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Gemini 3.8 Flash vs GLM 5.2

Gemini 3.8 Flash leads GLM 5.2 on coding, 61.6 to 61.1 on the Vibe Coding Index, at about the same output price ($3.75 vs $4.40 per million). Updated October 22, 2026; 88 benchmarks have a result for both.

Gemini 3.8 FlashGoogle
Coding
61.6
Overall
69.0
Output $/M
$3.75
79
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
9
vs boards won of 88 shared

Gemini 3.8 Flash or GLM 5.2: which to pick

Gemini 3.8 Flash and GLM 5.2 are level for coding, 61.6 against 61.1 on the Vibe Coding Index, so price and context decide.

Pick GLM 5.2 for a lower cost per solved task ($0.86 against $2.74) and work measured by LMArena WebDev or SWE-bench Verified (Vals AI), where it leads. Pick Gemini 3.8 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.

Shared results
88
Gemini 3.8 Flash only
40
GLM 5.2 only
10
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 3.8 Flash 61.6 from 6 benchmarks, GLM 5.2 61.1 from 4 benchmarks.
Widest category
Reasoning, 17.4 points apart on average over 11 shared benchmarks.
Widest single gap
ARC-AGI-2: Gemini 3.8 Flash 89.2% against GLM 5.2 22.8%, 66.4 points apart on a 0 to 100 scale.
Output price
Gemini 3.8 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 3.8 FlashBarsGLM 5.2Ties
Coding134
Intelligence20
Agentic work103
Reasoning110
Math51
Knowledge90
Web design71
General220
All shared boards799
CodingLike-for-likeSix of these boards feed the Vibe Coding Index134
Coding benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
LiveBench coding82.2%Not measuredOnly Gemini 3.8 Flash measured
LMArena WebDev15851604GLM 5.2
AA Coding Index76.368.8Gemini 3.8 Flash
LiveCodeBench (Vals AI)89.5%69.5%Gemini 3.8 Flash
SWE-bench Verified (Vals AI)80.0%82.8%GLM 5.2
Terminal-Bench 4 (Vals AI)19.7%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench (Vals AI)78.7%64.0%Gemini 3.8 Flash
Code migration (Vals AI)36.5%37.9%GLM 5.2
ProgramBench (Vals AI)1.0%0.5%Gemini 3.8 Flash
IOI (Vals AI)56.9%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)81.3%67.8%Gemini 3.8 Flash
SWE-bench Multimodal (Vals AI)40.8%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
SkillsBench (Vals AI)58.0%45.1%Gemini 3.8 Flash
CyberBench (Vals AI)43.8%Not measuredOnly Gemini 3.8 Flash measured
SciCode56.6%51.2%Gemini 3.8 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured50.8%Only GLM 5.2 measured
FrontierCode41.2%24.5%Gemini 3.8 Flash
FrontierSWE19.6%Not measuredOnly Gemini 3.8 Flash measured
WeirdML84.8%70.1%Gemini 3.8 Flash
ALE-Bench1269.71047.0Gemini 3.8 Flash
LMArena Text: coding15081487Gemini 3.8 Flash
LMArena Text: software and IT15071486Gemini 3.8 Flash
LMArena WebDev: React15771617GLM 5.2
LMArena WebDev: HTML16161565Gemini 3.8 Flash
LMArena WebDev: from an image1593Not measuredOnly Gemini 3.8 Flash measured
SciCode12.3%Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
AA Intelligence Index40.933.7Gemini 3.8 Flash
Epoch Capabilities Index157.4151.9Gemini 3.8 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index103
Agentic work benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
AA Agentic Index40.238.4Gemini 3.8 Flash
Design Arena: agents building web apps12381222Gemini 3.8 Flash
Design Arena: agents building full-stack apps12441241Gemini 3.8 Flash
Design Arena: agents building mobile apps12361159Gemini 3.8 Flash
Design Arena: agents building Android apps10781178GLM 5.2
Design Arena: agents building Godot games12731142Gemini 3.8 Flash
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1125Only GLM 5.2 measured
Design Arena: PowerPoint slides11531170GLM 5.2
Design Arena: spreadsheets1022Not measuredOnly Gemini 3.8 Flash measured
LiveBench research79.1%Not measuredOnly Gemini 3.8 Flash measured
LiveBench browser64.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench long agentic30.1%Not measuredOnly Gemini 3.8 Flash measured
SREBench (Vals AI)11.1%0.0%Gemini 3.8 Flash
Finance Agent (Vals AI)61.4%49.7%Gemini 3.8 Flash
Tax Agent Bench (Vals AI)32.1%Not measuredOnly Gemini 3.8 Flash measured
Legal Research Bench (Vals AI)38.9%31.3%Gemini 3.8 Flash
Harvey's Legal Agent Benchmark (Vals AI)12.1%Not measuredOnly Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)72.2%61.5%Gemini 3.8 Flash
BioMysteryBench (Vals AI)62.2%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)8.6%Not measuredOnly Gemini 3.8 Flash measured
Vals Science Index35.8%Not measuredOnly Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
CUA-bench (Vals AI)4.2%Not measuredOnly Gemini 3.8 Flash measured
τ²-Bench TelecomNot measured99.1%Only GLM 5.2 measured
APEX-Agents64.3%45.2%Gemini 3.8 Flash
Remote Labor Index5.8%Not measuredOnly Gemini 3.8 Flash measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2$5,094$8,314GLM 5.2
Neither model has a result on the other 12 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
