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GLM 4.7 vs Mercury 2

GLM 4.7 leads Mercury 2 on the Artificial Analysis Intelligence Index, 22.2 to 10.8, and Mercury 2 costs 2.9x less per output token ($0.75 vs $2.20 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace GLM 4.7 Replace Mercury 2
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
37
Mercury 2Inception
Coding
No data
Overall
No data
Output $/M
$0.75
2
vs boards won of 39 shared

GLM 4.7 or Mercury 2: which to pick

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

Pick Mercury 2 for output tokens 2.9x cheaper ($0.75 vs $2.20 per million) and work measured by ALE-Bench or IFBench, where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BMercury 2InceptionNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
39
GLM 4.7 only
42
Mercury 2 only
3
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
GLM 4.7 55.1 from 2 benchmarks, Mercury 2 not benchmarked.
Widest category
Agentic work, 25.1 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: GLM 4.7 1420 against Mercury 2 1160, 43.4 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against Mercury 2 $0.75 per million output tokens, 2.9x apart.
Context window
GLM 4.7 takes 205K 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 4.7BarsMercury 2Ties
Coding71
Intelligence10
Agentic work10
Reasoning50
Knowledge30
Web design80
General121
All shared boards372
CodingLike-for-likeSix of these boards feed the Vibe Coding Index71
Coding benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
LMArena WebDev14351171GLM 4.7
AA Coding Index45.331.1GLM 4.7
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured37.7%Only Mercury 2 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%26.5%GLM 4.7
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
WeirdMLNot measured43.2%Only Mercury 2 measured
ALE-Bench399.5785.6Mercury 2
LMArena Text: coding14541390GLM 4.7
LMArena Text: software and IT14531385GLM 4.7
LMArena WebDev: React14201160GLM 4.7
LMArena WebDev: HTML14471199GLM 4.7
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
AA Intelligence Index22.210.8GLM 4.7
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
AA Agentic IndexNot measured1.6Only Mercury 2 measured
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%70.8%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly 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 index50
Reasoning benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%77.0%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%17.1%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431362GLM 4.7
LMArena Text: expert14241358GLM 4.7
LMArena Text: hard prompts in English14521374GLM 4.7
Neither model has a result on the other 13 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Math benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math1424Not measuredOnly GLM 4.7 measured
LMArena Text: mathematical work1411Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
Knowledge benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571343GLM 4.7
LMArena Text: medicine and healthcare14691363GLM 4.7
LMArena Text: legal and government1444Not measuredOnly GLM 4.7 measured
LMArena Text: business and finance14291349GLM 4.7
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
Design Arena: websites12251036GLM 4.7
Design Arena: UI components1196996GLM 4.7
Design Arena: game dev1188984GLM 4.7
Design Arena: data viz11951009GLM 4.7
Design Arena: 3D1190982GLM 4.7
Design Arena: all code categories12161021GLM 4.7
Design Arena: SVG1142998GLM 4.7
Design Arena: ASCII art1165988GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
General benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 2Leader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%69.8%Mercury 2
Long context reasoning (AA-LCR)71.0%43.7%GLM 4.7
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351354GLM 4.7
LMArena Text: instruction following14121328GLM 4.7
LMArena Text: creative writing14011286GLM 4.7
LMArena Text: English14531378GLM 4.7
LMArena Text: non-English14171330GLM 4.7
LMArena Text: Chinese14951414GLM 4.7
LMArena Text: German1426Not measuredOnly GLM 4.7 measured
LMArena Text: Japanese1439Not measuredOnly GLM 4.7 measured
LMArena Text: Korean1399Not measuredOnly GLM 4.7 measured
LMArena Text: French1432Not measuredOnly GLM 4.7 measured
LMArena Text: Spanish1434Not measuredOnly GLM 4.7 measured
LMArena Text: Russian14231304GLM 4.7
LMArena Text: Polish1435Not measuredOnly GLM 4.7 measured
LMArena Text: multi-turn14461357GLM 4.7
LMArena Text: longer queries14321329GLM 4.7
LMArena Text: writing and language14111300GLM 4.7
LMArena Text: entertainment and media14001314GLM 4.7
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Mercury 2
BenchmarkGLM 4.7Mercury 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 4.7$0.0017Fits in one request
Mercury 2$0.0006Fits in one request

Mercury 2 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
Mercury 2$0.015Fits in one request

Mercury 2 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds 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 4.7 and Mercury 2
SpecificationZ.ai GLM 4.7Inception Mercury 2
MakerZ.aiInception
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025March 4, 2026
Context window205K128K
Input $/Mtok$0.60$0.25
Output $/Mtok$2.20$0.75
Coding index55.1Not benchmarked
Independent measurements behind it21
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Mercury 2 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding. Pick Mercury 2 for output tokens 2.9x cheaper ($0.75 vs $2.20 per million) and work measured by ALE-Bench or IFBench, where it leads.

Which is cheaper, GLM 4.7 or Mercury 2?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or Mercury 2?

GLM 4.7, with 205K tokens against 128K.

How many benchmarks is the GLM 4.7 vs Mercury 2 comparison based on?

39 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 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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