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Gemini 3.7 Flash vs GLM 5.2

GLM 5.2 leads Gemini 3.7 Flash on coding, 61.1 to 58.0 on the Vibe Coding Index, at about the same output price ($4.40 vs $3.75 per million). Updated October 22, 2026; 85 benchmarks have a result for both.

Replace Gemini 3.7 Flash Replace GLM 5.2
Gemini 3.7 FlashGoogle
Coding
58.0
Overall
65.7
Output $/M
$3.75
78
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
7
vs boards won of 85 shared

Gemini 3.7 Flash or GLM 5.2: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash, though on 4 benchmarks against 6.

Pick Gemini 3.7 Flash for output tokens 1.2x cheaper ($3.75 vs $4.40 per million) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Model AGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved taskModel BGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved task
Shared results
85
Gemini 3.7 Flash only
30
GLM 5.2 only
19
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
Gemini 3.7 Flash 58.0 from 6 benchmarks, GLM 5.2 61.1 from 4 benchmarks.
Widest category
Reasoning, 15.7 points apart on average over 11 shared benchmarks.
Widest single gap
ARC-AGI-2: Gemini 3.7 Flash 84.6% against GLM 5.2 22.8%, 61.8 points apart on a 0 to 100 scale.
Output price
Gemini 3.7 Flash $3.75 against GLM 5.2 $4.40 per million output tokens, 1.2x apart.
Context window
Both take 1.05M tokens.

