Model comparison

GLM-4.6V vs Qwen3.8 27B

Qwen3.8 27B is the stronger model overall, scoring 45.3 to 40.9 on the Noometry Index. GLM-4.6V costs 2.5× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.

Last verified October 10, 2026. 12 shared benchmarks.

GLM-4.6V Z.ai (Zhipu)

40.9

Rank #133 Confirmed

Qwen3.8 27B

Alibaba (Qwen)

45.3

Rank #64 Confirmed

Summary

They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 0 categories and Qwen3.8 27B in 8 categories; 8 gaps are clear of the uncertainty.
The widest gap is in coding, where Qwen3.8 27B leads 50.3 to 40.2.
GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
Qwen3.8 27B accepts more context: 262K tokens versus 128K.

Side by side

GLM-4.6V and Qwen3.8 27B specifications
	GLM-4.6V	Qwen3.8 27B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	40.9	45.3
Released	2025-12-08	2026-08-14
Weights	Open	Open
Context window	128K	262K
Max output	33K	33K
Input $ / M tokens	$0.30	$0.99
Output $ / M tokens	$0.90	$1.49
Results tracked	12	28

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Category by category

Coding Qwen3.8 27B leads

GLM-4.6V: 40.2 (#128), Qwen3.8 27B: 50.3 (#42)

Coding benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Coding	1390	1487
LMArena WebDev	—	1592
SciCode	—	46.6%

Reasoning Qwen3.8 27B leads

GLM-4.6V: 26.5 (#117), Qwen3.8 27B: 31.5 (#71)

Reasoning benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Hard Prompts	1368	1459
NYT Connections (extended)	—	54.5%
CritPt	—	5.4%
DTBench	—	88%
LMCA	—	41.4%
Surface Evolver Bench	—	45%
Epoch Capabilities Index	—	149.38

Math Not comparable

GLM-4.6V: —, Qwen3.8 27B: 35.7 (#139)

Math benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
ProofBench	—	16%
LMArena Math	—	1453

Knowledge Qwen3.8 27B leads

GLM-4.6V: 37.4 (#148), Qwen3.8 27B: 40.9 (#105)

Knowledge benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Expert	1372	1482

Multimodal Qwen3.8 27B leads

GLM-4.6V: 34.2 (#83), Qwen3.8 27B: 40.5 (#36)

Multimodal benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Vision	1164	1270

Multilingual Qwen3.8 27B leads

GLM-4.6V: 48.8 (#135), Qwen3.8 27B: 54.0 (#54)

Multilingual benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Non-English	1359	1431
LMArena Chinese	1426	1504
LMArena Russian	1341	1421
LMArena French	—	1468
LMArena German	—	1435
LMArena Japanese	—	1392
LMArena Korean	—	1389
LMArena Spanish	—	1444

Instruction Following Qwen3.8 27B leads

GLM-4.6V: 71.6 (#145), Qwen3.8 27B: 75.8 (#54)

Instruction Following benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Instruction Following	1351	1436

Long Context Qwen3.8 27B leads

GLM-4.6V: 41.3 (#138), Qwen3.8 27B: 44.3 (#66)

Long Context benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Longer Query	1358	1448

Writing & Preference Qwen3.8 27B leads

GLM-4.6V: 56.8 (#132), Qwen3.8 27B: 65.9 (#41)

Writing & Preference benchmarks
Benchmark	GLM-4.6V	Qwen3.8 27B
LMArena Text	1377	1441
LMArena Creative Writing	1347	1382
LMArena Multi-Turn	1359	1443
EQ-Bench Creative Writing	—	1671

Frequently asked questions

Is GLM-4.6V better than Qwen3.8 27B?

Which is cheaper, GLM-4.6V or Qwen3.8 27B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.

Is GLM-4.6V or Qwen3.8 27B better for coding?

Qwen3.8 27B scores higher on coding benchmarks: 50.3 versus 40.2 in the Noometry coding category.

Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 128K.

How many benchmarks do GLM-4.6V and Qwen3.8 27B share?

12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3.8 27B has 28.