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Gemma 4 26B A4B vs GLM 4.7

Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemma 4 26B A4B Replace GLM 4.7
Gemma 4 26B A4BGoogle
Coding
22.8
Overall
16.7
Output $/M
$0.255
13
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
26
vs boards won of 39 shared

Decision reading

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 22.8 for Gemma 4 26B A4B.

Pick Gemma 4 26B A4B when output tokens cost 7.8x less, or when the context window is larger, 262K against 205K tokens.

Model AGemma 4 26B A4BGoogle22.8/1001 independent benchmarkNo cost per solved taskModel BGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved task
Shared results
39
Gemma 4 26B A4B only
15
GLM 4.7 only
41
Like-for-like categories
7 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
Gemma 4 26B A4B 22.8 from 1 benchmark, GLM 4.7 33.6 from 1 benchmark.
Widest category
Agentic work, 52.3 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Gemma 4 26B A4B 43.6% against GLM 4.7 95.9%, 52.3 points apart on a 0 to 100 scale.
Output price
Gemma 4 26B A4B $0.255 against GLM 4.7 $1.98 per million output tokens, 7.8x apart.
Context window
Gemma 4 26B A4B takes 262K tokens, GLM 4.7 205K.

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 4.7Ties
Coding26
Intelligence02
Agentic work01
Reasoning15
Math21
Knowledge13
General78
All shared boards1326
CodingLike-for-likeFour of these boards feed the Vibe Coding Index26
Coding benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
LMArena WebDev13601435GLM 4.7
AA Coding Index39.345.3GLM 4.7
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)Not measured89.4%Only GLM 4.7 measured
SciCode40.0%Not measuredOnly Gemma 4 26B A4B measured
Terminal-Bench Hard (Artificial Analysis run)25.0%31.8%GLM 4.7
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
WeirdML35.2%Not measuredOnly Gemma 4 26B A4B measured
ALE-Bench927.2399.5Gemma 4 26B A4B
LMArena Text: coding14461454GLM 4.7
LMArena Text: software and IT14541453Gemma 4 26B A4B
LMArena WebDev: React13511420GLM 4.7
LMArena WebDev: HTML13621447GLM 4.7
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
AA Intelligence Index16.722.2GLM 4.7
Epoch Capabilities Index141.9143.6GLM 4.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
AA Agentic Index2.9Not measuredOnly Gemma 4 26B A4B measured
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1118Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom43.6%95.9%GLM 4.7
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)79.2%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)19.3%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)73.2%83.3%GLM 4.7
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14381443GLM 4.7
LMArena Text: expert14471424Gemma 4 26B A4B
LMArena Text: hard prompts in English14481452GLM 4.7
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 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index21
Math benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math IndexNot measured95.0Only GLM 4.7 measured
AIME 2025 (Artificial Analysis run)Not measured95.0%Only GLM 4.7 measured
OTIS Mock AIME82.2%83.3%GLM 4.7
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14701424Gemma 4 26B A4B
LMArena Text: mathematical work14751411Gemma 4 26B A4B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)Not measured85.6%Only GLM 4.7 measured
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14461457GLM 4.7
LMArena Text: medicine and healthcare14281469GLM 4.7
LMArena Text: legal and government14571444Gemma 4 26B A4B
LMArena Text: business and finance14291429GLM 4.7
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
Design Arena: websitesNot measured1226Only GLM 4.7 measured
Design Arena: UI componentsNot measured1199Only GLM 4.7 measured
Design Arena: game devNot measured1189Only GLM 4.7 measured
Design Arena: data vizNot measured1195Only GLM 4.7 measured
Design Arena: 3DNot measured1191Only GLM 4.7 measured
Design Arena: all code categoriesNot measured1217Only GLM 4.7 measured
Design Arena: SVGNot measured1142Only GLM 4.7 measured
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index78
General benchmarks for Gemma 4 26B A4B and GLM 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBench72.4%67.9%Gemma 4 26B A4B
Long context reasoning (AA-LCR)65.7%71.0%GLM 4.7
DTBench74.9%Not measuredOnly Gemma 4 26B A4B measured
LMCA29.7%Not measuredOnly Gemma 4 26B A4B measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14341435GLM 4.7
LMArena Text: instruction following14201412Gemma 4 26B A4B
LMArena Text: creative writing14001401GLM 4.7
LMArena Vision1260Not measuredOnly Gemma 4 26B A4B measured
LMArena Text: English14441453GLM 4.7
LMArena Text: non-English14211417Gemma 4 26B A4B
LMArena Text: Chinese14951495Gemma 4 26B A4B
LMArena Text: GermanNot measured1426Only GLM 4.7 measured
LMArena Text: JapaneseNot measured1439Only GLM 4.7 measured
LMArena Text: KoreanNot measured1399Only GLM 4.7 measured
LMArena Text: French14601432Gemma 4 26B A4B
LMArena Text: Spanish14151434GLM 4.7
LMArena Text: Russian14341423Gemma 4 26B A4B
LMArena Text: PolishNot measured1435Only GLM 4.7 measured
LMArena Text: multi-turn14411446GLM 4.7
LMArena Text: longer queries14271432GLM 4.7
LMArena Text: writing and language14051411GLM 4.7
LMArena Text: entertainment and media14031400Gemma 4 26B A4B
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 14 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 4.7
BenchmarkGemma 4 26B A4BGLM 4.7Leader
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$0.0002Fits in one request
GLM 4.7$0.0015Fits in one request

Gemma 4 26B A4B costs less

Repository review

50K input + 3K output tokens

Gemma 4 26B A4B$0.0046Fits in one request
GLM 4.7$0.033Fits in one request

Gemma 4 26B A4B costs less

Agent loop

200K input + 10K output tokens

Gemma 4 26B A4B$0.018Fits in one request
GLM 4.7$0.128Exceeds the context window

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 4.7
SpecificationGoogle Gemma 4 26B A4BZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 3, 2026December 22, 2025
Context window262K205K
Input $/Mtok$0.0765$0.54
Output $/Mtok$0.255$1.98
Coding index22.833.6
Independent measurements behind it11
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 26B A4B better than GLM 4.7 for coding?

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 22.8 for Gemma 4 26B A4B. Pick Gemma 4 26B A4B when output tokens cost 7.8x less, or when the context window is larger, 262K against 205K tokens.

Which is cheaper, Gemma 4 26B A4B or GLM 4.7?

Gemma 4 26B A4B costs $0.0765 per million input tokens and $0.255 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, Gemma 4 26B A4B or GLM 4.7?

Gemma 4 26B A4B, with 262K tokens against 205K.

How many benchmarks is the Gemma 4 26B A4B vs GLM 4.7 comparison based on?

39 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 4.7's on 1. Where only one model has a result, the page says so instead of naming a winner.

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