Model comparison

GLM-5 vs Qwen3.8 27B

GLM-5 and Qwen3.8 27B score almost the same on the Noometry Index (46.1 vs 46.0), so choose on price, context window or the category you care about most.

Last verified October 10, 2026. 23 shared benchmarks.

GLM-5 Z.ai (Zhipu)

46.1

Rank #66 Confirmed

Qwen3.8 27B

Alibaba (Qwen)

46.0

Rank #68 Confirmed

Summary

They share 23 benchmarks with published results for both. GLM-5 scores higher in 5 categories and Qwen3.8 27B in 4 categories; 5 gaps are clear of the uncertainty.
The widest gap is in reasoning, where Qwen3.8 27B leads 41.0 to 27.6.
The biggest single-benchmark swing is ARC-AGI-1: 44.7% for GLM-5 and 87.5% for Qwen3.8 27B.
Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $1 / $3.20 for GLM-5.
Qwen3.8 27B accepts more context: 262K tokens versus 205K.

Side by side

GLM-5 and Qwen3.8 27B specifications
	GLM-5	Qwen3.8 27B
Provider	Z.ai (Zhipu)	Alibaba (Qwen)
Noometry Index	46.1	46.0
Released	2026-02-11	2026-08-14
Weights	Open	Open
Context window	205K	262K
Max output	131K	33K
Input $ / M tokens	$1	$0.99
Output $ / M tokens	$3.20	$1.49
Results tracked	45	31

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Category by category

Coding Qwen3.8 27B leads

GLM-5: 49.0 (#52), Qwen3.8 27B: 50.5 (#44)

Coding benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena WebDev	1434	1593
LMArena Coding	1461	1482
SWE-bench Verified	72.1%	—
SWE-bench Verified (bash only)	72.8%	—
SWE-bench Multilingual	69.7%	—
SciCode	—	46.6%
WeirdML	48.2%	—
ALE-Bench	765.62	—

Agentic & Tool Use Qwen3.8 27B leads

GLM-5: 31.1 (#71), Qwen3.8 27B: 32.9 (#57)

Agentic & Tool Use benchmarks
Benchmark	GLM-5	Qwen3.8 27B
Terminal-Bench	52.4%	—
APEX-Agents	—	47.5%
τ²-bench Airline	82.5%	—
τ²-bench Banking	9.8%	—
τ²-bench Retail	73.7%	—
τ²-bench Telecom	86.8%	—
Vending-Bench 2	4,432	—

Reasoning Qwen3.8 27B leads

GLM-5: 27.6 (#116), Qwen3.8 27B: 41.0 (#54)

Reasoning benchmarks
Benchmark	GLM-5	Qwen3.8 27B
ARC-AGI-2	4.9%	42.4%
NYT Connections (extended)	74.8%	54.5%
ARC-AGI-1	44.7%	87.5%
LMArena Hard Prompts	1452	1460
Epoch Capabilities Index	145.83	149.38
SimpleBench	53.2%	—
Kagi LLM Benchmark	75%	—
CritPt	—	5.4%
Chess Puzzles	10%	—
DTBench	—	88%
LMCA	—	41.4%
Surface Evolver Bench	—	45%
ForecastBench	61	—

Math GLM-5 leads

GLM-5: 46.4 (#71), Qwen3.8 27B: 37.1 (#161)

Math benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Math	1440	1456
MathArena Final-Answer Competitions	65.7%	—
OTIS Mock AIME 2024-2025	80%	—
ProofBench	—	16%
FrontierMath (Feb 2025 set)	16.4%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge GLM-5 leads

GLM-5: 52.3 (#64), Qwen3.8 27B: 41.6 (#109)

Knowledge benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Expert	1454	1482
GPQA Diamond	87.8%	—
Vectara Hallucination Rate	10.1%	—

Multimodal Not comparable

GLM-5: —, Qwen3.8 27B: 41.3 (#37)

Multimodal benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Vision	—	1271

Multilingual Too close to call

GLM-5: 53.7 (#58), Qwen3.8 27B: 53.7 (#60)

Multilingual benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Non-English	1430	1430
LMArena Chinese	1511	1504
LMArena French	1455	1465
LMArena German	1445	1438
LMArena Japanese	1416	1384
LMArena Korean	1423	1393
LMArena Russian	1436	1415
LMArena Spanish	1454	1448

Instruction Following Too close to call

GLM-5: 75.2 (#67), Qwen3.8 27B: 75.8 (#53)

Instruction Following benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Instruction Following	1428	1439

Long Context Too close to call

GLM-5: 44.7 (#60), Qwen3.8 27B: 44.3 (#70)

Long Context benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Longer Query	1446	1450
CL-bench	18.7%	—

Writing & Preference Too close to call

GLM-5: 66.0 (#38), Qwen3.8 27B: 65.8 (#43)

Writing & Preference benchmarks
Benchmark	GLM-5	Qwen3.8 27B
LMArena Text	1446	1441
LMArena Creative Writing	1439	1384
EQ-Bench Creative Writing	1601	1671
LMArena Multi-Turn	1456	1441

Frequently asked questions

Is GLM-5 better than Qwen3.8 27B?

GLM-5 and Qwen3.8 27B score almost the same on the Noometry Index (46.1 vs 46.0), so choose on price, context window or the category you care about most.

Which is cheaper, GLM-5 or Qwen3.8 27B?

Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; GLM-5 lists at $1 and $3.20.

Is GLM-5 or Qwen3.8 27B better for coding?

Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 49.0 in the Noometry coding category.

Which has the bigger context window?

Qwen3.8 27B does, with 262K tokens against 205K.

How many benchmarks do GLM-5 and Qwen3.8 27B share?

23 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3.8 27B has 31.