Reasoning benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
GPQA Diamond94.4%85.6%Gemini 3.8 Flash
LiveBench reasoning89.3%Not measuredOnly Gemini 3.8 Flash measured
MysteryMechanism (Vals AI)36.5%Not measuredOnly Gemini 3.8 Flash measured
MMMU Pro (Vals AI)89.1%Not measuredOnly Gemini 3.8 Flash measured
GPQA Diamond (Artificial Analysis run)95.3%89.5%Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)47.8%41.1%Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)95.4%91.9%Gemini 3.8 Flash
ARC-AGI98.5%77.0%Gemini 3.8 Flash
ARC-AGI-289.2%22.8%Gemini 3.8 Flash
SimpleBenchNot measured58.8%Only GLM 5.2 measured
Chess puzzles58.0%22.0%Gemini 3.8 Flash
Mystery game puzzles47.0%19.0%Gemini 3.8 Flash
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts15091481Gemini 3.8 Flash
LMArena Text: expert15261487Gemini 3.8 Flash
LMArena Text: hard prompts in English15041481Gemini 3.8 Flash
LMArena Vision: diagrams1345Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: homework1351Not measuredOnly Gemini 3.8 Flash measured
Kagi LLM BenchmarkNot measured62.6%Only GLM 5.2 measured
Neither model has a result on the other 3 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index51
Math benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
OTIS Mock AIME98.9%86.4%Gemini 3.8 Flash
FrontierMath, tiers 1 to 368.4%59.2%Gemini 3.8 Flash
FrontierMath, tier 422.0%29.3%GLM 5.2
ProofBench48.0%35.0%Gemini 3.8 Flash
LMArena Text: math15321483Gemini 3.8 Flash
LMArena Text: mathematical work15191489Gemini 3.8 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 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
MMLU Pro90.2%86.7%Gemini 3.8 Flash
LegalBench (Vals AI)87.0%84.1%Gemini 3.8 Flash
CorpFin (Vals AI)Not measured66.1%Only GLM 5.2 measured
TaxEval (Vals AI)74.5%73.3%Gemini 3.8 Flash
MortgageTax (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
MedCode (Vals AI)48.1%40.8%Gemini 3.8 Flash
SimpleQA Verified69.7%34.2%Gemini 3.8 Flash
LMArena Text: science15231489Gemini 3.8 Flash
LMArena Text: medicine and healthcare15081475Gemini 3.8 Flash
LMArena Text: legal and government14971486Gemini 3.8 Flash
LMArena Text: business and finance14791451Gemini 3.8 Flash
FACTS Benchmark Suite69.4%Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index71
Web design benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
Design Arena: websites13001285Gemini 3.8 Flash
Design Arena: UI components13161294Gemini 3.8 Flash
Design Arena: game dev13041268Gemini 3.8 Flash
Design Arena: data viz12701285GLM 5.2
Design Arena: 3D12941287Gemini 3.8 Flash
Design Arena: all code categories13031287Gemini 3.8 Flash
Design Arena: SVG12961213Gemini 3.8 Flash
Design Arena: ASCII art12621191Gemini 3.8 Flash
LiveBench design72.7%Not measuredOnly Gemini 3.8 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
General benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 FlashGLM 5.2Leader
Vals Index56.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench data analysis54.0%Not measuredOnly Gemini 3.8 Flash measured
Public Benefits Bench (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
SAGE (Vals AI)35.1%Not measuredOnly Gemini 3.8 Flash measured
MedScribe (Vals AI)84.5%83.5%Gemini 3.8 Flash
IFBenchNot measured73.3%Only GLM 5.2 measured
Long context reasoning (AA-LCR)84.0%78.3%Gemini 3.8 Flash
DTBench95.7%93.6%Gemini 3.8 Flash
LMCA52.9%45.8%Gemini 3.8 Flash
Surface Evolver Bench76.9%55.6%Gemini 3.8 Flash
Furniture assembly31.7%Not measuredOnly Gemini 3.8 Flash measured
LMArena Text14991470Gemini 3.8 Flash
LMArena Text: instruction following14921466Gemini 3.8 Flash
LMArena Text: creative writing14881463Gemini 3.8 Flash
LMArena Vision1315Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: English14981475Gemini 3.8 Flash
LMArena Text: non-English14911459Gemini 3.8 Flash
LMArena Text: Chinese15531522Gemini 3.8 Flash
LMArena Text: German15011470Gemini 3.8 Flash
LMArena Text: Japanese15071450Gemini 3.8 Flash
LMArena Text: Korean14641440Gemini 3.8 Flash
LMArena Text: French14961479Gemini 3.8 Flash
LMArena Text: Spanish14871478Gemini 3.8 Flash
LMArena Text: Russian15141468Gemini 3.8 Flash
LMArena Text: Polish14971456Gemini 3.8 Flash
LMArena Text: multi-turn15031469Gemini 3.8 Flash
LMArena Text: longer queries15071480Gemini 3.8 Flash
LMArena Text: writing and language14941463Gemini 3.8 Flash
LMArena Text: entertainment and media14801450Gemini 3.8 Flash
LMArena Vision: English1318Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: Chinese1416Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: reading text1333Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: humor1321Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: creative writing1299Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 3.8 Flash and GLM 5.2
BenchmarkGemini 3.8 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 3.8 Flash$0.0026Fits in one request
GLM 5.2$0.0036Fits in one request

