GLM 5.1 vs GLM 5.3

GLM 5.3 leads llmrun Score 130.1 to 117.8; neither fits 24 GB.

Model A · Z.ai

GLM 5.1

117.8llmrun Score

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

ChatFunctions

Model B · Z.ai

GLM 5.3

130.1llmrun Score

753.3B · 41.8B active · 1M context · Other · Aug 2026

ChatFunctions

Which should you choose?

Choose GLM 5.1 if you want…

More permissive license
MIT vs Other

Choose GLM 5.3 if you want…

Higher llmrun Score
130.1 vs 117.8
Stronger at coding
130.1 vs 114.5
Stronger at reasoning
130.1 vs 115.6
Ahead on the benchmarks
11 of 12 shared boards
Longer context window
1M vs 198K tokens
Newer release
Aug 2026 vs Apr 2026

On your hardware

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

MemoryGLM 5.1GLM 5.3

8 GB

RTX 4060 · 8 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

12 GB

RTX 3060 12GB · 12 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

16 GB

RTX 5060 Ti 16GB · 16 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

24 GBreference

RTX 4090 · 24 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

32 GB

RTX 5090 · 32 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

48 GB

RTX A6000 · 48 GB tier

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

Mac 32 GB

Mac Mini M4 · Macs

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

Mac 64 GB

Mac Studio M4 Max · Macs

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 GB

Mac 128 GB

Mac Studio M4 Max · Macs

GLM 5.1

Doesn't fit

smallest quant needs 209.1 GB

GLM 5.3

Doesn't fit

smallest quant needs 256 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

GLM 5.1 GLM 5.3

Overall

GLM 5.1

117.8115–121

GLM 5.3

130.1128–132

Coding

GLM 5.1

114.5107–122

GLM 5.3

130.1124–136

Agents

GLM 5.1

122.7116–129

GLM 5.3

133.4130–137

Math

GLM 5.1

117.4113–122

GLM 5.3

128.6126–131

Science

GLM 5.1

116.8106–127

GLM 5.3

130.8119–143

Reasoning

GLM 5.1

115.6105–126

GLM 5.3

130.1124–136

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

GLM 5.3 · #4 of 100 open models · #3 of 96 that fit ≤512 GB

Benchmarks

12 shared boards: GLM 5.1 leads 1, GLM 5.3 leads 11 · Benchmarks imported Oct 2026

BoardGLM 5.1GLM 5.3

GPQA Diamond

GLM 5.1

89.9%#10/98 open

IndependentEpoch AI · Apr 2026

GLM 5.3

90.9%#7/98 open

IndependentEpoch AI · Aug 2026

AIME 2024/2025

GLM 5.1

93.3%#10/86 open

IndependentEpoch AI · Apr 2026

GLM 5.3

91.1%#13/86 open

IndependentEpoch AI · Aug 2026

FrontierMath

GLM 5.1

36.8%#13/17 open

IndependentEpoch AI · Apr 2026

GLM 5.3

68.8%#3/17 open

IndependentEpoch AI · Aug 2026

SciCode

GLM 5.1

44.8%#25/66 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

59.0%#3/66 open

Lab-reportedEpoch AI · Aug 2026

SimpleQA

GLM 5.1

34.0%#13/17 open

IndependentEpoch AI · Apr 2026

GLM 5.3

41.0%#6/17 open

IndependentEpoch AI · Aug 2026

WeirdML

GLM 5.1

57.1%#6/46 open

IndependentEpoch AI · Apr 2026

GLM 5.3

75.4%#2/46 open

IndependentEpoch AI · Aug 2026

ALE-Bench

GLM 5.1

887.1#15/43 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

1317.4#3/43 open

Lab-reportedEpoch AI · Aug 2026

APEX-Agents

GLM 5.1

40.9%#8/16 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

56.6%#2/16 open

Lab-reportedEpoch AI · Aug 2026

Chess Puzzles

GLM 5.1

20.0%#11/14 open

IndependentEpoch AI · Apr 2026

GLM 5.3

37.0%#4/14 open

IndependentEpoch AI · Aug 2026

CritPt

GLM 5.1

4.6%#17/66 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

19.1%#4/66 open

Lab-reportedEpoch AI · Aug 2026

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

ProofBench

GLM 5.1

22.2%#10/26 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

49.0%#8/26 open

Lab-reportedEpoch AI · Aug 2026

Vending-Bench 2

GLM 5.1

5634.4#4/17 open

Lab-reportedEpoch AI · Apr 2026

GLM 5.3

8163.6#2/17 open

Lab-reportedEpoch AI · Aug 2026

LMArena Text

GLM 5.1

1465.2#8/187 open

ArenaLMArena · Oct 2026

GLM 5.3

1478.5#3/187 open

ArenaLMArena · Oct 2026

LMArena WebDev

GLM 5.1

1508.5#15/52 open

ArenaLMArena · Oct 2026

GLM 5.3

1621.1#6/52 open

ArenaLMArena · Oct 2026

Only GLM 5.1 (2)

SimpleBench55.1%
ExploitBench18.1%

Only GLM 5.3 (18)

