Qwen3.8 27B vs GLM 4.6

Qwen3.8 27B leads llmrun Score 121.5 to 103.1; only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 4.6 needs 101 GB even at its smallest quant.

Model A · Alibaba

Qwen3.8 27B

121.5llmrun Score

27.8B · 256K context · Apache 2.0 · Aug 2026

VisionFunctions

Model B · Z.ai

GLM 4.6

103.1llmrun Score

356.8B · 34B active · 198K context · MIT · Sep 2025

ChatFunctions

Which should you choose?

Choose Qwen3.8 27B if you want…

Higher llmrun Score
121.5 vs 103.1
Stronger at coding
112.1 vs 96.0
Stronger at science
112.0 vs 99.8
Ahead on the benchmarks
4 of 4 shared boards
Fits a 12 GB GPU
IQ3_XXS · 12 GB; GLM 4.6 needs 101 GB at its smallest quant
Fits a Mac 32 GB
Q4_K_M · 17.9 GB; GLM 4.6 needs 101 GB at its smallest quant

Choose GLM 4.6 if you want…

Also does chat
Qwen3.8 27B doesn't list it

On your hardware

Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.

MemoryQwen3.8 27BGLM 4.6

8 GB

RTX 4060 · 8 GB tier

Qwen3.8 27B

Doesn't fit

smallest quant needs 8.8 GB

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

12 GB

RTX 3060 12GB · 12 GB tier

Qwen3.8 27B

IQ3_XXS · ~20 tok/s

12 GB · up to 16Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

16 GB

RTX 5060 Ti 16GB · 16 GB tier

Qwen3.8 27B

Q3_K_L · ~19 tok/s

15.4 GB · up to 16Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

24 GBreference

RTX 4090 · 24 GB tier

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

32 GB

RTX 5090 · 32 GB tier

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

48 GB

RTX A6000 · 48 GB tier

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

Mac 32 GB

Mac Mini M4 · Macs

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

Mac 64 GB

Mac Studio M4 Max · Macs

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256Kfits

GLM 4.6

Doesn't fit

smallest quant needs 101 GB

Mac 128 GB

Mac Studio M4 Max · Macs

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256Kmore headroom

GLM 4.6

IQ2_M · ~21 tok/s

123.3 GB · up to 16K

Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).

llmrun Score

with its 90% range · bars share one scale

Qwen3.8 27B GLM 4.6

Overall

Qwen3.8 27B

121.5119–124

GLM 4.6

103.198–108

Coding

Qwen3.8 27B

112.1105–119

GLM 4.6

96.081–111

Agents

Qwen3.8 27B

no data

GLM 4.6

104.399–109

Math

Qwen3.8 27B

117.5111–125

GLM 4.6

no data

Science

Qwen3.8 27B

112.093–131

GLM 4.6

99.878–121

Reasoning

Qwen3.8 27B

124.1121–127

GLM 4.6

no data

Qwen3.8 27B · #10 of 92 open models · #1 of 34 that fit 24 GB

GLM 4.6 · #41 of 92 open models · #29 of 77 that fit ≤256 GB

Benchmarks

4 shared boards: Qwen3.8 27B leads 4, GLM 4.6 leads 0 · Benchmarks imported Oct 2026

BoardQwen3.8 27BGLM 4.6

LMArena Text

Qwen3.8 27B

1438.1#22/186 open

ArenaLMArena · Oct 2026

GLM 4.6

1424.2#31/186 open

ArenaLMArena · Oct 2026

LMArena WebDev

Qwen3.8 27B

1592.8#10/50 open

ArenaLMArena · Oct 2026

GLM 4.6

1340.0#38/50 open

ArenaLMArena · Oct 2026

SciCode

Qwen3.8 27B

46.6%#19/65 open

Lab-reportedEpoch AI · Aug 2026

GLM 4.6

38.4%#33/65 open

Lab-reportedEpoch AI · Sep 2025

CritPt

Qwen3.8 27B

5.4%#15/65 open

Lab-reportedEpoch AI · Aug 2026

GLM 4.6

1.1%#32/65 open

Lab-reportedEpoch AI · Sep 2025

14 more boards (other shared and single-model boards)

Only Qwen3.8 27B (10)

Only GLM 4.6 (4)

VRAM by quantization

Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.