Gemini 3.8 Flash costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.049Fits in one request
GLM 5.2$0.083Fits in one request

Gemini 3.8 Flash costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.188Fits in one request
GLM 5.2$0.324Fits in one request

Gemini 3.8 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 3.8 Flash and GLM 5.2
SpecificationGoogle Gemini 3.8 FlashZ.ai GLM 5.2
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedSeptember 2, 2026June 16, 2026
Context window1.05M1.05M
Input $/Mtok$0.75$1.40
Output $/Mtok$3.75$4.40
Coding index61.661.1
Independent measurements behind it64
Cost per solved task$2.74$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.8 Flash better than GLM 5.2 for coding?

Gemini 3.8 Flash and GLM 5.2 are level for coding, 61.6 against 61.1 on the Vibe Coding Index, so price and context decide. Pick GLM 5.2 for a lower cost per solved task ($0.86 against $2.74) and work measured by LMArena WebDev or SWE-bench Verified (Vals AI), where it leads. Pick Gemini 3.8 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 3.8 Flash or GLM 5.2?

Gemini 3.8 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 3.8 Flash costs $2.74 and GLM 5.2 $0.86.

Which has the bigger context window, Gemini 3.8 Flash or GLM 5.2?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 3.8 Flash vs GLM 5.2 comparison based on?

88 benchmarks have a result for both models as of October 22, 2026. Gemini 3.8 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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