Noometry

Model comparison

GLM-4.7 vs Mercury 2.5

GLM-4.7 is the stronger model overall, scoring 42.0 to 33.5 on the Noometry Index. Mercury 2.5 costs 15× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

Last verified . 4 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Mercury 2.5 Inception

33.5

Rank #242 Reported

Summary

  • They share 4 benchmarks with published results for both. GLM-4.7 scores higher in 3 categories and Mercury 2.5 in 0 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in math, where GLM-4.7 leads 38.6 to 23.3.
  • The biggest single-benchmark swing is SciCode: 45.1% for GLM-4.7 and 38.5% for Mercury 2.5.
  • Mercury 2.5 is cheaper at $0.04 / $0.15 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • Mercury 2.5 accepts more context: 260K tokens versus 205K.
  • GLM-4.7 has downloadable open weights; the other is API-only.

Side by side

GLM-4.7Mercury 2.5
ProviderZ.ai (Zhipu)Inception
Noometry Index42.033.5
Released2025-12-222026-09-08
WeightsOpenProprietary
Context window205K260K
Max output131K66K
Input $ / M tokens$0.60$0.04
Output $ / M tokens$2.20$0.15
Results tracked364

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Mercury 2.5: 39.5 (#156)

BenchmarkGLM-4.7Mercury 2.5
SciCode45.1%38.5%
ALE-Bench399.48301.65
LMArena WebDev1435—
LMArena Coding1454—

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Mercury 2.5: 22.4 (#193)

BenchmarkGLM-4.7Mercury 2.5
CritPt1.7%0%
SimpleBench47.7%—
Chess Puzzles6%—
LMArena Hard Prompts1443—
Epoch Capabilities Index143.51—

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Mercury 2.5: 23.3 (#272)

BenchmarkGLM-4.7Mercury 2.5
ProofBench6%3%
OTIS Mock AIME 2024-202583.3%—
LMArena Math1423—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge Not comparable

GLM-4.7: 47.0 (#80), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—
LMArena Expert1424—

Multilingual Not comparable

GLM-4.7: 52.8 (#79), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
LMArena Non-English1417—
LMArena Chinese1495—
LMArena French1432—
LMArena German1424—
LMArena Japanese1439—
LMArena Korean1399—
LMArena Russian1423—
LMArena Spanish1434—

Instruction Following Not comparable

GLM-4.7: 74.4 (#95), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
LMArena Instruction Following1411—

Long Context Not comparable

GLM-4.7: 42.8 (#116), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
CL-bench15.9%—
CL-bench Life10.9%—
LMArena Longer Query1432—

Writing & Preference Not comparable

GLM-4.7: 60.9 (#93), Mercury 2.5: —

BenchmarkGLM-4.7Mercury 2.5
LMArena Text1435—
LMArena Creative Writing1401—
EQ-Bench Creative Writing1413—
LMArena Multi-Turn1446—

Frequently asked questions

Is GLM-4.7 better than Mercury 2.5?

GLM-4.7 is the stronger model overall, scoring 42.0 to 33.5 on the Noometry Index. Mercury 2.5 costs 15× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

Which is cheaper, GLM-4.7 or Mercury 2.5?

Mercury 2.5 is cheaper. It lists at $0.04 per million input tokens and $0.15 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or Mercury 2.5 better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 39.5 in the Noometry coding category.

Which has the bigger context window?

Mercury 2.5 does, with 260K tokens against 205K.

How many benchmarks do GLM-4.7 and Mercury 2.5 share?

4 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Mercury 2.5 has 4.

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