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Gemini 2.5 Pro vs GLM 4.7

Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 2.5 Pro Replace GLM 4.7
Gemini 2.5 ProGoogle
Coding
37.0
Overall
16.1
Output $/M
$10.00
36
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
22
vs boards won of 58 shared

Decision reading

Pick Gemini 2.5 Pro for coding: it scores 37.0 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when output tokens cost 5.1x less, or when a solved task costs $0.29 against $0.64.

Model AGemini 2.5 ProGoogle37.0/1002 independent benchmarks$0.64 per solved taskModel BGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved task
Shared results
58
Gemini 2.5 Pro only
29
GLM 4.7 only
22
Like-for-like categories
8 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
Gemini 2.5 Pro 37.0 from 2 benchmarks, GLM 4.7 33.6 from 1 benchmark.
Widest category
Agentic work, 26.5 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Gemini 2.5 Pro 54.1% against GLM 4.7 95.9%, 41.8 points apart on a 0 to 100 scale.
Output price
Gemini 2.5 Pro $10.00 against GLM 4.7 $1.98 per million output tokens, 5.1x apart.
Context window
Gemini 2.5 Pro takes 1.05M 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
CategoryGemini 2.5 ProBarsGLM 4.7Ties
Coding18
Intelligence11
Agentic work02
Reasoning42
Math32
Knowledge80
Web design15
General182
All shared boards3622
CodingLike-for-likeFour of these boards feed the Vibe Coding Index18
Coding benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
SWE-bench Verified53.6%Not measuredOnly Gemini 2.5 Pro measured
LMArena WebDev12261435GLM 4.7
AA Coding Index33.345.3GLM 4.7
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)54.4%69.4%GLM 4.7
Vibe Code Bench (Vals AI)0.4%Not measuredOnly Gemini 2.5 Pro measured
LiveCodeBench (Artificial Analysis run)80.1%89.4%GLM 4.7
SciCode46.3%Not measuredOnly Gemini 2.5 Pro measured
Terminal-Bench Hard (Artificial Analysis run)26.5%31.8%GLM 4.7
Terminal-Bench32.6%33.4%GLM 4.7
GSO-Bench3.9%Not measuredOnly Gemini 2.5 Pro measured
WeirdML54.0%Not measuredOnly Gemini 2.5 Pro measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding14521454GLM 4.7
LMArena Text: software and IT14591453Gemini 2.5 Pro
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTML12351447GLM 4.7
LMArena WebDev: from an image1285Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
AA Intelligence Index16.122.2GLM 4.7
Epoch Capabilities Index145.1143.6Gemini 2.5 Pro
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
AA Agentic Index1.6Not measuredOnly Gemini 2.5 Pro 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 Telecom54.1%95.9%GLM 4.7
GDPval23.3%Not measuredOnly Gemini 2.5 Pro measured
DeepResearch Bench41.5%Not measuredOnly Gemini 2.5 Pro measured
Vending-Bench 2$574$2,377GLM 4.7
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
Reasoning benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)84.4%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)22.5%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)85.3%83.3%Gemini 2.5 Pro
ARC-AGI41.0%Not measuredOnly Gemini 2.5 Pro measured
ARC-AGI-24.9%Not measuredOnly Gemini 2.5 Pro measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14551443Gemini 2.5 Pro
LMArena Text: expert14521424Gemini 2.5 Pro
LMArena Text: hard prompts in English14531452Gemini 2.5 Pro
LMArena Vision: diagrams1274Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: homework1281Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index32
Math benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index87.795.0GLM 4.7
AIME 2025 (Artificial Analysis run)87.7%95.0%GLM 4.7
OTIS Mock AIME84.7%83.3%Gemini 2.5 Pro
FrontierMath, tiers 1 to 324.6%Not measuredOnly Gemini 2.5 Pro measured
FrontierMath, tier 40.0%Not measuredOnly Gemini 2.5 Pro measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math14501424Gemini 2.5 Pro
LMArena Text: mathematical work14541411Gemini 2.5 Pro
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 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)63.9%54.9%Gemini 2.5 Pro
CorpFin (Vals AI)60.8%46.4%Gemini 2.5 Pro
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MortgageTax (Vals AI)68.9%Not measuredOnly Gemini 2.5 Pro measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)50.6%32.8%Gemini 2.5 Pro
MMLU Pro (Artificial Analysis run)86.2%85.6%Gemini 2.5 Pro
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14801457Gemini 2.5 Pro
LMArena Text: medicine and healthcare14761469Gemini 2.5 Pro
LMArena Text: legal and government14801444Gemini 2.5 Pro
LMArena Text: business and finance14391429Gemini 2.5 Pro
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index15
Web design benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
Design Arena: websites11671226GLM 4.7
Design Arena: UI components11391199GLM 4.7
Design Arena: game dev11121189GLM 4.7
Design Arena: data viz12221195Gemini 2.5 Pro
Design Arena: 3D10801191GLM 4.7
Design Arena: all code categories11521217GLM 4.7
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 index182
General benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 4.7Leader
SAGE (Vals AI)41.9%Not measuredOnly Gemini 2.5 Pro measured
MedScribe (Vals AI)73.5%68.6%Gemini 2.5 Pro
IFBench48.7%67.9%GLM 4.7
Long context reasoning (AA-LCR)69.0%71.0%GLM 4.7
DTBench82.4%Not measuredOnly Gemini 2.5 Pro measured
LMCA34.8%Not measuredOnly Gemini 2.5 Pro measured
Creative writing (Lech Mazur)83.8%Not measuredOnly Gemini 2.5 Pro measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14581435Gemini 2.5 Pro
LMArena Text: instruction following14371412Gemini 2.5 Pro
LMArena Text: creative writing14541401Gemini 2.5 Pro
LMArena Vision1263Not measuredOnly Gemini 2.5 Pro measured
LMArena Text: English14581453Gemini 2.5 Pro
LMArena Text: non-English14521417Gemini 2.5 Pro
LMArena Text: Chinese15071495Gemini 2.5 Pro
LMArena Text: German14871426Gemini 2.5 Pro
LMArena Text: Japanese14611439Gemini 2.5 Pro
LMArena Text: Korean14341399Gemini 2.5 Pro
LMArena Text: French14731432Gemini 2.5 Pro
LMArena Text: Spanish14741434Gemini 2.5 Pro
LMArena Text: Russian14611423Gemini 2.5 Pro
LMArena Text: Polish14791435Gemini 2.5 Pro
LMArena Text: multi-turn14531446Gemini 2.5 Pro
LMArena Text: longer queries14491432Gemini 2.5 Pro
LMArena Text: writing and language14541411Gemini 2.5 Pro
LMArena Text: entertainment and media14411400Gemini 2.5 Pro
LMArena Vision: English1261Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: Chinese1300Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: reading text1270Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: humor1262Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: captioning1304Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: recognizing things1283Not measuredOnly Gemini 2.5 Pro measured
LMArena Vision: creative writing1265Not measuredOnly Gemini 2.5 Pro measured
LMArena Document1421Not measuredOnly Gemini 2.5 Pro measured
Neither model has a result on the other 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 2.5 Pro and GLM 4.7
BenchmarkGemini 2.5 ProGLM 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

