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Gemini 3.6 Flash vs GLM 5.3

Updated October 21, 2026. 83 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 3.6 Flash Replace GLM 5.3
Gemini 3.6 FlashGoogle
Coding
48.3
Overall
34.0
Output $/M
$3.75
34
GLM 5.3Z.ai
Coding
73.1
Overall
44.8
Output $/M
$1.40
49
vs boards won of 83 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 48.3 for Gemini 3.6 Flash.

Pick Gemini 3.6 Flash when what matters is LiveCodeBench (Vals AI) or Terminal-Bench 2.1 (Vals AI), where it leads.

Model AGemini 3.6 FlashGoogle48.3/1001 independent benchmark$1.49 per solved taskModel BGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task
Shared results
83
Gemini 3.6 Flash only
24
GLM 5.3 only
17
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
Gemini 3.6 Flash 48.3 from 1 benchmark, GLM 5.3 73.1 from 2 benchmarks.
Widest category
Coding, 11.3 points apart on average over 18 shared benchmarks.
Widest single gap
IOI (Vals AI): Gemini 3.6 Flash 35.1% against GLM 5.3 68.4%, 33.4 points apart on a 0 to 100 scale.
Output price
Gemini 3.6 Flash $3.75 against GLM 5.3 $1.40 per million output tokens, 2.7x 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 3.6 FlashBarsGLM 5.3Ties
Coding216
Intelligence02
Agentic work37
Reasoning45
Math24
Knowledge72
Web design34
General139
All shared boards3449
CodingLike-for-likeFour of these boards feed the Vibe Coding Index216
Coding benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev15381621GLM 5.3
AA Coding Index69.274.8GLM 5.3
LiveCodeBench (Vals AI)88.1%80.5%Gemini 3.6 Flash
SWE-bench Verified (Vals AI)79.6%95.4%GLM 5.3
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)64.0%78.1%GLM 5.3
Code migration (Vals AI)30.9%44.2%GLM 5.3
ProgramBench (Vals AI)0.0%1.5%GLM 5.3
IOI (Vals AI)35.1%68.4%GLM 5.3
Terminal-Bench 2.1 (Vals AI)73.8%71.5%Gemini 3.6 Flash
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)45.4%72.1%GLM 5.3
SciCode53.4%59.0%GLM 5.3
FrontierCode34.4%40.1%GLM 5.3
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdML56.1%75.4%GLM 5.3
ALE-Bench715.51317.4GLM 5.3
LMArena Text: coding14911497GLM 5.3
LMArena Text: software and IT14921493GLM 5.3
LMArena WebDev: React15281641GLM 5.3
LMArena WebDev: HTML15471595GLM 5.3
LMArena WebDev: from an image1545Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
AA Intelligence Index34.044.8GLM 5.3
Epoch Capabilities Index154.5156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index37
Agentic work benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
AA Agentic Index29.053.1GLM 5.3
Design Arena: agents building web apps1197Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building full-stack apps1181Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building mobile apps11991194Gemini 3.6 Flash
Design Arena: agents building Android apps1208Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building games1181Not measuredOnly Gemini 3.6 Flash measured
Design Arena: HTML slides11481189GLM 5.3
Design Arena: PowerPoint slides11461255GLM 5.3
Design Arena: spreadsheets1017Not measuredOnly Gemini 3.6 Flash measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)56.3%55.8%Gemini 3.6 Flash
Tax Agent Bench (Vals AI)18.0%39.7%GLM 5.3
Legal Research Bench (Vals AI)25.0%49.0%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)65.4%56.3%Gemini 3.6 Flash
BioMysteryBench (Vals AI)58.5%Not measuredOnly Gemini 3.6 Flash measured
Terminal-Bench-Science (Vals AI)4.3%5.7%GLM 5.3
APEX-Agents46.9%56.6%GLM 5.3
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Neither model has a result on the other 17 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index45
Reasoning benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
GPQA Diamond93.4%88.1%Gemini 3.6 Flash
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
MMMU Pro (Vals AI)88.4%Not measuredOnly Gemini 3.6 Flash measured
GPQA Diamond (Artificial Analysis run)92.8%91.7%Gemini 3.6 Flash
Humanity's Last Exam (Artificial Analysis run)40.8%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)94.1%90.9%Gemini 3.6 Flash
ARC-AGI91.2%Not measuredOnly Gemini 3.6 Flash measured
ARC-AGI-260.4%Not measuredOnly Gemini 3.6 Flash measured
Chess puzzles42.0%37.0%Gemini 3.6 Flash
Mystery game puzzles30.0%33.0%GLM 5.3
LMArena Text: hard prompts14861490GLM 5.3
LMArena Text: expert14901514GLM 5.3
LMArena Text: hard prompts in English14841496GLM 5.3
LMArena Vision: diagrams1314Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: homework1314Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index24
Math benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
OTIS Mock AIME94.2%91.1%Gemini 3.6 Flash
FrontierMath, tiers 1 to 358.9%68.8%GLM 5.3
FrontierMath, tier 422.0%29.3%GLM 5.3
ProofBench36.0%49.0%GLM 5.3
LMArena Text: math15081495Gemini 3.6 Flash
LMArena Text: mathematical work14921497GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index72
Knowledge benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
MMLU Pro89.3%86.8%Gemini 3.6 Flash
LegalBench (Vals AI)86.7%84.8%Gemini 3.6 Flash
CorpFin (Vals AI)63.3%Not measuredOnly Gemini 3.6 Flash measured
TaxEval (Vals AI)74.9%72.4%Gemini 3.6 Flash
MortgageTax (Vals AI)67.6%Not measuredOnly Gemini 3.6 Flash measured
MedCode (Vals AI)53.1%42.9%Gemini 3.6 Flash
SimpleQA Verified66.2%41.0%Gemini 3.6 Flash
LMArena Text: science14891498GLM 5.3
LMArena Text: medicine and healthcare14691481GLM 5.3
LMArena Text: legal and government14791473Gemini 3.6 Flash
LMArena Text: business and finance14641459Gemini 3.6 Flash
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index34
Web design benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
Design Arena: websites13011295Gemini 3.6 Flash
Design Arena: UI components12971314GLM 5.3
Design Arena: game dev12631311GLM 5.3
Design Arena: data viz12891260Gemini 3.6 Flash
Design Arena: 3D12721332GLM 5.3
Design Arena: all code categories12901304GLM 5.3
Design Arena: SVGNot measured1292Only GLM 5.3 measured
Design Arena: ASCII art12811213Gemini 3.6 Flash
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index139
General benchmarks for Gemini 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)56.6%68.5%GLM 5.3
SAGE (Vals AI)48.7%Not measuredOnly Gemini 3.6 Flash measured
MedScribe (Vals AI)79.7%88.8%GLM 5.3
Vals Multimodal Index65.1%Not measuredOnly Gemini 3.6 Flash measured
Long context reasoning (AA-LCR)80.0%79.7%Gemini 3.6 Flash
DTBench95.5%87.7%Gemini 3.6 Flash
LMCA44.9%55.5%GLM 5.3
Furniture assembly23.3%Not measuredOnly Gemini 3.6 Flash measured
LMArena Text14801474Gemini 3.6 Flash
LMArena Text: instruction following14671479GLM 5.3
LMArena Text: creative writing14661458Gemini 3.6 Flash
LMArena Vision1299Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: English14821482GLM 5.3
LMArena Text: non-English14701459Gemini 3.6 Flash
LMArena Text: Chinese15311532GLM 5.3
LMArena Text: German14801488GLM 5.3
LMArena Text: Japanese14751460Gemini 3.6 Flash
LMArena Text: Korean14291460GLM 5.3
LMArena Text: French15031503Gemini 3.6 Flash
LMArena Text: Spanish14761462Gemini 3.6 Flash
LMArena Text: Russian14881469Gemini 3.6 Flash
LMArena Text: Polish14731466Gemini 3.6 Flash
LMArena Text: multi-turn14811472Gemini 3.6 Flash
LMArena Text: longer queries14751483GLM 5.3
LMArena Text: writing and language14701469Gemini 3.6 Flash
LMArena Text: entertainment and media14561447Gemini 3.6 Flash
LMArena Vision: English1295Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: Chinese1357Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: reading text1312Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: humor1289Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: creative writing1278Not measuredOnly Gemini 3.6 Flash measured
LMArena Document1456Not measuredOnly Gemini 3.6 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 3.6 Flash and GLM 5.3
BenchmarkGemini 3.6 FlashGLM 5.3Leader
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 3.6 Flash$0.0026Fits in one request
GLM 5.3$0.0008Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