QuantQwen3.8 27BGLM 4.6

Q2_K

Qwen3.8 27B

12.6 GB10.8 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 4.6

152 GB130.7 GB file

32K 157 GB · 128K 173 GB

Q3_K_S

Qwen3.8 27B

12.9 GB12.7 GB file

32K 14.2 GB · 128K 19.6 GB

GLM 4.6

157 GB154.4 GB file

32K 162 GB · 128K 177 GB

Q3_K_M

Qwen3.8 27B

14.3 GB13.4 GB file

32K 15.6 GB · 128K 21.0 GB

GLM 4.6

175 GB170.6 GB file

32K 179 GB · 128K 195 GB

Q4_0

Qwen3.8 27B

14.6 GB~13.9 GB file

32K 16.0 GB · 128K 21.4 GB

GLM 4.6

179 GB202.2 GB file

32K 184 GB · 128K 199 GB

Q4_K_M

Qwen3.8 27B

17.4 GB16.5 GB file

32K 18.8 GB · 128K 24.1 GB

GLM 4.6

215 GB215.6 GB file

32K 220 GB · 128K 235 GB

Q5_K_M

Qwen3.8 27B

20.5 GB19.8 GB file

32K 21.9 GB · 128K 27.3 GB

GLM 4.6

255 GB253.2 GB file

32K 260 GB · 128K 275 GB

Q6_K

Qwen3.8 27B

23.7 GB22.0 GB file

32K 25.0 GB · 128K 30.4 GB

GLM 4.6

295 GB293.1 GB file

32K 300 GB · 128K 315 GB

Q8_0

Qwen3.8 27B

28.5 GB29.0 GB file

32K 29.9 GB · 128K 35.2 GB

GLM 4.6

357 GB379.3 GB file

32K 362 GB · 128K 378 GB

17 more quants

IQ2_XXS

Qwen3.8 27B

8.4 GB7.3 GB file

32K 9.7 GB · 128K 15.1 GB

GLM 4.6

98.7 GB115.4 GB file

32K 104 GB · 128K 119 GB

IQ2_XS

Qwen3.8 27B

9.1 GB9.1 GB file

32K 10.4 GB · 128K 15.8 GB

GLM 4.6

108 GB101.0 GB file

32K 112 GB · 128K 128 GB

IQ2_S

Qwen3.8 27B

9.4 GB8.4 GB file

32K 10.8 GB · 128K 16.1 GB

GLM 4.6

112 GB101.9 GB file

32K 117 GB · 128K 132 GB

IQ2_M

Qwen3.8 27B

10.1 GB10.5 GB file

32K 11.5 GB · 128K 16.8 GB

GLM 4.6

121 GB121.9 GB file

32K 126 GB · 128K 141 GB

IQ3_XXS

Qwen3.8 27B

11.5 GB10.9 GB file

32K 12.9 GB · 128K 18.2 GB

GLM 4.6

139 GB144.7 GB file

32K 144 GB · 128K 159 GB

IQ3_XS

Qwen3.8 27B

12.2 GB10.4 GB file

32K 13.6 GB · 128K 18.9 GB

GLM 4.6

148 GB148.2 GB file

32K 153 GB · 128K 168 GB

IQ3_S

Qwen3.8 27B

12.6 GB12.0 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 4.6

not listed

IQ3_M

Qwen3.8 27B

13.3 GB14.9 GB file

32K 14.6 GB · 128K 20.0 GB

GLM 4.6

161 GB164.3 GB file

32K 166 GB · 128K 181 GB

Q3_K_L

Qwen3.8 27B

15.0 GB14.1 GB file

32K 16.3 GB · 128K 21.7 GB

GLM 4.6

183 GB169.9 GB file

32K 188 GB · 128K 204 GB

IQ4_XS

Qwen3.8 27B

15.7 GB14.3 GB file

32K 17.0 GB · 128K 22.4 GB

GLM 4.6

192 GB190.7 GB file

32K 197 GB · 128K 213 GB

IQ4_NL

Qwen3.8 27B

16.4 GB17.4 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 4.6

201 GB201.6 GB file

32K 206 GB · 128K 222 GB

Q4_1

Qwen3.8 27B

16.4 GB17.5 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 4.6

201 GB223.6 GB file

32K 206 GB · 128K 222 GB

Q4_K_S

Qwen3.8 27B

16.4 GB15.4 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 4.6

201 GB202.8 GB file

32K 206 GB · 128K 222 GB

Q4_K_L

Qwen3.8 27B

17.8 GB18.8 GB file

32K 19.1 GB · 128K 24.5 GB

GLM 4.6

not listed

Q5_K_S

Qwen3.8 27B

19.9 GB18.7 GB file

32K 21.2 GB · 128K 26.6 GB

GLM 4.6

246 GB245.9 GB file

32K 251 GB · 128K 266 GB

Q5_K_L

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

GLM 4.6

not listed

BF16

Qwen3.8 27B

56.3 GB54.7 GB file

32K 57.7 GB · 128K 63.0 GB

GLM 4.6

714 GB713.7 GB file

32K 719 GB · 128K 734 GB

How to run each

Qwen3.8 27B

Ollama · Q4_K_M

$ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:Q4_K_M

All ways to run Qwen3.8 27B →

GLM 4.6

Ollama · Q4_K_M

$ollama run hf.co/unsloth/GLM-4.6-GGUF:Q4_K_M

All ways to run GLM 4.6 →

Specifications

Specifications of Qwen3.8 27B and GLM 4.6
Spec	Qwen3.8 27B	GLM 4.6
Publisher	Alibaba	Z.ai
Parameters	27.8B	356.8B
Active parameters	—	34B per token
Architecture	Qwen3_5ForConditionalGeneration	Glm4MoeForCausalLM
Context	256K tokens	198K tokens
Modalities	Vision, Function-calling	Chat, Function-calling
License	Apache 2.0	MIT
Released	Aug 2026	Sep 2025

Frequently Asked Questions

Which is better, Qwen3.8 27B or GLM 4.6?

Qwen3.8 27B leads llmrun Score 121.5 to 103.1; only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 4.6 needs 101 GB even at its smallest quant. Choose Qwen3.8 27B for higher llmrun Score and stronger at coding; choose GLM 4.6 for also does chat.

Which needs less VRAM, Qwen3.8 27B or GLM 4.6?

At Q4_K_M, Qwen3.8 27B requires 17.4 GB while GLM 4.6 needs 214.7 GB, making Qwen3.8 27B the easier model to run on your local setup.

Can I run Qwen3.8 27B or GLM 4.6 on a 16 GB GPU?

Qwen3.8 27B fits at Q3_K_L; GLM 4.6 does not fit (smallest quant needs 101 GB).

Which has a longer context window, Qwen3.8 27B or GLM 4.6?

Qwen3.8 27B supports 262,144 tokens and GLM 4.6 supports 202,752 tokens.