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Gemma 4 31B vs GLM 4.6

Gemma 4 31B leads GLM 4.6 on coding, 39.0 to 8.8 on the Vibe Coding Index, and Gemma 4 31B costs 5.5x less per output token ($0.40 vs $2.20 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace Gemma 4 31B Replace GLM 4.6
Gemma 4 31BGoogle
Coding
39.0
Overall
42.1
Output $/M
$0.40
28
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
9
vs boards won of 37 shared

Gemma 4 31B or GLM 4.6: which to pick

Pick Gemma 4 31B for coding: it scores 39.0 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for work measured by AA Coding Index or AA Intelligence Index, where it leads.

Model AGemma 4 31BGoogle39.0/1002 independent benchmarksNo cost per solved taskModel BGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved task
Shared results
37
Gemma 4 31B only
34
GLM 4.6 only
32
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 31B 39.0 from 2 benchmarks, GLM 4.6 8.8 from 2 benchmarks.
Widest category
Agentic work, 11.4 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: Gemma 4 31B 75.6% against GLM 4.6 43.4%, 32.2 points apart on a 0 to 100 scale.
Output price
Gemma 4 31B $0.40 against GLM 4.6 $2.20 per million output tokens, 5.5x apart.
Context window
Gemma 4 31B takes 262K tokens, GLM 4.6 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 31BBarsGLM 4.6Ties
Coding61
Intelligence01
Agentic work01
Reasoning60
Math20
Knowledge22
General124
All shared boards289
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
Coding benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
SWE-bench VerifiedNot measured68.2%Only GLM 4.6 measured
LiveBench coding49.0%Not measuredOnly Gemma 4 31B measured
LMArena WebDev13671341Gemma 4 31B
AA Coding Index43.445.8GLM 4.6
LiveCodeBench (Vals AI)Not measured81.0%Only GLM 4.6 measured
Vibe Code Bench (Vals AI)Not measured3.1%Only GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)Not measured69.5%Only GLM 4.6 measured
SciCode45.5%Not measuredOnly Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)36.4%28.8%Gemma 4 31B
Terminal-BenchNot measured24.5%Only GLM 4.6 measured
WeirdML52.3%Not measuredOnly Gemma 4 31B measured
ALE-Bench925.5340.8Gemma 4 31B
LMArena Text: coding14591449Gemma 4 31B
LMArena Text: software and IT14631455Gemma 4 31B
LMArena WebDev: React1359Not measuredOnly Gemma 4 31B measured
LMArena WebDev: HTML13801350Gemma 4 31B
SciCode8.9%Not measuredOnly Gemma 4 31B measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
AA Intelligence Index14.718.5GLM 4.6
Epoch Capabilities Index143.0Not measuredOnly Gemma 4 31B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
AA Agentic Index4.2Not measuredOnly Gemma 4 31B measured
Design Arena: agents building full-stack appsNot measured1029Only GLM 4.6 measured
Design Arena: agents building mobile appsNot measured1096Only GLM 4.6 measured
Design Arena: agents building Android appsNot measured1084Only GLM 4.6 measured
Design Arena: agents building Godot gamesNot measured1180Only GLM 4.6 measured
LiveBench research44.4%Not measuredOnly Gemma 4 31B measured
LiveBench browser4.0%Not measuredOnly Gemma 4 31B measured
LiveBench long agentic13.3%Not measuredOnly Gemma 4 31B measured
τ²-Bench Telecom65.5%76.9%GLM 4.6
Berkeley Function Calling Leaderboard (BFCL)Not measured72.4%Only GLM 4.6 measured
BrowseComp-PlusNot measured48.2%Only GLM 4.6 measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
GPQA DiamondNot measured74.5%Only GLM 4.6 measured
LiveBench reasoning68.3%Not measuredOnly Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)85.7%78.0%Gemma 4 31B
Humanity's Last Exam (Artificial Analysis run)23.6%14.5%Gemma 4 31B
GPQA Diamond (Epoch AI run)75.8%Not measuredOnly Gemma 4 31B measured
LMArena Text: hard prompts14481440Gemma 4 31B
LMArena Text: expert14641432Gemma 4 31B
LMArena Text: hard prompts in English14561445Gemma 4 31B
LMArena Vision: diagrams1283Not measuredOnly Gemma 4 31B measured
LMArena Vision: homework1310Not measuredOnly Gemma 4 31B measured
Kagi LLM Benchmark63.5%45.7%Gemma 4 31B
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
AIMENot measured92.7%Only GLM 4.6 measured
MGSM (Vals AI)Not measured89.8%Only GLM 4.6 measured
AA Math IndexNot measured86.0Only GLM 4.6 measured
AIME 2025 (Artificial Analysis run)Not measured86.0%Only GLM 4.6 measured
OTIS Mock AIME73.3%Not measuredOnly Gemma 4 31B measured
LMArena Text: math14631432Gemma 4 31B
LMArena Text: mathematical work14651447Gemma 4 31B
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
Knowledge benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
MMLU ProNot measured82.2%Only GLM 4.6 measured
LegalBench (Vals AI)Not measured79.6%Only GLM 4.6 measured
CaseLaw (Vals AI)52.6%Not measuredOnly Gemma 4 31B measured
CorpFin (Vals AI)Not measured56.8%Only GLM 4.6 measured
TaxEval (Vals AI)Not measured66.2%Only GLM 4.6 measured
MortgageTax (Vals AI)61.4%Not measuredOnly Gemma 4 31B measured
MedQA (Vals AI)Not measured92.2%Only GLM 4.6 measured
MMLU Pro (Artificial Analysis run)Not measured82.9%Only GLM 4.6 measured
SimpleQA Verified10.4%Not measuredOnly Gemma 4 31B measured
LMArena Text: science14571456Gemma 4 31B
LMArena Text: medicine and healthcare14511455GLM 4.6
LMArena Text: legal and government14381448GLM 4.6
LMArena Text: business and finance14301416Gemma 4 31B
FACTS Benchmark Suite45.3%Not measuredOnly Gemma 4 31B measured
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 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
Design Arena: websitesNot measured1174Only GLM 4.6 measured
Design Arena: UI componentsNot measured1145Only GLM 4.6 measured
Design Arena: game devNot measured1150Only GLM 4.6 measured
Design Arena: data vizNot measured1161Only GLM 4.6 measured
Design Arena: 3DNot measured1128Only GLM 4.6 measured
Design Arena: all code categoriesNot measured1165Only GLM 4.6 measured
Design Arena: SVGNot measured1108Only GLM 4.6 measured
LiveBench design58.4%Not measuredOnly Gemma 4 31B measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index124
General benchmarks for Gemma 4 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
LiveBench data analysis53.3%Not measuredOnly Gemma 4 31B measured
SAGE (Vals AI)55.0%Not measuredOnly Gemma 4 31B measured
IFBench75.6%43.4%Gemma 4 31B
Long context reasoning (AA-LCR)69.7%54.0%Gemma 4 31B
DTBench82.7%Not measuredOnly Gemma 4 31B measured
LMCA39.3%Not measuredOnly Gemma 4 31B measured
Surface Evolver Bench30.6%Not measuredOnly Gemma 4 31B measured
LMArena Text14431440Gemma 4 31B
LMArena Text: instruction following14331410Gemma 4 31B
LMArena Text: creative writing14171412Gemma 4 31B
LMArena Vision1276Not measuredOnly Gemma 4 31B measured
LMArena Text: English14511449Gemma 4 31B
LMArena Text: non-English14301426Gemma 4 31B
LMArena Text: Chinese14751499GLM 4.6
LMArena Text: GermanNot measured1447Only GLM 4.6 measured
LMArena Text: JapaneseNot measured1393Only GLM 4.6 measured
LMArena Text: KoreanNot measured1400Only GLM 4.6 measured
LMArena Text: French14361459GLM 4.6
LMArena Text: Spanish14421436Gemma 4 31B
LMArena Text: Russian14601418Gemma 4 31B
LMArena Text: Polish14331447GLM 4.6
LMArena Text: multi-turn14521427Gemma 4 31B
LMArena Text: longer queries14461422Gemma 4 31B
LMArena Text: writing and language14321415Gemma 4 31B
LMArena Text: entertainment and media14051409GLM 4.6
LMArena Vision: English1271Not measuredOnly Gemma 4 31B measured
LMArena Vision: Chinese1326Not measuredOnly Gemma 4 31B measured
LMArena Vision: reading text1282Not measuredOnly Gemma 4 31B measured
LMArena Vision: humor1280Not measuredOnly Gemma 4 31B measured
LMArena Vision: captioning1218Not measuredOnly Gemma 4 31B measured
LMArena Vision: recognizing things1226Not measuredOnly Gemma 4 31B measured
LMArena Vision: creative writing1285Not measuredOnly Gemma 4 31B measured
LMArena Document1425Not measuredOnly Gemma 4 31B 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 31B and GLM 4.6
BenchmarkGemma 4 31BGLM 4.6Leader
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 31B$0.0003Fits in one request
GLM 4.6$0.0017Fits in one request

