Enter opens the highlighted result. Esc closes.

Gemma 4 26B A4B vs GLM 5.3

GLM 5.3 leads Gemma 4 26B A4B on the Artificial Analysis Intelligence Index, 44.8 to 16.7, and Gemma 4 26B A4B costs 7.3x less per output token ($0.60 vs $4.40 per million). Updated October 21, 2026; 41 benchmarks have a result for both.

Replace Gemma 4 26B A4B Replace GLM 5.3
Gemma 4 26B A4BGoogle
Coding
No data
Overall
54.7
Output $/M
$0.60
0
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
41
vs boards won of 41 shared

Gemma 4 26B A4B or GLM 5.3: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Gemma 4 26B A4B has none, so the two cannot be ranked on coding.

Pick Gemma 4 26B A4B for output tokens 7.3x cheaper ($0.60 vs $4.40 per million).

Model AGemma 4 26B A4BGoogleNot benchmarked1 independent benchmarkNo cost per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
41
Gemma 4 26B A4B only
14
GLM 5.3 only
59
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 4 26B A4B not benchmarked, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Agentic work, 50.2 points apart on average over 1 shared benchmark.
Widest single gap
AA Agentic Index: Gemma 4 26B A4B 2.9 against GLM 5.3 53.1, 50.2 points apart on a 0 to 100 scale.
Output price
Gemma 4 26B A4B $0.60 against GLM 5.3 $4.40 per million output tokens, 7.3x apart.
Context window
Gemma 4 26B A4B takes 262K 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 4 26B A4BBarsGLM 5.3Ties
Coding09
Intelligence02
Agentic work01
Reasoning06
Math03
Knowledge04
General016
All shared boards041
CodingLike-for-likeSix of these boards feed the Vibe Coding Index09
Coding benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDev13601621GLM 5.3
AA Coding Index39.374.8GLM 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
SciCode40.0%59.0%GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)25.0%Not measuredOnly Gemma 4 26B A4B measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdML35.2%75.4%GLM 5.3
ALE-Bench927.21317.4GLM 5.3
LMArena Text: coding14461497GLM 5.3
LMArena Text: software and IT14541493GLM 5.3
LMArena WebDev: React13511641GLM 5.3
LMArena WebDev: HTML13621595GLM 5.3
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 5.3Leader
AA Intelligence Index16.744.8GLM 5.3
Epoch Capabilities Index141.9156.0GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 5.3Leader
AA Agentic Index2.953.1GLM 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 Telecom43.6%Not measuredOnly Gemma 4 26B A4B measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 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)79.2%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)19.3%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)73.2%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 prompts14381490GLM 5.3
LMArena Text: expert14471514GLM 5.3
LMArena Text: hard prompts in English14481496GLM 5.3
LMArena Vision: diagrams1264Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: homework1293Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 5.3Leader
OTIS Mock AIME82.2%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: math14701495GLM 5.3
LMArena Text: mathematical work14751497GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 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
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science14461498GLM 5.3
LMArena Text: medicine and healthcare14281481GLM 5.3
LMArena Text: legal and government14571473GLM 5.3
LMArena Text: business and finance14291459GLM 5.3
FACTS Benchmark Suite42.7%Not measuredOnly Gemma 4 26B A4B measured
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 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 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 index016
General benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 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
IFBench72.4%Not measuredOnly Gemma 4 26B A4B measured
Long context reasoning (AA-LCR)65.7%79.7%GLM 5.3
DTBench74.9%87.7%GLM 5.3
LMCA29.7%55.5%GLM 5.3
LMArena Text14341474GLM 5.3
LMArena Text: instruction following14201479GLM 5.3
LMArena Text: creative writing14001458GLM 5.3
LMArena Vision1260Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: English14441482GLM 5.3
LMArena Text: non-English14211459GLM 5.3
LMArena Text: Chinese14951532GLM 5.3
LMArena Text: GermanNot measured1488Only GLM 5.3 measured
LMArena Text: JapaneseNot measured1460Only GLM 5.3 measured
LMArena Text: KoreanNot measured1460Only GLM 5.3 measured
LMArena Text: French14601503GLM 5.3
LMArena Text: Spanish14151462GLM 5.3
LMArena Text: Russian14341469GLM 5.3
LMArena Text: PolishNot measured1466Only GLM 5.3 measured
LMArena Text: multi-turn14411472GLM 5.3
LMArena Text: longer queries14271483GLM 5.3
LMArena Text: writing and language14051469GLM 5.3
LMArena Text: entertainment and media14031447GLM 5.3
LMArena Vision: English1256Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: Chinese1312Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: reading text1268Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: humor1272Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: captioning1297Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: recognizing things1191Not measuredOnly Gemma 4 26B A4B measured
LMArena Vision: creative writing1256Not measuredOnly Gemma 4 26B A4B measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 4 26B A4B and GLM 5.3
BenchmarkGemma 4 26B A4BGLM 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 4 26B A4B$0.0004Fits in one request
GLM 5.3$0.0036Fits in one request

Gemma 4 26B A4B costs less

Repository review

50K input + 3K output tokens

Gemma 4 26B A4B$0.0093Fits in one request
GLM 5.3$0.083Fits in one request

Gemma 4 26B A4B costs less

Agent loop

200K input + 10K output tokens

Gemma 4 26B A4B$0.036Fits in one request
GLM 5.3$0.324Fits in one request

Gemma 4 26B A4B 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 4 26B A4B and GLM 5.3
SpecificationGoogle Gemma 4 26B A4BZ.ai GLM 5.3
MakerGoogleZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 3, 2026August 18, 2026
Context window262K1.05M
Input $/Mtok$0.15$1.40
Output $/Mtok$0.60$4.40
Coding indexNot benchmarked70.6
Independent measurements behind it16
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 26B A4B better than GLM 5.3 for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Gemma 4 26B A4B has none, so the two cannot be ranked on coding. Pick Gemma 4 26B A4B for output tokens 7.3x cheaper ($0.60 vs $4.40 per million).

Which is cheaper, Gemma 4 26B A4B or GLM 5.3?

Gemma 4 26B A4B costs $0.15 per million input tokens and $0.60 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 4 26B A4B or GLM 5.3?

GLM 5.3, with 1.05M tokens against 262K.

How many benchmarks is the Gemma 4 26B A4B vs GLM 5.3 comparison based on?

41 benchmarks have a result for both models as of October 21, 2026. Gemma 4 26B A4B's coding index rests on 1 independent benchmark and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

Compare next