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Gemma 3 12B vs GLM 5.3

GLM 5.3 leads Gemma 3 12B on the Artificial Analysis Intelligence Index, 44.8 to 3.8, and Gemma 3 12B costs 27.1x less per output token ($0.1625 vs $4.40 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace Gemma 3 12B Replace GLM 5.3
Gemma 3 12BGoogle
Coding
No data
Overall
28.0
Output $/M
$0.1625
0
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
34
vs boards won of 34 shared

Gemma 3 12B or GLM 5.3: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Gemma 3 12B has none, so the two cannot be ranked on coding.

Pick Gemma 3 12B for output tokens 27.1x cheaper ($0.1625 vs $4.40 per million).

Model AGemma 3 12BGoogleNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
34
Gemma 3 12B only
9
GLM 5.3 only
66
Like-for-like categories
7 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
Gemma 3 12B not benchmarked, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Agentic work, 53.0 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: Gemma 3 12B 16.7% against GLM 5.3 91.1%, 74.4 points apart on a 0 to 100 scale.
Output price
Gemma 3 12B $0.1625 against GLM 5.3 $4.40 per million output tokens, 27.1x apart.
Context window
Gemma 3 12B takes 131K tokens, GLM 5.3 1.05M.

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
CategoryGemma 3 12BBarsGLM 5.3Ties
Coding04
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge04
General014
All shared boards034
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDevNot measured1621Only GLM 5.3 measured
AA Coding Index5.874.8GLM 5.3
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)Not measured78.1%Only GLM 5.3 measured
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
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)Not measured72.1%Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)13.7%Not measuredOnly Gemma 3 12B measured
SciCode16.4%59.0%GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)0.8%Not measuredOnly Gemma 3 12B measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdMLNot measured75.4%Only GLM 5.3 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding12811497GLM 5.3
LMArena Text: software and IT13131493GLM 5.3
LMArena WebDev: ReactNot measured1641Only GLM 5.3 measured
LMArena WebDev: HTMLNot measured1595Only GLM 5.3 measured
SciCode0.0%Not measuredOnly Gemma 3 12B measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
AA Intelligence Index3.844.8GLM 5.3
Epoch Capabilities Index123.5156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
AA Agentic Index0.153.1GLM 5.3
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1254Only GLM 5.3 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)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom10.8%Not measuredOnly Gemma 3 12B measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Berkeley Function Calling Leaderboard (BFCL)30.4%Not measuredOnly Gemma 3 12B measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)34.9%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)4.2%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)39.5%90.9%GLM 5.3
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts13091490GLM 5.3
LMArena Text: expert12481514GLM 5.3
LMArena Text: hard prompts in English13181496GLM 5.3
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 Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
AA Math Index18.3Not measuredOnly Gemma 3 12B measured
AIME 2025 (Artificial Analysis run)18.3%Not measuredOnly Gemma 3 12B measured
OTIS Mock AIME16.7%91.1%GLM 5.3
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math13071495GLM 5.3
LMArena Text: mathematical work13361497GLM 5.3
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)Not measured84.8%Only GLM 5.3 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
MMLU Pro (Artificial Analysis run)59.5%Not measuredOnly Gemma 3 12B measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science13661498GLM 5.3
LMArena Text: medicine and healthcare13341481GLM 5.3
LMArena Text: legal and government13811473GLM 5.3
LMArena Text: business and finance13761459GLM 5.3
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 5.3Leader
Design Arena: websitesNot measured1295Only GLM 5.3 measured
Design Arena: UI componentsNot measured1314Only GLM 5.3 measured
Design Arena: game devNot measured1311Only GLM 5.3 measured
Design Arena: data vizNot measured1259Only GLM 5.3 measured
Design Arena: 3DNot measured1332Only GLM 5.3 measured
Design Arena: all code categoriesNot measured1304Only GLM 5.3 measured
Design Arena: SVGNot measured1293Only GLM 5.3 measured
Design Arena: ASCII artNot measured1213Only GLM 5.3 measured
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
General benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 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)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
IFBench36.7%Not measuredOnly Gemma 3 12B measured
Long context reasoning (AA-LCR)8.3%79.7%GLM 5.3
DTBench48.8%87.7%GLM 5.3
LMCA4.5%55.5%GLM 5.3
LMArena Text13341474GLM 5.3
LMArena Text: instruction following12991479GLM 5.3
LMArena Text: creative writing13311458GLM 5.3
LMArena Text: English13491482GLM 5.3
LMArena Text: non-English13181459GLM 5.3
LMArena Text: ChineseNot measured1532Only GLM 5.3 measured
LMArena Text: German13701488GLM 5.3
LMArena Text: JapaneseNot measured1460Only GLM 5.3 measured
LMArena Text: KoreanNot measured1460Only GLM 5.3 measured
LMArena Text: FrenchNot measured1503Only GLM 5.3 measured
LMArena Text: SpanishNot measured1462Only GLM 5.3 measured
LMArena Text: Russian13351469GLM 5.3
LMArena Text: PolishNot measured1466Only GLM 5.3 measured
LMArena Text: multi-turn13341472GLM 5.3
LMArena Text: longer queries13171483GLM 5.3
LMArena Text: writing and language13071469GLM 5.3
LMArena Text: entertainment and media12971447GLM 5.3
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 3 12B and GLM 5.3
BenchmarkGemma 3 12BGLM 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

Gemma 3 12B$0.0002Fits in one request
GLM 5.3$0.0036Fits in one request

Gemma 3 12B costs less

Repository review

50K input + 3K output tokens

Gemma 3 12B$0.0042Fits in one request
GLM 5.3$0.083Fits in one request

Gemma 3 12B costs less

Agent loop

200K input + 10K output tokens

Gemma 3 12B$0.017Exceeds the context window
GLM 5.3$0.324Fits in one request

Gemma 3 12B 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 Gemma 3 12B and GLM 5.3
SpecificationGoogle Gemma 3 12BZ.ai GLM 5.3
MakerGoogleZ.ai
WeightsOpen weightsOpen weights
ReleasedMarch 13, 2025August 18, 2026
Context window131K1.05M
Input $/Mtok$0.075$1.40
Output $/Mtok$0.1625$4.40
Coding indexNot benchmarked70.6
Independent measurements behind it06
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 3 12B better than GLM 5.3 for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Gemma 3 12B has none, so the two cannot be ranked on coding. Pick Gemma 3 12B for output tokens 27.1x cheaper ($0.1625 vs $4.40 per million).

Which is cheaper, Gemma 3 12B or GLM 5.3?

Gemma 3 12B costs $0.075 per million input tokens and $0.1625 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, Gemma 3 12B or GLM 5.3?

GLM 5.3, with 1.05M tokens against 131K.

How many benchmarks is the Gemma 3 12B vs GLM 5.3 comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. Gemma 3 12B's coding index rests on 0 independent benchmarks and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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