Gemma 4 31B costs less

Repository review

50K input + 3K output tokens

Gemma 4 31B$0.0082Fits in one request
GLM 4.6$0.037Fits in one request

Gemma 4 31B costs less

Agent loop

200K input + 10K output tokens

Gemma 4 31B$0.032Fits in one request
GLM 4.6$0.142Exceeds the context window

Gemma 4 31B 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 31B and GLM 4.6
SpecificationGoogle Gemma 4 31BZ.ai GLM 4.6
MakerGoogleZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 2, 2026September 30, 2025
Context window262K205K
Input $/Mtok$0.14$0.60
Output $/Mtok$0.40$2.20
Coding index39.08.8
Independent measurements behind it22
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 4 31B better than GLM 4.6 for coding?

Pick Gemma 4 31B for coding: it scores 39.0 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for work measured by AA Coding Index or AA Intelligence Index, where it leads.

Which is cheaper, Gemma 4 31B or GLM 4.6?

Gemma 4 31B costs $0.14 per million input tokens and $0.40 per million output tokens; GLM 4.6 costs $0.60 and $2.20. Cost per solved task is known for neither of them.

Which has the bigger context window, Gemma 4 31B or GLM 4.6?

Gemma 4 31B, with 262K tokens against 205K.

How many benchmarks is the Gemma 4 31B vs GLM 4.6 comparison based on?

37 benchmarks have a result for both models as of October 22, 2026. Gemma 4 31B's coding index rests on 2 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemma-4-31b-it-vs-glm-4-6">LLMBenchmark.ai, Gemma 4 31B vs GLM 4.6</a></p>