Model comparison

GLM-4.7-Flash vs Qwen3.6 27B

Qwen3.6 27B is the stronger model overall, scoring 42.2 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 9.3× less per token, which makes it the better buy when Qwen3.6 27B's lead doesn't matter for your workload.

Last verified October 12, 2026. 3 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

Qwen3.6 27B

Alibaba (Qwen)

42.2

Rank #117 Confirmed

Summary

They share 3 benchmarks with published results for both. GLM-4.7-Flash scores higher in 1 category and Qwen3.6 27B in 4 categories; 5 gaps are clear of the uncertainty.
The widest gap is in knowledge, where Qwen3.6 27B leads 52.4 to 35.5.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 91.1% for Qwen3.6 27B.
GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.60 / $3.60 for Qwen3.6 27B.
Qwen3.6 27B accepts more context: 262K tokens versus 200K.

Side by side

GLM-4.7-Flash and Qwen3.6 27B specifications
	GLM-4.7-Flash	Qwen3.6 27B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	38.8	42.2
Released	2026-01-19	2026-04-22
Weights	Open	Open
Context window	200K	262K
Max output	131K	66K
Input $ / M tokens	$0.06	$0.60
Output $ / M tokens	$0.40	$3.60
Results tracked	21	11

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Category by category

Coding GLM-4.7-Flash leads

GLM-4.7-Flash: 40.6 (#135), Qwen3.6 27B: 39.1 (#163)

Coding benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
SciCode	—	37.3%
LMArena Coding	1383	—

Reasoning Qwen3.6 27B leads

GLM-4.7-Flash: 20.9 (#229), Qwen3.6 27B: 25.0 (#153)

Reasoning benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
Chess Puzzles	0%	22%
CritPt	—	0.9%
LMArena Hard Prompts	1356	—
Mystery Game Puzzles	—	7%
DTBench	—	78.1%
LMCA	—	34.5%
Epoch Capabilities Index	—	146.5

Math Qwen3.6 27B leads

GLM-4.7-Flash: 36.1 (#173), Qwen3.6 27B: 48.5 (#62)

Math benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
OTIS Mock AIME 2024-2025	58.3%	91.1%
FrontierMath (Tiers 1-3)	—	35.1%
LMArena Math	1355	—

Knowledge Qwen3.6 27B leads

GLM-4.7-Flash: 35.5 (#184), Qwen3.6 27B: 52.4 (#63)

Knowledge benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
GPQA Diamond	60.5%	85.9%
Vectara Hallucination Rate	9.3%	—
LMArena Expert	1357	—

Multilingual Not comparable

GLM-4.7-Flash: 46.5 (#158), Qwen3.6 27B: —

Multilingual benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
LMArena Non-English	1330	—
LMArena Chinese	1403	—
LMArena French	1332	—
LMArena German	1337	—
LMArena Korean	1283	—
LMArena Russian	1332	—
LMArena Spanish	1350	—

Instruction Following Not comparable

GLM-4.7-Flash: 70.1 (#167), Qwen3.6 27B: —

Instruction Following benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
LMArena Instruction Following	1327	—

Long Context Not comparable

GLM-4.7-Flash: 40.9 (#148), Qwen3.6 27B: —

Long Context benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
LMArena Longer Query	1345	—

Writing & Preference Qwen3.6 27B leads

GLM-4.7-Flash: 47.4 (#210), Qwen3.6 27B: 50.3 (#181)

Writing & Preference benchmarks
Benchmark	GLM-4.7-Flash	Qwen3.6 27B
LMArena Text	1351	—
LMArena Creative Writing	1297	—
EQ-Bench Creative Writing	1125	—
EQ-Bench 4	—	1026
LMArena Multi-Turn	1342	—

Frequently asked questions

Is GLM-4.7-Flash better than Qwen3.6 27B?

Which is cheaper, GLM-4.7-Flash or Qwen3.6 27B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3.6 27B lists at $0.60 and $3.60.

Is GLM-4.7-Flash or Qwen3.6 27B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 39.1 in the Noometry coding category.

Which has the bigger context window?

Qwen3.6 27B does, with 262K tokens against 200K.

How many benchmarks do GLM-4.7-Flash and Qwen3.6 27B share?

3 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3.6 27B has 11.