Qwen3.8 27B vs GLM 5.1

Qwen3.8 27B leads llmrun Score 122.1 to 117.8 (ranges overlap); only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 5.1 needs 209.1 GB even at its smallest quant.

Model A · Alibaba

Qwen3.8 27B

122.1llmrun Score

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

VisionFunctions

Model B · Z.ai

GLM 5.1

117.8llmrun Score

753.9B · 42.4B active · 198K context · MIT · Apr 2026

ChatFunctions

Which should you choose?

Choose Qwen3.8 27B if you want…

Higher llmrun Score
122.1 vs 117.8, ranges overlap
Stronger at reasoning
124.0 vs 115.6
Ahead on the benchmarks
3 of 4 shared boards
Fits a 12 GB GPU
IQ3_XXS · 12 GB; GLM 5.1 needs 209.1 GB at its smallest quant
Fits a Mac 32 GB
Q4_K_M · 17.9 GB; GLM 5.1 needs 209.1 GB at its smallest quant
Lighter on memory
17.4 vs 456.5 GB at Q4_K_M

Choose GLM 5.1 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 5.1

8 GB

RTX 4060 · 8 GB tier

Qwen3.8 27B

Doesn't fit

smallest quant needs 8.8 GB

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

12 GB

RTX 3060 12GB · 12 GB tier

Qwen3.8 27B

IQ3_XXS · ~20 tok/s

12 GB · up to 16Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 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 5.1

Doesn't fit

smallest quant needs 209.1 GB

24 GBreference

RTX 4090 · 24 GB tier

Qwen3.8 27B

Q4_K_M · ~38 tok/s

17.9 GB · up to 64Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

32 GB

RTX 5090 · 32 GB tier

Qwen3.8 27B

Q4_K_M · ~67 tok/s

17.9 GB · up to 256Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

48 GB

RTX A6000 · 48 GB tier

Qwen3.8 27B

Q4_K_M · ~29 tok/s

17.9 GB · up to 256Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

Mac 32 GB

Mac Mini M4 · Macs

Qwen3.8 27B

Q4_K_M · ~5 tok/s

17.9 GB · up to 128Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 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 5.1

Doesn't fit

smallest quant needs 209.1 GB

Mac 128 GB

Mac Studio M4 Max · Macs

Qwen3.8 27B

Q4_K_M · ~22 tok/s

17.9 GB · up to 256Kfits

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

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 5.1

Overall

Qwen3.8 27B

122.1120–124

GLM 5.1

117.8115–121

Coding

Qwen3.8 27B

119.7114–125

GLM 5.1

114.5107–122

Agents

Qwen3.8 27B

122.8117–129

GLM 5.1

122.7116–129

Math

Qwen3.8 27B

117.5111–124

GLM 5.1

117.4113–122

Science

Qwen3.8 27B

112.093–131

GLM 5.1

116.8106–127

Reasoning

Qwen3.8 27B

124.0121–127

GLM 5.1

115.6105–126

Qwen3.8 27B · #11 of 100 open models · #1 of 35 that fit 24 GB

GLM 5.1 · #15 of 100 open models · #13 of 96 that fit ≤512 GB

Benchmarks

4 shared boards: Qwen3.8 27B leads 3, GLM 5.1 leads 1 · Benchmarks imported Oct 2026

