4.7 Flash
Terminal-Bench	Not measured	27.8%	Only Kimi K2 0711 measured
GSO-Bench	Not measured	4.9%	Only Kimi K2 0711 measured
WeirdML	Not measured	39.4%	Only Kimi K2 0711 measured
LMArena Text: coding	1382	Not measured	Only GLM 4.7 Flash measured
LMArena Text: software and IT	1389	Not measured	Only GLM 4.7 Flash measured
Neither model has a result on the other 28 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10

Intelligence benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
AA Intelligence Index	14.9	12.7	GLM 4.7 Flash
Epoch Capabilities Index	Not measured	140.0	Only Kimi K2 0711 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 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
τ²-Bench Telecom	98.8%	61.1%	GLM 4.7 Flash
Neither model has a result on the other 38 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index11

Reasoning benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
GPQA Diamond	Not measured	71.5%	Only Kimi K2 0711 measured
GPQA Diamond (Artificial Analysis run)	58.1%	76.6%	Kimi K2 0711
Humanity's Last Exam (Artificial Analysis run)	7.6%	7.4%	GLM 4.7 Flash
GPQA Diamond (Epoch AI run)	60.5%	Not measured	Only GLM 4.7 Flash measured
SimpleBench	Not measured	26.3%	Only Kimi K2 0711 measured
LMArena Text: hard prompts	1356	Not measured	Only GLM 4.7 Flash measured
LMArena Text: expert	1357	Not measured	Only GLM 4.7 Flash measured
LMArena Text: hard prompts in English	1369	Not measured	Only GLM 4.7 Flash measured
Neither model has a result on the other 13 boards here

MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared

Math benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
AIME	Not measured	62.7%	Only Kimi K2 0711 measured
MATH 500 (Vals AI)	Not measured	94.2%	Only Kimi K2 0711 measured
MGSM (Vals AI)	Not measured	91.0%	Only Kimi K2 0711 measured
AA Math Index	Not measured	57.0	Only Kimi K2 0711 measured
AIME 2025 (Artificial Analysis run)	Not measured	57.0%	Only Kimi K2 0711 measured
OTIS Mock AIME	58.3%	Not measured	Only GLM 4.7 Flash measured
LMArena Text: math	1355	Not measured	Only GLM 4.7 Flash measured
LMArena Text: mathematical work	1369	Not measured	Only GLM 4.7 Flash measured
Neither model has a result on the other 5 boards here

KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared

Knowledge benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
MMLU Pro	Not measured	79.4%	Only Kimi K2 0711 measured
LegalBench (Vals AI)	Not measured	81.5%	Only Kimi K2 0711 measured
CorpFin (Vals AI)	Not measured	50.4%	Only Kimi K2 0711 measured
TaxEval (Vals AI)	Not measured	70.2%	Only Kimi K2 0711 measured
MedQA (Vals AI)	Not measured	84.0%	Only Kimi K2 0711 measured
MMLU Pro (Artificial Analysis run)	Not measured	82.4%	Only Kimi K2 0711 measured
LMArena Text: science	1358	Not measured	Only GLM 4.7 Flash measured
LMArena Text: medicine and healthcare	1343	Not measured	Only GLM 4.7 Flash measured
LMArena Text: legal and government	1357	Not measured	Only GLM 4.7 Flash measured
LMArena Text: business and finance	1343	Not measured	Only GLM 4.7 Flash measured
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index50

Web design benchmarks for GLM 4.7 Flash and Kimi K2 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
Design Arena: websites	1194	1050	GLM 4.7 Flash
Design Arena: UI components	1208	1030	GLM 4.7 Flash
Design Arena: game dev	1135	974	GLM 4.7 Flash
Design Arena: data viz	1123	1018	GLM 4.7 Flash
Design Arena: 3D	1121	Not measured	Only GLM 4.7 Flash measured
Design Arena: all code categories	1178	1033	GLM 4.7 Flash
Design Arena: SVG	1038	Not measured	Only 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 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
IFBench	60.8%	41.5%	GLM 4.7 Flash
Long context reasoning (AA-LCR)	41.7%	53.0%	Kimi K2 0711
Fiction.LiveBench	Not measured	61.1%	Only Kimi K2 0711 measured
Creative writing (Lech Mazur)	Not measured	85.6%	Only Kimi K2 0711 measured
CL-bench	Not measured	11.9%	Only Kimi K2 0711 measured
LMArena Text	1351	Not measured	Only GLM 4.7 Flash measured
LMArena Text: instruction following	1327	Not measured	Only GLM 4.7 Flash measured
LMArena Text: creative writing	1296	Not measured	Only GLM 4.7 Flash measured
LMArena Text: English	1373	Not measured	Only GLM 4.7 Flash measured
LMArena Text: non-English	1330	Not measured	Only GLM 4.7 Flash measured
LMArena Text: Chinese	1403	Not measured	Only GLM 4.7 Flash measured
LMArena Text: German	1336	Not measured	Only GLM 4.7 Flash measured
LMArena Text: Korean	1283	Not measured	Only GLM 4.7 Flash measured
LMArena Text: French	1332	Not measured	Only GLM 4.7 Flash measured
LMArena Text: Spanish	1350	Not measured	Only GLM 4.7 Flash measured
LMArena Text: Russian	1333	Not measured	Only GLM 4.7 Flash measured
LMArena Text: Polish	1291	Not measured	Only GLM 4.7 Flash measured
LMArena Text: multi-turn	1342	Not measured	Only GLM 4.7 Flash measured
LMArena Text: longer queries	1345	Not measured	Only GLM 4.7 Flash measured
LMArena Text: writing and language	1320	Not measured	Only GLM 4.7 Flash measured
LMArena Text: entertainment and media	1304	Not measured	Only GLM 4.7 Flash measured
Neither model has a result on the other 25 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 0711
Benchmark	GLM 4.7 Flash	Kimi K2 0711	Leader
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 0711$0.0017Fits 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 0711$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 0711$0.137Exceeds the context window

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 0711
Specification	Z.ai GLM 4.7 Flash	MoonshotAI Kimi K2 0711
Maker	Z.ai	Moonshot AI
Weights	Open weights	Open weights
Released	January 19, 2026	July 11, 2025
Context window	200K	131K
Input $/Mtok	$0.0653	$0.57
Output $/Mtok	$0.40	$2.30
Coding index	Not benchmarked	Not benchmarked
Independent measurements behind it	0	0
Cost per solved task	Not computable	Not computable
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 4.7 Flash better than Kimi K2 0711 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 5.7x less, and where the context window is larger, 200K against 131K tokens.

Which is cheaper, GLM 4.7 Flash or Kimi K2 0711?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Kimi K2 0711 costs $0.57 and $2.30. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Kimi K2 0711?

GLM 4.7 Flash, with 200K tokens against 131K.

How many benchmarks is the GLM 4.7 Flash vs Kimi K2 0711 comparison based on?

12 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 0711's on 0. Where only one model has a result, the page says so instead of naming a winner.

Decision reading

Which one for your work

Coding

Intelligence

Reasoning

Math

Knowledge

Web design

Long documents

Everyday cost

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next