Gemini 2.5 Pro$0.0063Fits in one request
GLM 4.7$0.0015Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Gemini 2.5 Pro$0.092Fits in one request
GLM 4.7$0.033Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

Gemini 2.5 Pro$0.350Fits in one request
GLM 4.7$0.128Exceeds the context window

GLM 4.7 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 Gemini 2.5 Pro and GLM 4.7
SpecificationGoogle Gemini 2.5 ProZ.ai GLM 4.7
MakerGoogleZ.ai
WeightsNo public weights listedOpen weights
ReleasedJune 17, 2025December 22, 2025
Context window1.05M205K
Input $/Mtok$1.25$0.54
Output $/Mtok$10.00$1.98
Coding index37.033.6
Independent measurements behind it21
Cost per solved task$0.64$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 2.5 Pro better than GLM 4.7 for coding?

Pick Gemini 2.5 Pro for coding: it scores 37.0 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 5.1x less, or when a solved task costs $0.29 against $0.64.

Which is cheaper, Gemini 2.5 Pro or GLM 4.7?

Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; GLM 4.7 costs $0.54 and $1.98. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and GLM 4.7 $0.29.

Which has the bigger context window, Gemini 2.5 Pro or GLM 4.7?

Gemini 2.5 Pro, with 1.05M tokens against 205K.

How many benchmarks is the Gemini 2.5 Pro vs GLM 4.7 comparison based on?

58 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 2 independent benchmarks 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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