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
CategoryGemini 3.7 FlashBarsGLM 5.2Ties
Coding106
Intelligence20
Agentic work101
Reasoning110
Math70
Knowledge90
Web design80
General210
All shared boards787
CodingLike-for-likeSix of these boards feed the Vibe Coding Index106
Coding benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
LiveBench coding80.5%Not measuredOnly Gemini 3.7 Flash measured
LMArena WebDev15911604GLM 5.2
AA Coding Index71.568.8Gemini 3.7 Flash
LiveCodeBench (Vals AI)88.7%69.5%Gemini 3.7 Flash
SWE-bench Verified (Vals AI)80.8%82.8%GLM 5.2
Terminal-Bench 4 (Vals AI)12.1%Not measuredOnly Gemini 3.7 Flash measured
Vibe Code Bench (Vals AI)70.4%64.0%Gemini 3.7 Flash
Code migration (Vals AI)34.8%37.9%GLM 5.2
ProgramBench (Vals AI)0.0%0.5%GLM 5.2
IOI (Vals AI)67.8%Not measuredOnly Gemini 3.7 Flash measured
Terminal-Bench 2.1 (Vals AI)77.5%67.8%Gemini 3.7 Flash
SkillsBench (Vals AI)65.9%45.1%Gemini 3.7 Flash
CyberBench (Vals AI)43.8%Not measuredOnly Gemini 3.7 Flash measured
SciCode59.8%51.2%Gemini 3.7 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured50.8%Only GLM 5.2 measured
FrontierCode43.6%24.5%Gemini 3.7 Flash
FrontierSWE20.3%Not measuredOnly Gemini 3.7 Flash measured
WeirdMLNot measured70.1%Only GLM 5.2 measured
ALE-Bench904.31047.0GLM 5.2
LMArena Text: coding14991487Gemini 3.7 Flash
LMArena Text: software and IT14981486Gemini 3.7 Flash
LMArena WebDev: React15871617GLM 5.2
LMArena WebDev: HTML15931565Gemini 3.7 Flash
LMArena WebDev: from an image1599Not measuredOnly Gemini 3.7 Flash measured
SciCode13.8%Not measuredOnly Gemini 3.7 Flash measured
SWE Atlas: Codebase QnANot measured48.1%Only GLM 5.2 measured
SWE Atlas: RefactoringNot measured42.4%Only GLM 5.2 measured
SWE Atlas: Test WritingNot measured41.5%Only GLM 5.2 measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
AA Intelligence Index39.633.7Gemini 3.7 Flash
Epoch Capabilities Index158.0151.9Gemini 3.7 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index101
Agentic work benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
AA Agentic IndexNot measured38.4Only GLM 5.2 measured
Design Arena: agents building web apps12231222Gemini 3.7 Flash
Design Arena: agents building full-stack apps12081241GLM 5.2
Design Arena: agents building mobile apps12301159Gemini 3.7 Flash
Design Arena: agents building Android apps12421177Gemini 3.7 Flash
Design Arena: agents building Godot games12641142Gemini 3.7 Flash
Design Arena: agents building games12351206Gemini 3.7 Flash
Design Arena: HTML slidesNot measured1114Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1167Only GLM 5.2 measured
Design Arena: spreadsheets1094Not measuredOnly Gemini 3.7 Flash measured
LiveBench research76.5%Not measuredOnly Gemini 3.7 Flash measured
LiveBench browser56.0%Not measuredOnly Gemini 3.7 Flash measured
LiveBench long agentic28.3%Not measuredOnly Gemini 3.7 Flash measured
SREBench (Vals AI)4.6%0.0%Gemini 3.7 Flash
Finance Agent (Vals AI)59.0%49.7%Gemini 3.7 Flash
Tax Agent Bench (Vals AI)22.8%Not measuredOnly Gemini 3.7 Flash measured
Legal Research Bench (Vals AI)34.6%31.3%Gemini 3.7 Flash
Excel Modeling Benchmark (Vals AI)71.3%61.5%Gemini 3.7 Flash
Terminal-Bench-Science (Vals AI)7.1%Not measuredOnly Gemini 3.7 Flash measured
τ²-Bench TelecomNot measured99.1%Only GLM 5.2 measured
APEX-Agents67.8%45.2%Gemini 3.7 Flash
Remote Labor Index5.0%Not measuredOnly Gemini 3.7 Flash measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2Not measured$8,314Only GLM 5.2 measured
MCP AtlasNot measured77.8%Only GLM 5.2 measured
Neither model has a result on the other 17 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
Reasoning benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
GPQA Diamond93.9%85.6%Gemini 3.7 Flash
LiveBench reasoning87.8%Not measuredOnly Gemini 3.7 Flash measured
MMMU Pro (Vals AI)89.0%Not measuredOnly Gemini 3.7 Flash measured
GPQA Diamond (Artificial Analysis run)94.5%89.5%Gemini 3.7 Flash
Humanity's Last Exam (Artificial Analysis run)47.9%41.1%Gemini 3.7 Flash
GPQA Diamond (Epoch AI run)94.8%91.9%Gemini 3.7 Flash
ARC-AGI95.5%77.0%Gemini 3.7 Flash
ARC-AGI-284.6%22.8%Gemini 3.7 Flash
SimpleBenchNot measured58.8%Only GLM 5.2 measured
Chess puzzles66.0%22.0%Gemini 3.7 Flash
Mystery game puzzles37.0%19.0%Gemini 3.7 Flash
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14951481Gemini 3.7 Flash
LMArena Text: expert15121487Gemini 3.7 Flash
LMArena Text: hard prompts in English14871481Gemini 3.7 Flash
LMArena Vision: diagrams1329Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: homework1320Not measuredOnly Gemini 3.7 Flash measured
Kagi LLM BenchmarkNot measured62.6%Only GLM 5.2 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index70
Math benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
OTIS Mock AIME97.2%86.4%Gemini 3.7 Flash
FrontierMath, tiers 1 to 371.6%59.2%Gemini 3.7 Flash
FrontierMath, tier 436.6%29.3%Gemini 3.7 Flash
ProofBench58.0%35.0%Gemini 3.7 Flash
LMArena Text: math15111483Gemini 3.7 Flash
LMArena Text: mathematical work15021489Gemini 3.7 Flash
MathArena25.9%22.7%Gemini 3.7 Flash
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
MMLU Pro90.1%86.7%Gemini 3.7 Flash
LegalBench (Vals AI)87.3%84.1%Gemini 3.7 Flash
CorpFin (Vals AI)Not measured66.1%Only GLM 5.2 measured
TaxEval (Vals AI)74.7%73.3%Gemini 3.7 Flash
MortgageTax (Vals AI)66.7%Not measuredOnly Gemini 3.7 Flash measured
MedCode (Vals AI)53.4%40.8%Gemini 3.7 Flash
SimpleQA Verified69.2%34.2%Gemini 3.7 Flash
LMArena Text: science15111489Gemini 3.7 Flash
LMArena Text: medicine and healthcare14941475Gemini 3.7 Flash
LMArena Text: legal and government14961486Gemini 3.7 Flash
LMArena Text: business and finance14791451Gemini 3.7 Flash
FACTS Benchmark Suite71.2%Not measuredOnly Gemini 3.7 Flash measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
Design Arena: websites13031285Gemini 3.7 Flash
Design Arena: UI components12951294Gemini 3.7 Flash
Design Arena: game dev13171267Gemini 3.7 Flash
Design Arena: data viz13151286Gemini 3.7 Flash
Design Arena: 3D13061287Gemini 3.7 Flash
Design Arena: all code categories13071287Gemini 3.7 Flash
Design Arena: SVG12841213Gemini 3.7 Flash
Design Arena: ASCII art12781191Gemini 3.7 Flash
LiveBench design73.2%Not measuredOnly Gemini 3.7 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
General benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
LiveBench data analysis68.0%Not measuredOnly Gemini 3.7 Flash measured
SAGE (Vals AI)49.2%Not measuredOnly Gemini 3.7 Flash measured
MedScribe (Vals AI)83.9%83.5%Gemini 3.7 Flash
IFBenchNot measured73.3%Only GLM 5.2 measured
Long context reasoning (AA-LCR)83.0%78.3%Gemini 3.7 Flash
DTBench96.8%93.6%Gemini 3.7 Flash
LMCA50.4%45.8%Gemini 3.7 Flash
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
Furniture assembly26.7%Not measuredOnly Gemini 3.7 Flash measured
LMArena Text14891470Gemini 3.7 Flash
LMArena Text: instruction following14841466Gemini 3.7 Flash
LMArena Text: creative writing14881463Gemini 3.7 Flash
LMArena Vision1314Not measuredOnly Gemini 3.7 Flash measured
LMArena Text: English14831475Gemini 3.7 Flash
LMArena Text: non-English14871459Gemini 3.7 Flash
LMArena Text: Chinese15521522Gemini 3.7 Flash
LMArena Text: German15061470Gemini 3.7 Flash
LMArena Text: Japanese15111450Gemini 3.7 Flash
LMArena Text: Korean14811440Gemini 3.7 Flash
LMArena Text: French15001479Gemini 3.7 Flash
LMArena Text: Spanish14951478Gemini 3.7 Flash
LMArena Text: Russian15151468Gemini 3.7 Flash
LMArena Text: Polish15031456Gemini 3.7 Flash
LMArena Text: multi-turn14851469Gemini 3.7 Flash
LMArena Text: longer queries14921480Gemini 3.7 Flash
LMArena Text: writing and language14911463Gemini 3.7 Flash
LMArena Text: entertainment and media14771450Gemini 3.7 Flash
LMArena Vision: English1309Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: Chinese1394Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: reading text1322Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: humor1299Not measuredOnly Gemini 3.7 Flash measured
LMArena Vision: creative writing1312Not measuredOnly Gemini 3.7 Flash measured
EQ-Bench 4Not measured1222Only GLM 5.2 measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 3.7 Flash and GLM 5.2
BenchmarkGemini 3.7 FlashGLM 5.2Leader
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

