Noometry

Model comparison

GLM-4.7 vs Qwen3 8B

GLM-4.7 is the stronger model overall, scoring 42.0 to 33.7 on the Noometry Index. Qwen3 8B costs 3.2× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

Last verified . 7 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

Qwen3 8B Alibaba (Qwen)

33.7

Rank #238 Confirmed

Summary

  • They share 7 benchmarks with published results for both. GLM-4.7 scores higher in 5 categories and Qwen3 8B in 1 category; 6 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 36.1.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 56.1% for Qwen3 8B.
  • Qwen3 8B is cheaper at $0.18 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • GLM-4.7 accepts more context: 205K tokens versus 131K.

Side by side

GLM-4.7Qwen3 8B
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index42.033.7
Released2025-12-222025-04
WeightsOpenOpen
Context window205K131K
Max output131K8K
Input $ / M tokens$0.60$0.18
Output $ / M tokens$2.20$0.70
Results tracked3611

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Category by category

Coding GLM-4.7 leads

GLM-4.7: 44.0 (#79), Qwen3 8B: 34.0 (#248)

BenchmarkGLM-4.7Qwen3 8B
SciCode45.1%22.6%
LMArena WebDev1435—
LMArena Coding1454—
ALE-Bench399.48—

Agentic & Tool Use Qwen3 8B leads

GLM-4.7: 26.5 (#103), Qwen3 8B: 30.2 (#78)

BenchmarkGLM-4.7Qwen3 8B
Terminal-Bench33.4%—
Berkeley Function Calling Leaderboard—42.6%
Vending-Bench 22,377—

Reasoning GLM-4.7 leads

GLM-4.7: 24.3 (#164), Qwen3 8B: 16.6 (#303)

BenchmarkGLM-4.7Qwen3 8B
CritPt1.7%0%
Chess Puzzles6%5%
Epoch Capabilities Index143.51136.17
SimpleBench47.7%—
LMArena Hard Prompts1443—
DTBench—59.7%
LMCA—8.8%

Math GLM-4.7 leads

GLM-4.7: 38.6 (#135), Qwen3 8B: 34.9 (#191)

BenchmarkGLM-4.7Qwen3 8B
OTIS Mock AIME 2024-202583.3%56.1%
ProofBench6%—
LMArena Math1423—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), Qwen3 8B: 36.1 (#173)

BenchmarkGLM-4.7Qwen3 8B
GPQA Diamond83.3%56.8%
Vectara Hallucination Rate11.7%4.8%
SimpleQA Verified32.2%—
LMArena Expert1424—

Multilingual Not comparable

GLM-4.7: 52.8 (#79), Qwen3 8B: —

BenchmarkGLM-4.7Qwen3 8B
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), Qwen3 8B: —

BenchmarkGLM-4.7Qwen3 8B
LMArena Instruction Following1411—

Long Context GLM-4.7 leads

GLM-4.7: 42.8 (#116), Qwen3 8B: 37.9 (#210)

BenchmarkGLM-4.7Qwen3 8B
Fiction.LiveBench—62.1%
CL-bench15.9%—
CL-bench Life10.9%—
LMArena Longer Query1432—

Writing & Preference Not comparable

GLM-4.7: 60.9 (#93), Qwen3 8B: —

BenchmarkGLM-4.7Qwen3 8B
LMArena Text1435—
LMArena Creative Writing1401—
EQ-Bench Creative Writing1413—
LMArena Multi-Turn1446—

Frequently asked questions

Is GLM-4.7 better than Qwen3 8B?

GLM-4.7 is the stronger model overall, scoring 42.0 to 33.7 on the Noometry Index. Qwen3 8B costs 3.2× 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 Qwen3 8B?

Qwen3 8B is cheaper. It lists at $0.18 per million input tokens and $0.70 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or Qwen3 8B better for coding?

GLM-4.7 scores higher on coding benchmarks: 44.0 versus 34.0 in the Noometry coding category.

Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 131K.

How many benchmarks do GLM-4.7 and Qwen3 8B share?

7 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3 8B has 11.

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