Model comparison

GLM-4.6 vs Qwen3.7 Flash

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 18× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

Last verified . 0 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3.7 Flash Alibaba (Qwen)

39.9

Rank #156 Confirmed

Summary

  • The widest gap is in knowledge, where Qwen3.7 Flash leads 48.9 to 40.2.
  • Qwen3.7 Flash is cheaper at $0.03 / $0.13 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • Qwen3.7 Flash accepts more context: 1M tokens versus 205K.
  • GLM-4.6 has downloadable open weights; the other is API-only.

Side by side

GLM-4.6 and Qwen3.7 Flash specifications
GLM-4.6Qwen3.7 Flash
ProviderZ.ai (Zhipu)Alibaba (Qwen)
Noometry Index41.439.9
Released2025-09-302026-07-15
WeightsOpenProprietary
Context window205K1M
Max output131K131K
Input $ / M tokens$0.60$0.03
Output $ / M tokens$2.20$0.13
Results tracked297

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Category by category

Coding Not comparable

GLM-4.6: 40.1 (#148), Qwen3.7 Flash: —

Coding benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
LMArena Coding1449—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), Qwen3.7 Flash: —

Agentic & Tool Use benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning Qwen3.7 Flash leads

GLM-4.6: 23.7 (#172), Qwen3.7 Flash: 28.2 (#108)

Reasoning benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
Kagi LLM Benchmark47.4%—
NYT Connections (extended)—43.8%
CritPt1.1%—
Chess Puzzles—23%
LMArena Hard Prompts1440—
Mystery Game Puzzles—15%
Epoch Capabilities Index—144.64

Math Too close to call

GLM-4.6: 39.1 (#111), Qwen3.7 Flash: 38.3 (#140)

Math benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
FrontierMath (Tiers 1-3)—19.3%
OTIS Mock AIME 2024-2025—86.7%
LMArena Math1432—
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Qwen3.7 Flash leads

GLM-4.6: 40.2 (#124), Qwen3.7 Flash: 48.9 (#75)

Knowledge benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
GPQA Diamond—82.3%
Vectara Hallucination Rate9.5%—
LMArena Expert1431—

Multilingual Not comparable

GLM-4.6: 53.5 (#66), Qwen3.7 Flash: —

Multilingual benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
LMArena Non-English1426—
LMArena Chinese1499—
LMArena French1459—
LMArena German1447—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Russian1419—
LMArena Spanish1436—

Instruction Following Not comparable

GLM-4.6: 74.3 (#98), Qwen3.7 Flash: —

Instruction Following benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
LMArena Instruction Following1410—

Long Context Not comparable

GLM-4.6: 43.4 (#94), Qwen3.7 Flash: —

Long Context benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
LMArena Longer Query1422—

Writing & Preference Not comparable

GLM-4.6: 61.1 (#90), Qwen3.7 Flash: —

Writing & Preference benchmarks
BenchmarkGLM-4.6Qwen3.7 Flash
LMArena Text1440—
LMArena Creative Writing1411—
EQ-Bench Creative Writing1411—
LMArena Multi-Turn1427—

Frequently asked questions

Is GLM-4.6 better than Qwen3.7 Flash?

GLM-4.6 is the stronger model overall, scoring 41.4 to 39.9 on the Noometry Index. Qwen3.7 Flash costs 18× 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 Qwen3.7 Flash?

Qwen3.7 Flash is cheaper. It lists at $0.03 per million input tokens and $0.13 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

Which has the bigger context window?

Qwen3.7 Flash does, with 1M tokens against 205K.

How many benchmarks do GLM-4.6 and Qwen3.7 Flash share?

0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3.7 Flash has 7.

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