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GLM 4.7 Flash vs GLM 5

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 Flash Replace GLM 5
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
1
GLM 5Z.ai
Coding
41.7
Overall
27.9
Output $/M
$3.20
42
vs boards won of 43 shared

Decision reading

Only GLM 5 has a coding score, 41.7 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 8.0x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved task
Shared results
43
GLM 4.7 Flash only
0
GLM 5 only
32
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 Flash not benchmarked, GLM 5 41.7 from 3 benchmarks.
Widest category
Reasoning, 20.0 points apart on average over 6 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against GLM 5 75.7%, 34.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against GLM 5 $3.20 per million output tokens, 8.0x apart.
Context window
GLM 4.7 Flash takes 200K tokens, GLM 5 205K.

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 FlashBarsGLM 5Ties
Coding03
Intelligence01
Agentic work10
Reasoning06
Math03
Knowledge04
Web design07
General018
All shared boards142
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
SWE-bench VerifiedNot measured72.8%Only GLM 5 measured
SWE-bench MultilingualNot measured69.7%Only GLM 5 measured
LMArena WebDevNot measured1435Only GLM 5 measured
LiveCodeBench (Vals AI)Not measured81.9%Only GLM 5 measured
SWE-bench Verified (Vals AI)Not measured71.4%Only GLM 5 measured
Vibe Code Bench (Vals AI)Not measured23.4%Only GLM 5 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%43.2%GLM 5
Terminal-BenchNot measured52.4%Only GLM 5 measured
WeirdMLNot measured48.2%Only GLM 5 measured
ALE-BenchNot measured765.6Only GLM 5 measured
LMArena Text: coding13821461GLM 5
LMArena Text: software and IT13891460GLM 5
LMArena WebDev: ReactNot measured1424Only GLM 5 measured
LMArena WebDev: HTMLNot measured1450Only GLM 5 measured
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
AA Intelligence Index14.927.9GLM 5
Epoch Capabilities IndexNot measured145.8Only GLM 5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
Design Arena: agents building full-stack appsNot measured1123Only GLM 5 measured
Design Arena: agents building mobile appsNot measured1136Only GLM 5 measured
Design Arena: agents building Android appsNot measured1153Only GLM 5 measured
Design Arena: agents building Godot gamesNot measured1144Only GLM 5 measured
Design Arena: HTML slidesNot measured1148Only GLM 5 measured
τ²-Bench Telecom98.8%98.2%GLM 4.7 Flash
Vending-Bench 2Not measured$4,432Only GLM 5 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
GPQA DiamondNot measured83.3%Only GLM 5 measured
GPQA Diamond (Artificial Analysis run)58.1%82.0%GLM 5
Humanity's Last Exam (Artificial Analysis run)7.6%29.3%GLM 5
GPQA Diamond (Epoch AI run)60.5%87.8%GLM 5
ARC-AGINot measured44.7%Only GLM 5 measured
ARC-AGI-2Not measured4.9%Only GLM 5 measured
SimpleBenchNot measured53.2%Only GLM 5 measured
LMArena Text: hard prompts13561452GLM 5
LMArena Text: expert13571454GLM 5
LMArena Text: hard prompts in English13691460GLM 5
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
AIMENot measured91.7%Only GLM 5 measured
OTIS Mock AIME58.3%80.0%GLM 5
LMArena Text: math13551440GLM 5
LMArena Text: mathematical work13691451GLM 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 GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
MMLU ProNot measured86.0%Only GLM 5 measured
LegalBench (Vals AI)Not measured84.1%Only GLM 5 measured
CaseLaw (Vals AI)Not measured52.5%Only GLM 5 measured
CorpFin (Vals AI)Not measured62.9%Only GLM 5 measured
TaxEval (Vals AI)Not measured70.0%Only GLM 5 measured
MedQA (Vals AI)Not measured94.3%Only GLM 5 measured
LMArena Text: science13581460GLM 5
LMArena Text: medicine and healthcare13431455GLM 5
LMArena Text: legal and government13571453GLM 5
LMArena Text: business and finance13431430GLM 5
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.7 Flash and GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
Design Arena: websites11941247GLM 5
Design Arena: UI components12081230GLM 5
Design Arena: game dev11351234GLM 5
Design Arena: data viz11231228GLM 5
Design Arena: 3D11211229GLM 5
Design Arena: all code categories11781246GLM 5
Design Arena: SVG10381168GLM 5
Design Arena: ASCII artNot measured1146Only GLM 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 GLM 5
BenchmarkGLM 4.7 FlashGLM 5Leader
IFBench60.8%72.3%GLM 5
Long context reasoning (AA-LCR)41.7%75.7%GLM 5
CL-benchNot measured18.7%Only GLM 5 measured
LMArena Text13511446GLM 5
LMArena Text: instruction following13271428GLM 5
LMArena Text: creative writing12961438GLM 5
LMArena Text: English13731458GLM 5
LMArena Text: non-English13301430GLM 5
LMArena Text: Chinese14031511GLM 5
LMArena Text: German13361447GLM 5
LMArena Text: JapaneseNot measured1416Only GLM 5 measured
LMArena Text: Korean12831423GLM 5
LMArena Text: French13321456GLM 5
LMArena Text: Spanish13501454GLM 5
LMArena Text: Russian13331436GLM 5
LMArena Text: Polish12911434GLM 5
LMArena Text: multi-turn13421456GLM 5
LMArena Text: longer queries13451446GLM 5
LMArena Text: writing and language13201437GLM 5
LMArena Text: entertainment and media13041426GLM 5
Neither model has a result on the other 26 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 GLM 5
BenchmarkGLM 4.7 FlashGLM 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
GLM 5$0.0026Fits 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
GLM 5$0.060Fits 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
GLM 5$0.232Exceeds 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 GLM 5
SpecificationZ.ai GLM 4.7 FlashZ.ai GLM 5
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026February 11, 2026
Context window200K205K
Input $/Mtok$0.0653$1.00
Output $/Mtok$0.40$3.20
Coding indexNot benchmarked41.7
Independent measurements behind it03
Cost per solved taskNot computable$0.63
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than GLM 5 for coding?

Only GLM 5 has a coding score, 41.7 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 8.0x less, and where the benchmark that matters is τ²-Bench Telecom, where it leads.

Which is cheaper, GLM 4.7 Flash or GLM 5?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GLM 5 costs $1.00 and $3.20. Cost per solved task is known for GLM 5 only.

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

GLM 5, with 205K tokens against 200K.

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

43 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 GLM 5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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