Qwen3.8 27B vs GLM 4.7

Qwen3.8 27B leads llmrun Score 121.5 to 108.3; only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 4.7 needs 101.4 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 4.7

108.3llmrun Score

358.3B · 35.6B active · 198K context · MIT · Dec 2025

ChatFunctions

Which should you choose?

Choose Qwen3.8 27B if you want…

Higher llmrun Score
121.5 vs 108.3
Stronger at reasoning
124.1 vs 104.3
Stronger at coding
112.1 vs 101.3
Ahead on the benchmarks
4 of 5 shared boards
Fits a 12 GB GPU
IQ3_XXS · 12 GB; GLM 4.7 needs 101.4 GB at its smallest quant
Fits a Mac 32 GB
Q4_K_M · 17.9 GB; GLM 4.7 needs 101.4 GB at its smallest quant

Choose GLM 4.7 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 4.7

8 GB

RTX 4060 · 8 GB tier

Qwen3.8 27B

Doesn't fit

smallest quant needs 8.8 GB

GLM 4.7

Doesn't fit

smallest quant needs 101.4 GB

12 GB

RTX 3060 12GB · 12 GB tier

Qwen3.8 27B

IQ3_XXS · ~20 tok/s

12 GB · up to 16Kfits

GLM 4.7

Doesn't fit

smallest quant needs 101.4 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 4.7

Doesn't fit

smallest quant needs 101.4 GB

24 GBreference

RTX 4090 · 24 GB tier

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64Kfits

GLM 4.7

Doesn't fit

smallest quant needs 101.4 GB

32 GB

RTX 5090 · 32 GB tier

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256Kfits

GLM 4.7

Doesn't fit

smallest quant needs 101.4 GB

48 GB

RTX A6000 · 48 GB tier

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256Kfits

GLM 4.7

Doesn't fit

smallest quant needs 101.4 GB

Mac 32 GB

Mac Mini M4 · Macs

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128Kfits

GLM 4.7

Doesn't fit

smallest quant needs 101.4 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 4.7

Doesn't fit

smallest quant needs 101.4 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 4.7

IQ2_M · ~20 tok/s

123.8 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 4.7

Overall

Qwen3.8 27B

121.5119–124

GLM 4.7

108.3105–112

Coding

Qwen3.8 27B

112.1105–119

GLM 4.7

101.388–115

Agents

Qwen3.8 27B

no data

GLM 4.7

105.898–114

Math

Qwen3.8 27B

117.5111–125

GLM 4.7

111.1104–118

Science

Qwen3.8 27B

112.093–131

GLM 4.7

111.2102–120

Reasoning

Qwen3.8 27B

124.1121–127

GLM 4.7

104.392–116

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

GLM 4.7 · #29 of 92 open models · #18 of 77 that fit ≤256 GB

Benchmarks

5 shared boards: Qwen3.8 27B leads 4, GLM 4.7 leads 0 · Benchmarks imported Oct 2026

BoardQwen3.8 27BGLM 4.7

LMArena Text

Qwen3.8 27B

1438.1#22/186 open

ArenaLMArena · Oct 2026

GLM 4.7

1441.2#18/186 open

ArenaLMArena · Oct 2026

LMArena WebDev

Qwen3.8 27B

1592.8#10/50 open

ArenaLMArena · Oct 2026

GLM 4.7

1435.0#22/50 open

ArenaLMArena · Oct 2026

SciCode

Qwen3.8 27B

46.6%#19/65 open

Lab-reportedEpoch AI · Aug 2026

GLM 4.7

45.1%#20/65 open

Lab-reportedEpoch AI · Dec 2025

CritPt

Qwen3.8 27B

5.4%#15/65 open

Lab-reportedEpoch AI · Aug 2026

GLM 4.7

1.7%#28/65 open

Lab-reportedEpoch AI · Dec 2025

ProofBench

Qwen3.8 27B

16.0%#16/26 open

Lab-reportedEpoch AI · Aug 2026

GLM 4.7

6.0%#20/26 open

Lab-reportedEpoch AI · Dec 2025

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

Only Qwen3.8 27B (9)

Only GLM 4.7 (10)

