Noometry

Model comparison

GLM-4.6 vs Mercury 2

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.1 on the Noometry Index. Mercury 2 costs 2.7× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 16 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Mercury 2 Inception

39.1

Rank #175 Confirmed

Summary

  • They share 16 benchmarks with published results for both. GLM-4.6 scores higher in 6 categories and Mercury 2 in 1 category; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 53.8.
  • Mercury 2 is cheaper at $0.25 / $0.75 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • GLM-4.6 accepts more context: 205K tokens versus 128K.
  • GLM-4.6 has downloadable open weights; the other is API-only.

Side by side

GLM-4.6Mercury 2
ProviderZ.ai (Zhipu)Inception
Noometry Index41.439.1
Released2025-09-302026-02-20
WeightsOpenProprietary
Context window205K128K
Max output131K50K
Input $ / M tokens$0.60$0.25
Output $ / M tokens$2.20$0.75
Results tracked2917

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Category by category

Coding GLM-4.6 leads

GLM-4.6: 40.1 (#148), Mercury 2: 33.5 (#255)

BenchmarkGLM-4.6Mercury 2
LMArena WebDev13401171
SciCode38.4%38.7%
LMArena Coding14491391
ALE-Bench340.82785.58
SWE-bench Verified (bash only)55.4%—
WeirdML—43.2%

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Mercury 2: —

BenchmarkGLM-4.6Mercury 2
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning Too close to call

GLM-4.6: 23.7 (#172), Mercury 2: 23.8 (#170)

BenchmarkGLM-4.6Mercury 2
CritPt1.1%0.8%
LMArena Hard Prompts14401362
Kagi LLM Benchmark47.4%—

Math Not comparable

GLM-4.6: 39.1 (#111), Mercury 2: —

BenchmarkGLM-4.6Mercury 2
LMArena Math1432—
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge GLM-4.6 leads

GLM-4.6: 40.2 (#124), Mercury 2: 36.2 (#172)

BenchmarkGLM-4.6Mercury 2
Vectara Hallucination Rate9.5%12.3%
LMArena Expert14311358

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), Mercury 2: 46.6 (#157)

BenchmarkGLM-4.6Mercury 2
LMArena Non-English14261331
LMArena Chinese14991417
LMArena Russian14191304
LMArena French1459—
LMArena German1447—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Spanish1436—

Instruction Following GLM-4.6 leads

GLM-4.6: 74.3 (#98), Mercury 2: 70.2 (#165)

BenchmarkGLM-4.6Mercury 2
LMArena Instruction Following14101329

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), Mercury 2: 40.5 (#154)

BenchmarkGLM-4.6Mercury 2
LMArena Longer Query14221330

Writing & Preference GLM-4.6 leads

GLM-4.6: 61.1 (#90), Mercury 2: 53.8 (#155)

BenchmarkGLM-4.6Mercury 2
LMArena Text14401355
LMArena Creative Writing14111289
LMArena Multi-Turn14271358
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than Mercury 2?

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.1 on the Noometry Index. Mercury 2 costs 2.7× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Which is cheaper, GLM-4.6 or Mercury 2?

Mercury 2 is cheaper. It lists at $0.25 per million input tokens and $0.75 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or Mercury 2 better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 33.5 in the Noometry coding category.

Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 128K.

How many benchmarks do GLM-4.6 and Mercury 2 share?

16 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Mercury 2 has 17.

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