BoardQwen3.8 27BGLM 5.1

SciCode

Qwen3.8 27B

46.6%#19/66 open

Lab-reportedEpoch AI · Aug 2026

GLM 5.1

44.8%#25/66 open

Lab-reportedEpoch AI · Apr 2026

APEX-Agents

Qwen3.8 27B

47.5%#5/16 open

Lab-reportedEpoch AI · Aug 2026

GLM 5.1

40.9%#8/16 open

Lab-reportedEpoch AI · Apr 2026

CritPt

Qwen3.8 27B

5.4%#15/66 open

Lab-reportedEpoch AI · Aug 2026

GLM 5.1

4.6%#17/66 open

Lab-reportedEpoch AI · Apr 2026

ProofBench

Qwen3.8 27B

16.0%#16/26 open

Lab-reportedEpoch AI · Aug 2026

GLM 5.1

22.2%#10/26 open

Lab-reportedEpoch AI · Apr 2026

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

LMArena Text

Qwen3.8 27B

1437.8#22/187 open

ArenaLMArena · Oct 2026

GLM 5.1

1465.2#8/187 open

ArenaLMArena · Oct 2026

LMArena WebDev

Qwen3.8 27B

1593.2#10/52 open

ArenaLMArena · Oct 2026

GLM 5.1

1508.5#15/52 open

ArenaLMArena · Oct 2026

Only Qwen3.8 27B (15)

