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GLM 4.5V vs Mercury 2

Updated October 21, 2026. 26 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5V Replace Mercury 2
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
2
Mercury 2Inception
Coding
No data
Overall
10.8
Output $/M
$0.75
24
vs boards won of 26 shared

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

GLM 4.5V has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMercury 2InceptionNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
26
GLM 4.5V only
14
Mercury 2 only
16
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
GLM 4.5V not benchmarked, Mercury 2 not benchmarked.
Widest category
Agentic work, 48.2 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5V 22.5% against Mercury 2 70.8%, 48.2 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against Mercury 2 $0.75 per million output tokens, 2.4x apart.
Context window
GLM 4.5V takes 66K tokens, Mercury 2 128K.

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
CategoryGLM 4.5VBarsMercury 2Ties
Coding03
Intelligence01
Agentic work01
Reasoning05
Knowledge03
General211
All shared boards224
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
LMArena WebDevNot measured1171Only Mercury 2 measured
AA Coding IndexNot measured31.1Only Mercury 2 measured
LiveCodeBench (Artificial Analysis run)60.4%Not measuredOnly GLM 4.5V measured
SciCodeNot measured37.7%Only Mercury 2 measured
Terminal-Bench Hard (Artificial Analysis run)6.8%26.5%Mercury 2
WeirdMLNot measured43.2%Only Mercury 2 measured
ALE-BenchNot measured785.6Only Mercury 2 measured
LMArena Text: coding13471390Mercury 2
LMArena Text: software and IT13531385Mercury 2
LMArena WebDev: ReactNot measured1160Only Mercury 2 measured
LMArena WebDev: HTMLNot measured1199Only Mercury 2 measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
AA Intelligence Index7.610.8Mercury 2
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
AA Agentic IndexNot measured1.6Only Mercury 2 measured
τ²-Bench Telecom22.5%70.8%Mercury 2
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
GPQA Diamond (Artificial Analysis run)68.4%77.0%Mercury 2
Humanity's Last Exam (Artificial Analysis run)6.3%17.1%Mercury 2
LMArena Text: hard prompts13351362Mercury 2
LMArena Text: expert13531358Mercury 2
LMArena Text: hard prompts in English13571374Mercury 2
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
AA Math Index73.0Not measuredOnly GLM 4.5V measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly GLM 4.5V measured
LMArena Text: math1354Not measuredOnly GLM 4.5V measured
LMArena Text: mathematical work1336Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
Knowledge benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
MMLU Pro (Artificial Analysis run)78.8%Not measuredOnly GLM 4.5V measured
LMArena Text: science13301343Mercury 2
LMArena Text: medicine and healthcare13491363Mercury 2
LMArena Text: legal and government1338Not measuredOnly GLM 4.5V measured
LMArena Text: business and finance13091349Mercury 2
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
Design Arena: websitesNot measured1036Only Mercury 2 measured
Design Arena: UI componentsNot measured998Only Mercury 2 measured
Design Arena: game devNot measured985Only Mercury 2 measured
Design Arena: data vizNot measured1009Only Mercury 2 measured
Design Arena: 3DNot measured983Only Mercury 2 measured
Design Arena: all code categoriesNot measured1022Only Mercury 2 measured
Design Arena: SVGNot measured998Only Mercury 2 measured
Design Arena: ASCII artNot measured990Only Mercury 2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
General benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
IFBench34.2%69.8%Mercury 2
Long context reasoning (AA-LCR)0.0%43.7%Mercury 2
LMArena Text13331354Mercury 2
LMArena Text: instruction following13111328Mercury 2
LMArena Text: creative writing12941286GLM 4.5V
LMArena Vision1154Not measuredOnly GLM 4.5V measured
LMArena Text: English13621378Mercury 2
LMArena Text: non-English13031330Mercury 2
LMArena Text: Chinese13371414Mercury 2
LMArena Text: Spanish1336Not measuredOnly GLM 4.5V measured
LMArena Text: Russian12981304Mercury 2
LMArena Text: Polish1302Not measuredOnly GLM 4.5V measured
LMArena Text: multi-turn13331357Mercury 2
LMArena Text: longer queries13051329Mercury 2
LMArena Text: writing and language13021300GLM 4.5V
LMArena Text: entertainment and media13031314Mercury 2
LMArena Vision: English1183Not measuredOnly GLM 4.5V measured
LMArena Vision: reading text1151Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 28 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5V and Mercury 2
BenchmarkGLM 4.5VMercury 2Leader
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

GLM 4.5V$0.0015Fits in one request
Mercury 2$0.0006Fits in one request

Mercury 2 costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
Mercury 2$0.015Fits in one request

Mercury 2 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
Mercury 2$0.058Exceeds the context window

Mercury 2 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 GLM 4.5V and Mercury 2
SpecificationZ.ai GLM 4.5VInception Mercury 2
MakerZ.aiInception
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2025March 4, 2026
Context window66K128K
Input $/Mtok$0.60$0.25
Output $/Mtok$1.80$0.75
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than Mercury 2 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5V has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 4.5V or Mercury 2?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5V or Mercury 2?

Mercury 2, with 128K tokens against 66K.

How many benchmarks is the GLM 4.5V vs Mercury 2 comparison based on?

26 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.

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