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GLM 4.7 vs GLM 4.7 Flash

Updated October 21, 2026. 43 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 Replace GLM 4.7 Flash
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
41
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
2
vs boards won of 43 shared

Decision reading

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

GLM 4.7 Flash has the edge where output tokens cost 5.5x less, and where what matters is τ²-Bench Telecom or Design Arena: UI components, where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
43
GLM 4.7 only
37
GLM 4.7 Flash only
0
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
GLM 4.7 55.1 from 2 benchmarks, GLM 4.7 Flash not benchmarked.
Widest category
Reasoning, 18.3 points apart on average over 6 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 71.0% against GLM 4.7 Flash 41.7%, 29.3 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GLM 4.7 Flash $0.40 per million output tokens, 5.5x apart.
Context window
GLM 4.7 takes 205K tokens, GLM 4.7 Flash 200K.

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 4.7BarsGLM 4.7 FlashTies
Coding30
Intelligence10
Agentic work01
Reasoning60
Math30
Knowledge40
Web design61
General180
All shared boards412
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
LMArena WebDev1435Not measuredOnly GLM 4.7 measured
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%22.0%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541382GLM 4.7
LMArena Text: software and IT14531389GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML1447Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
AA Intelligence Index22.214.9GLM 4.7
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1119Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%98.8%GLM 4.7 Flash
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%58.1%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%7.6%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%60.5%GLM 4.7
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431356GLM 4.7
LMArena Text: expert14241357GLM 4.7
LMArena Text: hard prompts in English14521369GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%58.3%GLM 4.7
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241355GLM 4.7
LMArena Text: mathematical work14111369GLM 4.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571358GLM 4.7
LMArena Text: medicine and healthcare14691343GLM 4.7
LMArena Text: legal and government14441357GLM 4.7
LMArena Text: business and finance14291343GLM 4.7
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index61
Web design benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
Design Arena: websites12261194GLM 4.7
Design Arena: UI components11991208GLM 4.7 Flash
Design Arena: game dev11881135GLM 4.7
Design Arena: data viz11951123GLM 4.7
Design Arena: 3D11911121GLM 4.7
Design Arena: all code categories12171178GLM 4.7
Design Arena: SVG11431038GLM 4.7
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%60.8%GLM 4.7
Long context reasoning (AA-LCR)71.0%41.7%GLM 4.7
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351351GLM 4.7
LMArena Text: instruction following14121327GLM 4.7
LMArena Text: creative writing14011296GLM 4.7
LMArena Text: English14531373GLM 4.7
LMArena Text: non-English14171330GLM 4.7
LMArena Text: Chinese14951403GLM 4.7
LMArena Text: German14261336GLM 4.7
LMArena Text: Japanese1439Not measuredOnly GLM 4.7 measured
LMArena Text: Korean13991283GLM 4.7
LMArena Text: French14321332GLM 4.7
LMArena Text: Spanish14341350GLM 4.7
LMArena Text: Russian14231333GLM 4.7
LMArena Text: Polish14351291GLM 4.7
LMArena Text: multi-turn14461342GLM 4.7
LMArena Text: longer queries14321345GLM 4.7
LMArena Text: writing and language14111320GLM 4.7
LMArena Text: entertainment and media14001304GLM 4.7
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and GLM 4.7 Flash
BenchmarkGLM 4.7GLM 4.7 FlashLeader
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$0.0017Fits in one request
GLM 4.7 Flash$0.0003Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
GLM 4.7 Flash$0.0045Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
GLM 4.7 Flash$0.017Exceeds 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 and GLM 4.7 Flash
SpecificationZ.ai GLM 4.7Z.ai GLM 4.7 Flash
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025January 19, 2026
Context window205K200K
Input $/Mtok$0.60$0.0653
Output $/Mtok$2.20$0.40
Coding index55.1Not benchmarked
Independent measurements behind it20
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GLM 4.7 Flash for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 5.5x less, and where what matters is τ²-Bench Telecom or Design Arena: UI components, where it leads.

Which is cheaper, GLM 4.7 or GLM 4.7 Flash?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GLM 4.7 Flash costs $0.0653 and $0.40. Cost per solved task is known for GLM 4.7 only.

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

GLM 4.7, with 205K tokens against 200K.

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

43 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and GLM 4.7 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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