Model comparison

GLM-4.6 vs Qwen3.5 397B-A17B

Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 41.4 on the Noometry Index.

Last verified October 10, 2026. 20 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

Qwen3.5 397B-A17B

Alibaba (Qwen)

46.0

Rank #67 Confirmed

Summary

They share 20 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and Qwen3.5 397B-A17B in 9 categories; 6 gaps are clear of the uncertainty.
The widest gap is in knowledge, where Qwen3.5 397B-A17B leads 53.3 to 40.2.
The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 73.7% for Qwen3.5 397B-A17B.
GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.60 / $3.60 for Qwen3.5 397B-A17B.
Qwen3.5 397B-A17B accepts more context: 262K tokens versus 205K.

Side by side

GLM-4.6 and Qwen3.5 397B-A17B specifications
	GLM-4.6	Qwen3.5 397B-A17B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	41.4	46.0
Released	2025-09-30	2026-02-01
Weights	Open	Open
Context window	205K	262K
Max output	131K	66K
Input $ / M tokens	$0.60	$0.60
Output $ / M tokens	$2.20	$3.60
Results tracked	29	36

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Category by category

Coding Qwen3.5 397B-A17B leads

GLM-4.6: 40.1 (#148), Qwen3.5 397B-A17B: 42.0 (#114)

Coding benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena WebDev	1340	1400
LMArena Coding	1449	1465
SWE-bench Verified (bash only)	55.4%	—
SciCode	38.4%	—
ALE-Bench	340.82	—

Agentic & Tool Use Qwen3.5 397B-A17B leads

GLM-4.6: 32.3 (#66), Qwen3.5 397B-A17B: 33.3 (#53)

Agentic & Tool Use benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
Terminal-Bench	24.5%	—
APEX-Agents	—	24.9%
Berkeley Function Calling Leaderboard	72.4%	—
τ²-bench Airline	—	81.5%
τ²-bench Banking	—	9.8%
τ²-bench Retail	—	84.4%
τ²-bench Telecom	—	97.8%

Reasoning Qwen3.5 397B-A17B leads

GLM-4.6: 23.7 (#172), Qwen3.5 397B-A17B: 34.5 (#70)

Reasoning benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
Kagi LLM Benchmark	47.4%	73.7%
LMArena Hard Prompts	1440	1448
NYT Connections (extended)	—	58.9%
CritPt	1.1%	—
Chess Puzzles	—	13%
Thematic Generalization	—	65.1%
Mystery Game Puzzles	—	18%
DTBench	—	87.5%
LMCA	—	37.9%
Epoch Capabilities Index	—	146.65

Math Qwen3.5 397B-A17B leads

GLM-4.6: 39.1 (#111), Qwen3.5 397B-A17B: 46.1 (#73)

Math benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Math	1432	1454
FrontierMath (Tiers 1-3)	—	31.2%
OTIS Mock AIME 2024-2025	—	88.9%
FrontierMath (Feb 2025 set)	3.8%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge Qwen3.5 397B-A17B leads

GLM-4.6: 40.2 (#124), Qwen3.5 397B-A17B: 53.3 (#58)

Knowledge benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Expert	1431	1462
GPQA Diamond	—	86.4%
Vectara Hallucination Rate	9.5%	—

Multimodal Not comparable

GLM-4.6: —, Qwen3.5 397B-A17B: 40.7 (#44)

Multimodal benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Vision	—	1263

Multilingual Too close to call

GLM-4.6: 53.5 (#66), Qwen3.5 397B-A17B: 53.7 (#59)

Multilingual benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Non-English	1426	1430
LMArena Chinese	1499	1500
LMArena French	1459	1461
LMArena German	1447	1447
LMArena Japanese	1393	1426
LMArena Korean	1400	1384
LMArena Russian	1419	1429
LMArena Spanish	1436	1441

Instruction Following Too close to call

GLM-4.6: 74.3 (#98), Qwen3.5 397B-A17B: 75.0 (#77)

Instruction Following benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Instruction Following	1410	1424

Long Context Too close to call

GLM-4.6: 43.4 (#94), Qwen3.5 397B-A17B: 44.1 (#74)

Long Context benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Longer Query	1422	1442

Writing & Preference Qwen3.5 397B-A17B leads

GLM-4.6: 61.1 (#90), Qwen3.5 397B-A17B: 62.3 (#79)

Writing & Preference benchmarks
Benchmark	GLM-4.6	Qwen3.5 397B-A17B
LMArena Text	1440	1438
LMArena Creative Writing	1411	1401
EQ-Bench Creative Writing	1411	1478
LMArena Multi-Turn	1427	1446

Frequently asked questions

Is GLM-4.6 better than Qwen3.5 397B-A17B?

Qwen3.5 397B-A17B is the stronger model overall, scoring 46.0 to 41.4 on the Noometry Index.

Which is cheaper, GLM-4.6 or Qwen3.5 397B-A17B?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5 397B-A17B lists at $0.60 and $3.60.

Is GLM-4.6 or Qwen3.5 397B-A17B better for coding?

Qwen3.5 397B-A17B scores higher on coding benchmarks: 42.0 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

Qwen3.5 397B-A17B does, with 262K tokens against 205K.

How many benchmarks do GLM-4.6 and Qwen3.5 397B-A17B share?

20 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3.5 397B-A17B has 36.