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Gemma 2 27B vs GLM 4.7

Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemma 2 27B Replace GLM 4.7
Gemma 2 27BGoogle
Coding
No data
Overall
No data
Output $/M
$0.65
0
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
32
vs boards won of 32 shared

Decision reading

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding.

Gemma 2 27B has the edge where output tokens cost 3.4x less.

Model AGemma 2 27BGoogleNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
32
Gemma 2 27B only
4
GLM 4.7 only
48
Like-for-like categories
6 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 2 27B not benchmarked, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Math, 49.6 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: Gemma 2 27B 1.4% against GLM 4.7 83.3%, 81.9 points apart on a 0 to 100 scale.
Output price
Gemma 2 27B $0.65 against GLM 4.7 $2.20 per million output tokens, 3.4x apart.
Context window
Gemma 2 27B takes 8K 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 2 27BBarsGLM 4.7Ties
Coding02
Intelligence01
Reasoning04
Math03
Knowledge05
General017
All shared boards032
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding IndexNot measured45.3Only GLM 4.7 measured
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
Terminal-Bench Hard (Artificial Analysis run)Not measured31.8%Only GLM 4.7 measured
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding12111454GLM 4.7
LMArena Text: software and IT12281453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
AA Intelligence IndexNot measured22.2Only GLM 4.7 measured
Epoch Capabilities Index122.1143.6GLM 4.7
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Agentic work benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
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 measured1119Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench TelecomNot measured95.9%Only GLM 4.7 measured
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)Not measured85.9%Only GLM 4.7 measured
Humanity's Last Exam (Artificial Analysis run)Not measured27.4%Only GLM 4.7 measured
GPQA Diamond (Epoch AI run)36.5%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 prompts11981443GLM 4.7
LMArena Text: expert11711424GLM 4.7
LMArena Text: hard prompts in English12061452GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 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 AIME1.4%83.3%GLM 4.7
ProofBenchNot measured6.0%Only GLM 4.7 measured
MATH level 527.9%Not measuredOnly Gemma 2 27B measured
LMArena Text: math12121424GLM 4.7
LMArena Text: mathematical work12211411GLM 4.7
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)68.0%83.4%GLM 4.7
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
MMLU75.7%Not measuredOnly Gemma 2 27B measured
LMArena Text: science12281457GLM 4.7
LMArena Text: medicine and healthcare11981469GLM 4.7
LMArena Text: legal and government12411444GLM 4.7
LMArena Text: business and finance12081429GLM 4.7
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
Design Arena: websitesNot measured1226Only GLM 4.7 measured
Design Arena: UI componentsNot measured1199Only GLM 4.7 measured
Design Arena: game devNot measured1188Only 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 measured1143Only 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 index017
General benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBenchNot measured67.9%Only GLM 4.7 measured
Long context reasoning (AA-LCR)Not measured71.0%Only GLM 4.7 measured
DTBench48.0%Not measuredOnly Gemma 2 27B measured
LMCA7.1%Not measuredOnly Gemma 2 27B measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text12311435GLM 4.7
LMArena Text: instruction following12061412GLM 4.7
LMArena Text: creative writing12401401GLM 4.7
LMArena Text: English12421453GLM 4.7
LMArena Text: non-English12171417GLM 4.7
LMArena Text: Chinese12211495GLM 4.7
LMArena Text: German12091426GLM 4.7
LMArena Text: Japanese11751439GLM 4.7
LMArena Text: Korean11741399GLM 4.7
LMArena Text: French12481432GLM 4.7
LMArena Text: Spanish12281434GLM 4.7
LMArena Text: Russian12341423GLM 4.7
LMArena Text: Polish12771435GLM 4.7
LMArena Text: multi-turn12241446GLM 4.7
LMArena Text: longer queries12301432GLM 4.7
LMArena Text: writing and language12491411GLM 4.7
LMArena Text: entertainment and media12121400GLM 4.7
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemma 2 27B and GLM 4.7
BenchmarkGemma 2 27BGLM 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 2 27B$0.0010Fits in one request
GLM 4.7$0.0017Fits in one request

Gemma 2 27B costs less

Repository review

50K input + 3K output tokens

Gemma 2 27B$0.034Exceeds the context window
GLM 4.7$0.037Fits in one request

Gemma 2 27B costs less

Agent loop

200K input + 10K output tokens

Gemma 2 27B$0.137Exceeds the context window
GLM 4.7$0.142Exceeds the context window

Gemma 2 27B 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 2 27B and GLM 4.7
SpecificationGoogle Gemma 2 27BZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsOpen weightsOpen weights
ReleasedJuly 13, 2024December 22, 2025
Context window8K205K
Input $/Mtok$0.65$0.60
Output $/Mtok$0.65$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it02
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemma 2 27B better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding. Gemma 2 27B has the edge where output tokens cost 3.4x less.

Which is cheaper, Gemma 2 27B or GLM 4.7?

Gemma 2 27B costs $0.65 per million input tokens and $0.65 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, Gemma 2 27B or GLM 4.7?

GLM 4.7, with 205K tokens against 8K.

How many benchmarks is the Gemma 2 27B vs GLM 4.7 comparison based on?

32 benchmarks have a result for both models as of October 21, 2026. Gemma 2 27B's coding index rests on 0 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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