Only GLM 5.1 (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 5.1

Q2_K

Qwen3.8 27B

12.6 GB10.8 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 5.1

325 GB265.8 GB file

32K 324 GB · 128K 332 GB

Q3_K_S

Qwen3.8 27B

12.9 GB12.7 GB file

32K 14.2 GB · 128K 19.6 GB

GLM 5.1

334 GB313.5 GB file

32K 333 GB · 128K 342 GB

Q3_K_M

Qwen3.8 27B

14.3 GB13.4 GB file

32K 15.6 GB · 128K 21.0 GB

GLM 5.1

372 GB338.4 GB file

32K 371 GB · 128K 380 GB

Q4_0

Qwen3.8 27B

14.6 GB~13.9 GB file

32K 16.0 GB · 128K 21.4 GB

GLM 5.1

381 GB427.6 GB file

32K 380 GB · 128K 389 GB

Q4_K_M

Qwen3.8 27B

17.4 GB16.5 GB file

32K 18.8 GB · 128K 24.1 GB

GLM 5.1

457 GB464.5 GB file

32K 456 GB · 128K 464 GB

Q5_K_M

Qwen3.8 27B

20.5 GB19.8 GB file

32K 21.9 GB · 128K 27.3 GB

GLM 5.1

541 GB558.5 GB file

32K 540 GB · 128K 549 GB

Q6_K

Qwen3.8 27B

23.7 GB22.0 GB file

32K 25.0 GB · 128K 30.4 GB

GLM 5.1

626 GB621.3 GB file

32K 625 GB · 128K 634 GB

Q8_0

Qwen3.8 27B

28.5 GB29.0 GB file

32K 29.9 GB · 128K 35.2 GB

GLM 5.1

758 GB801.3 GB file

32K 757 GB · 128K 766 GB

17 more quants

IQ2_XXS

Qwen3.8 27B

8.4 GB7.3 GB file

32K 9.7 GB · 128K 15.1 GB

GLM 5.1

212 GB220.6 GB file

32K 211 GB · 128K 219 GB

IQ2_XS

Qwen3.8 27B

9.1 GB9.1 GB file

32K 10.4 GB · 128K 15.8 GB

GLM 5.1

230 GB226.6 GB file

32K 229 GB · 128K 238 GB

IQ2_S

Qwen3.8 27B

9.4 GB8.4 GB file

32K 10.8 GB · 128K 16.1 GB

GLM 5.1

240 GB231.4 GB file

32K 239 GB · 128K 248 GB

IQ2_M

Qwen3.8 27B

10.1 GB10.5 GB file

32K 11.5 GB · 128K 16.8 GB

GLM 5.1

259 GB236.2 GB file

32K 258 GB · 128K 267 GB

IQ3_XXS

Qwen3.8 27B

11.5 GB10.9 GB file

32K 12.9 GB · 128K 18.2 GB

GLM 5.1

296 GB268.3 GB file

32K 295 GB · 128K 304 GB

IQ3_XS

Qwen3.8 27B

12.2 GB10.4 GB file

32K 13.6 GB · 128K 18.9 GB

GLM 5.1

315 GB343.9 GB file

32K 314 GB · 128K 323 GB

IQ3_S

Qwen3.8 27B

12.6 GB12.0 GB file

32K 13.9 GB · 128K 19.3 GB

GLM 5.1

325 GB279.6 GB file

32K 324 GB · 128K 332 GB

IQ3_M

Qwen3.8 27B

13.3 GB14.9 GB file

32K 14.6 GB · 128K 20.0 GB

GLM 5.1

343 GB360.3 GB file

32K 342 GB · 128K 351 GB

Q3_K_L

Qwen3.8 27B

15.0 GB14.1 GB file

32K 16.3 GB · 128K 21.7 GB

GLM 5.1

391 GB358.9 GB file

32K 390 GB · 128K 398 GB

IQ4_XS

Qwen3.8 27B

15.7 GB14.3 GB file

32K 17.0 GB · 128K 22.4 GB

GLM 5.1

409 GB361.3 GB file

32K 408 GB · 128K 417 GB

IQ4_NL

Qwen3.8 27B

16.4 GB17.4 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 5.1

428 GB368.9 GB file

32K 427 GB · 128K 436 GB

Q4_1

Qwen3.8 27B

16.4 GB17.5 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 5.1

428 GB472.7 GB file

32K 427 GB · 128K 436 GB

Q4_K_S

Qwen3.8 27B

16.4 GB15.4 GB file

32K 17.7 GB · 128K 23.1 GB

GLM 5.1

428 GB433.9 GB file

32K 427 GB · 128K 436 GB

Q4_K_L

Qwen3.8 27B

17.8 GB18.8 GB file

32K 19.1 GB · 128K 24.5 GB

GLM 5.1

466 GB460.4 GB file

32K 465 GB · 128K 474 GB

Q5_K_S

Qwen3.8 27B

19.9 GB18.7 GB file

32K 21.2 GB · 128K 26.6 GB

GLM 5.1

523 GB525.9 GB file

32K 522 GB · 128K 530 GB

Q5_K_L

Qwen3.8 27B

20.9 GB21.5 GB file

32K 22.2 GB · 128K 27.6 GB

GLM 5.1

not listed

BF16

Qwen3.8 27B

56.3 GB54.7 GB file

32K 57.7 GB · 128K 63.0 GB

GLM 5.1

1512 GB1508.0 GB file

32K 1511 GB · 128K 1520 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 5.1

Ollama · Q4_K_M

$ollama run hf.co/unsloth/GLM-5.1-GGUF:Q4_K_M

All ways to run GLM 5.1 →

Specifications

Specifications of Qwen3.8 27B and GLM 5.1
Spec	Qwen3.8 27B	GLM 5.1
Publisher	Alibaba	Z.ai
Parameters	27.8B	753.9B
Active parameters	—	42B per token
Architecture	Qwen3_5ForConditionalGeneration	GlmMoeDsaForCausalLM
Context	256K tokens	198K tokens
Modalities	Vision, Function-calling	Chat, Function-calling
License	Apache 2.0	MIT
Released	Aug 2026	Apr 2026

Frequently Asked Questions

Which is better, Qwen3.8 27B or GLM 5.1?

Qwen3.8 27B leads llmrun Score 122.1 to 117.8 (ranges overlap); only Qwen3.8 27B fits 24 GB (Q4_K_M); GLM 5.1 needs 209.1 GB even at its smallest quant. Choose Qwen3.8 27B for higher llmrun Score and stronger at reasoning; choose GLM 5.1 for also does chat.

Which needs less VRAM, Qwen3.8 27B or GLM 5.1?

At Q4_K_M, Qwen3.8 27B requires 17.4 GB while GLM 5.1 needs 456.5 GB, making Qwen3.8 27B the easier model to run on your local setup.

Can I run Qwen3.8 27B or GLM 5.1 on a 16 GB GPU?

Qwen3.8 27B fits at Q3_K_L; GLM 5.1 does not fit (smallest quant needs 209.1 GB).

Which has a longer context window, Qwen3.8 27B or GLM 5.1?

Qwen3.8 27B supports 262,144 tokens and GLM 5.1 supports 202,752 tokens.