VRAM by quantization

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

QuantGLM 5.1GLM 5.3

Q2_K

GLM 5.1

325 GB265.8 GB file

32K 324 GB · 128K 332 GB

GLM 5.3

324 GB~320.2 GB file

32K 323 GB · 128K 332 GB

Q3_K_S

GLM 5.1

334 GB313.5 GB file

32K 333 GB · 128K 342 GB

GLM 5.3

not listed

Q3_K_M

GLM 5.1

372 GB338.4 GB file

32K 371 GB · 128K 380 GB

GLM 5.3

371 GB~367.3 GB file

32K 370 GB · 128K 379 GB

Q4_0

GLM 5.1

381 GB427.6 GB file

32K 380 GB · 128K 389 GB

GLM 5.3

not listed

Q4_K_M

GLM 5.1

457 GB464.5 GB file

32K 456 GB · 128K 464 GB

GLM 5.3

456 GB~452.0 GB file

32K 455 GB · 128K 464 GB

Q5_K_M

GLM 5.1

541 GB558.5 GB file

32K 540 GB · 128K 549 GB

GLM 5.3

541 GB~536.8 GB file

32K 540 GB · 128K 549 GB

Q6_K

GLM 5.1

626 GB621.3 GB file

32K 625 GB · 128K 634 GB

GLM 5.3

626 GB~621.5 GB file

32K 625 GB · 128K 634 GB

Q8_0

GLM 5.1

758 GB801.3 GB file

32K 757 GB · 128K 766 GB

GLM 5.3

758 GB801.4 GB file

32K 757 GB · 128K 765 GB

16 more quants

IQ2_XXS

GLM 5.1

212 GB220.6 GB file

32K 211 GB · 128K 219 GB

GLM 5.3

not listed

IQ2_XS

GLM 5.1

230 GB226.6 GB file

32K 229 GB · 128K 238 GB

GLM 5.3

not listed

IQ2_S

GLM 5.1

240 GB231.4 GB file

32K 239 GB · 128K 248 GB

GLM 5.3

not listed

IQ2_M

GLM 5.1

259 GB236.2 GB file

32K 258 GB · 128K 267 GB

GLM 5.3

258 GB238.6 GB file

32K 257 GB · 128K 266 GB

IQ3_XXS

GLM 5.1

296 GB268.3 GB file

32K 295 GB · 128K 304 GB

GLM 5.3

296 GB281.7 GB file

32K 295 GB · 128K 304 GB

IQ3_XS

GLM 5.1

315 GB343.9 GB file

32K 314 GB · 128K 323 GB

GLM 5.3

not listed

IQ3_S

GLM 5.1

325 GB279.6 GB file

32K 324 GB · 128K 332 GB

GLM 5.3

not listed

IQ3_M

GLM 5.1

343 GB360.3 GB file

32K 342 GB · 128K 351 GB

GLM 5.3

not listed

Q3_K_L

GLM 5.1

391 GB358.9 GB file

32K 390 GB · 128K 398 GB

GLM 5.3

not listed

IQ4_XS

GLM 5.1

409 GB361.3 GB file

32K 408 GB · 128K 417 GB

GLM 5.3

409 GB365.3 GB file

32K 408 GB · 128K 417 GB

IQ4_NL

GLM 5.1

428 GB368.9 GB file

32K 427 GB · 128K 436 GB

GLM 5.3

not listed

Q4_1

GLM 5.1

428 GB472.7 GB file

32K 427 GB · 128K 436 GB

GLM 5.3

not listed

Q4_K_S

GLM 5.1

428 GB433.9 GB file

32K 427 GB · 128K 436 GB

GLM 5.3

not listed

Q4_K_L

GLM 5.1

466 GB460.4 GB file

32K 465 GB · 128K 474 GB

GLM 5.3

not listed

Q5_K_S

GLM 5.1

523 GB525.9 GB file

32K 522 GB · 128K 530 GB

GLM 5.3

not listed

BF16

GLM 5.1

1512 GB1508.0 GB file

32K 1511 GB · 128K 1520 GB

GLM 5.3

1511 GB1508.0 GB file

32K 1510 GB · 128K 1519 GB

How to run each

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 →

GLM 5.3

Ollama · Q4_K_M

$ollama run hf.co/unsloth/GLM-5.3-GGUF

All ways to run GLM 5.3 →

Specifications

Specifications of GLM 5.1 and GLM 5.3
Spec	GLM 5.1	GLM 5.3
Publisher	Z.ai	Z.ai
Parameters	753.9B	753.3B
Active parameters	42B per token	42B per token
Architecture	GlmMoeDsaForCausalLM	GlmMoeDsaForCausalLM
Context	198K tokens	1M tokens
Modalities	Chat, Function-calling	Chat, Function-calling
License	MIT	Other
Released	Apr 2026	Aug 2026

Frequently Asked Questions

Which is better, GLM 5.1 or GLM 5.3?

GLM 5.3 leads llmrun Score 130.1 to 117.8; neither fits 24 GB. Choose GLM 5.1 for more permissive license; choose GLM 5.3 for higher llmrun Score and stronger at coding.

Which needs less VRAM, GLM 5.1 or GLM 5.3?

At Q4_K_M, GLM 5.1 requires 456.5 GB while GLM 5.3 needs 456.2 GB, making GLM 5.3 the easier model to run on your local setup.

Can I run GLM 5.1 or GLM 5.3 on a 16 GB GPU?

GLM 5.1 does not fit (smallest quant needs 209.1 GB); GLM 5.3 does not fit (smallest quant needs 256 GB).

Which has a longer context window, GLM 5.1 or GLM 5.3?

GLM 5.1 supports 202,752 tokens and GLM 5.3 supports 1,048,576 tokens.