Enter opens the highlighted result. Esc closes.

GLM 4.6 vs Mercury 2

Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace Mercury 2
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
34
Mercury 2Inception
Coding
0.0
Overall
10.8
Output $/M
$0.75
3
vs boards won of 37 shared

Decision reading

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 0.0 for Mercury 2.

Pick Mercury 2 when output tokens cost 2.9x less, or when what matters is ALE-Bench or Humanity's Last Exam (Artificial Analysis run), where it leads.

Model AGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved taskModel BMercury 2Inception0.0/1001 independent benchmarkNo cost per solved task
Shared results
37
GLM 4.6 only
29
Mercury 2 only
5
Like-for-like categories
7 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 4.6 52.2 from 2 benchmarks, Mercury 2 0.0 from 1 benchmark.
Widest category
Web design, 18.0 points apart on average over 7 shared benchmarks.
Widest single gap
IFBench: GLM 4.6 43.4% against Mercury 2 69.8%, 26.4 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Mercury 2 $0.75 per million output tokens, 2.9x apart.
Context window
GLM 4.6 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 4.6BarsMercury 2Ties
Coding61
Intelligence10
Agentic work10
Reasoning41
Knowledge30
Web design70
General121
All shared boards343
CodingLike-for-likeFour of these boards feed the Vibe Coding Index61
Coding benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411171GLM 4.6
AA Coding Index45.831.1GLM 4.6
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured37.7%Only Mercury 2 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%26.5%GLM 4.6
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
WeirdMLNot measured43.2%Only Mercury 2 measured
ALE-Bench340.8785.6Mercury 2
LMArena Text: coding14491390GLM 4.6
LMArena Text: software and IT14551385GLM 4.6
LMArena WebDev: ReactNot measured1160Only Mercury 2 measured
LMArena WebDev: HTML13501199GLM 4.6
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
AA Intelligence Index18.510.8GLM 4.6
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
AA Agentic IndexNot measured1.6Only Mercury 2 measured
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1090Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%70.8%GLM 4.6
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
Reasoning benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%77.0%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%17.1%Mercury 2
LMArena Text: hard prompts14401362GLM 4.6
LMArena Text: expert14321358GLM 4.6
LMArena Text: hard prompts in English14451374GLM 4.6
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math1432Not measuredOnly GLM 4.6 measured
LMArena Text: mathematical work1447Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
Knowledge benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561343GLM 4.6
LMArena Text: medicine and healthcare14551363GLM 4.6
LMArena Text: legal and government1448Not measuredOnly GLM 4.6 measured
LMArena Text: business and finance14161349GLM 4.6
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
Design Arena: websites11741036GLM 4.6
Design Arena: UI components1147998GLM 4.6
Design Arena: game dev1151985GLM 4.6
Design Arena: data viz11611009GLM 4.6
Design Arena: 3D1129982GLM 4.6
Design Arena: all code categories11661022GLM 4.6
Design Arena: SVG1108998GLM 4.6
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 index121
General benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 2Leader
IFBench43.4%69.8%Mercury 2
Long context reasoning (AA-LCR)54.0%43.7%GLM 4.6
LMArena Text14401354GLM 4.6
LMArena Text: instruction following14101328GLM 4.6
LMArena Text: creative writing14121286GLM 4.6
LMArena Text: English14491378GLM 4.6
LMArena Text: non-English14261330GLM 4.6
LMArena Text: Chinese14991414GLM 4.6
LMArena Text: German1447Not measuredOnly GLM 4.6 measured
LMArena Text: Japanese1393Not measuredOnly GLM 4.6 measured
LMArena Text: Korean1400Not measuredOnly GLM 4.6 measured
LMArena Text: French1459Not measuredOnly GLM 4.6 measured
LMArena Text: Spanish1436Not measuredOnly GLM 4.6 measured
LMArena Text: Russian14181304GLM 4.6
LMArena Text: Polish1447Not measuredOnly GLM 4.6 measured
LMArena Text: multi-turn14271357GLM 4.6
LMArena Text: longer queries14221329GLM 4.6
LMArena Text: writing and language14151300GLM 4.6
LMArena Text: entertainment and media14091314GLM 4.6
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Mercury 2
BenchmarkGLM 4.6Mercury 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 4.6$0.0016Fits in one request
Mercury 2$0.0006Fits in one request

Mercury 2 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.034Fits in one request
Mercury 2$0.015Fits in one request

Mercury 2 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.132Exceeds 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 4.6 and Mercury 2
SpecificationZ.ai GLM 4.6Inception Mercury 2
MakerZ.aiInception
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025March 4, 2026
Context window205K128K
Input $/Mtok$0.55$0.25
Output $/Mtok$2.20$0.75
Coding index52.20.0
Independent measurements behind it21
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Mercury 2 for coding?

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 0.0 for Mercury 2. Pick Mercury 2 when output tokens cost 2.9x less, or when what matters is ALE-Bench or Humanity's Last Exam (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.6 or Mercury 2?

GLM 4.6 costs $0.55 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 neither of them.

Which has the bigger context window, GLM 4.6 or Mercury 2?

GLM 4.6, with 205K tokens against 128K.

How many benchmarks is the GLM 4.6 vs Mercury 2 comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. GLM 4.6'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.

Compare next