Gemini 3.6 Flash$0.049Fits in one request
GLM 5.3$0.0081Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

Gemini 3.6 Flash$0.188Fits in one request
GLM 5.3$0.030Fits in one request

GLM 5.3 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 3.6 Flash and GLM 5.3
SpecificationGoogle Gemini 3.6 FlashZ.ai GLM 5.3
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedJuly 21, 2026August 18, 2026
Context window1.05M1.05M
Input $/Mtok$0.75$0.079
Output $/Mtok$3.75$1.40
Coding index48.373.1
Independent measurements behind it12
Cost per solved task$1.49$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.6 Flash better than GLM 5.3 for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 48.3 for Gemini 3.6 Flash. Pick Gemini 3.6 Flash when what matters is LiveCodeBench (Vals AI) or Terminal-Bench 2.1 (Vals AI), where it leads.

Which is cheaper, Gemini 3.6 Flash or GLM 5.3?

Gemini 3.6 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; GLM 5.3 costs $0.079 and $1.40. Per solved SWE-bench Verified task, Gemini 3.6 Flash costs $1.49 and GLM 5.3 $0.36.

Which has the bigger context window, Gemini 3.6 Flash or GLM 5.3?

Neither: both take 1.05M tokens.

How many benchmarks is the Gemini 3.6 Flash vs GLM 5.3 comparison based on?

83 benchmarks have a result for both models as of October 21, 2026. Gemini 3.6 Flash's coding index rests on 1 independent benchmark and GLM 5.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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