Gemini 3.7 Flash$0.0026Fits in one request
GLM 5.2$0.0036Fits in one request

Gemini 3.7 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.7 Flash$0.049Fits in one request
GLM 5.2$0.083Fits in one request

Gemini 3.7 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.7 Flash$0.188Fits in one request
GLM 5.2$0.324Fits in one request

Gemini 3.7 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 Gemini 3.7 Flash and GLM 5.2
SpecificationGoogle Gemini 3.7 FlashZ.ai GLM 5.2
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedAugust 13, 2026June 16, 2026
Context window1.05M1.05M
Input $/Mtok$0.75$1.40
Output $/Mtok$3.75$4.40
Coding index58.061.1
Independent measurements behind it64
Cost per solved task$1.78$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.7 Flash better than GLM 5.2 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 58.0 for Gemini 3.7 Flash, though on 4 benchmarks against 6. Pick Gemini 3.7 Flash for output tokens 1.2x cheaper ($3.75 vs $4.40 per million) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, Gemini 3.7 Flash or GLM 5.2?

Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Per solved SWE-bench Verified task, Gemini 3.7 Flash costs $1.78 and GLM 5.2 $0.86.

Which has the bigger context window, Gemini 3.7 Flash or GLM 5.2?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 3.7 Flash vs GLM 5.2 comparison based on?

85 benchmarks have a result for both models as of October 22, 2026. Gemini 3.7 Flash's coding index rests on 6 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.

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