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 4.7

Q2_K

Qwen3.8 27B

12.6 GB10.8 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 4.7

153 GB131.2 GB file

32K 158 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 4.7

157 GB155.0 GB file

32K 162 GB · 128K 178 GB

Q3_K_M

Qwen3.8 27B

14.3 GB13.4 GB file

32K 15.6 GB · 128K 21.0 GB

GLM 4.7

175 GB171.3 GB file

32K 180 GB · 128K 196 GB

Q4_0

Qwen3.8 27B

14.6 GB~13.9 GB file

32K 16.0 GB · 128K 21.4 GB

GLM 4.7

180 GB203.0 GB file

32K 185 GB · 128K 200 GB

Q4_K_M

Qwen3.8 27B

17.4 GB16.5 GB file

32K 18.8 GB · 128K 24.1 GB

GLM 4.7

216 GB216.5 GB file

32K 220 GB · 128K 236 GB

Q5_K_M

Qwen3.8 27B

20.5 GB19.8 GB file

32K 21.9 GB · 128K 27.3 GB

GLM 4.7

256 GB254.2 GB file

32K 261 GB · 128K 276 GB

Q6_K

Qwen3.8 27B

23.7 GB22.0 GB file

32K 25.0 GB · 128K 30.4 GB

GLM 4.7

296 GB294.4 GB file

32K 301 GB · 128K 317 GB

Q8_0

Qwen3.8 27B

28.5 GB29.0 GB file

32K 29.9 GB · 128K 35.2 GB

GLM 4.7

359 GB381.0 GB file

32K 364 GB · 128K 379 GB

17 more quants

IQ2_XXS

Qwen3.8 27B

8.4 GB7.3 GB file

32K 9.7 GB · 128K 15.1 GB

GLM 4.7

99.2 GB115.9 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 4.7

108 GB101.5 GB file

32K 113 GB · 128K 128 GB

IQ2_S

Qwen3.8 27B

9.4 GB8.4 GB file

32K 10.8 GB · 128K 16.1 GB

GLM 4.7

113 GB102.3 GB file

32K 117 GB · 128K 133 GB

IQ2_M

Qwen3.8 27B

10.1 GB10.5 GB file

32K 11.5 GB · 128K 16.8 GB

GLM 4.7

122 GB122.4 GB file

32K 126 GB · 128K 142 GB

IQ3_XXS

Qwen3.8 27B

11.5 GB10.9 GB file

32K 12.9 GB · 128K 18.2 GB

GLM 4.7

139 GB145.1 GB file

32K 144 GB · 128K 160 GB

IQ3_XS

Qwen3.8 27B

12.2 GB10.4 GB file

32K 13.6 GB · 128K 18.9 GB

GLM 4.7

148 GB148.7 GB file

32K 153 GB · 128K 169 GB

IQ3_S

Qwen3.8 27B

12.6 GB12.0 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 4.7

153 GB154.6 GB file

32K 158 GB · 128K 173 GB

IQ3_M

Qwen3.8 27B

13.3 GB14.9 GB file

32K 14.6 GB · 128K 20.0 GB

GLM 4.7

162 GB164.8 GB file

32K 167 GB · 128K 182 GB

Q3_K_L

Qwen3.8 27B

15.0 GB14.1 GB file

32K 16.3 GB · 128K 21.7 GB

GLM 4.7

184 GB170.6 GB file

32K 189 GB · 128K 205 GB

IQ4_XS

Qwen3.8 27B

15.7 GB14.3 GB file

32K 17.0 GB · 128K 22.4 GB

GLM 4.7

193 GB191.6 GB file

32K 198 GB · 128K 213 GB

IQ4_NL

Qwen3.8 27B

16.4 GB17.4 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 4.7

202 GB202.5 GB file

32K 207 GB · 128K 222 GB

Q4_1

Qwen3.8 27B

16.4 GB17.5 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 4.7

202 GB224.6 GB file

32K 207 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 4.7

202 GB203.7 GB file

32K 207 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 4.7

220 GB219.1 GB file

32K 225 GB · 128K 240 GB

Q5_K_S

Qwen3.8 27B

19.9 GB18.7 GB file

32K 21.2 GB · 128K 26.6 GB

GLM 4.7

247 GB247.0 GB file

32K 252 GB · 128K 267 GB

Q5_K_L

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

GLM 4.7

not listed

BF16

Qwen3.8 27B

56.3 GB54.7 GB file

32K 57.7 GB · 128K 63.0 GB

GLM 4.7

717 GB716.8 GB file

32K 722 GB · 128K 738 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 4.7

Ollama · Q4_K_M

$ollama run hf.co/unsloth/GLM-4.7-GGUF:Q4_K_M

All ways to run GLM 4.7 →

Specifications

Specifications of Qwen3.8 27B and GLM 4.7
Spec	Qwen3.8 27B	GLM 4.7
Publisher	Alibaba	Z.ai
Parameters	27.8B	358.3B
Active parameters	—	36B 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	Dec 2025

Frequently Asked Questions

Which is better, Qwen3.8 27B or GLM 4.7?

Qwen3.8 27B leads llmrun Score 121.5 to 108.3; only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 4.7 needs 101.4 GB even at its smallest quant. Choose Qwen3.8 27B for higher llmrun Score and stronger at reasoning; choose GLM 4.7 for also does chat.

Which needs less VRAM, Qwen3.8 27B or GLM 4.7?

At Q4_K_M, Qwen3.8 27B requires 17.4 GB while GLM 4.7 needs 215.6 GB, making Qwen3.8 27B the easier model to run on your local setup.

Can I run Qwen3.8 27B or GLM 4.7 on a 16 GB GPU?

Qwen3.8 27B fits at Q3_K_L; GLM 4.7 does not fit (smallest quant needs 101.4 GB).

Which has a longer context window, Qwen3.8 27B or GLM 4.7?

Qwen3.8 27B supports 262,144 tokens and GLM 4.7 supports 202,752 tokens.