DeepSeek V3.2 vs GLM 4.7
DeepSeek V3.2 and GLM 4.7 match in llmrun Score, with values of 110.4 versus 109.1; neither fits 24 GB.
Model A · DeepSeek
DeepSeek V3.2685.4B · 41B active · 160K context · MIT · Dec 2025
Model B · Z.ai
GLM 4.7358.3B · 35.6B active · 198K context · MIT · Dec 2025
Choose GLM 4.7 if you want…
Lighter on memory
215.6 vs 415.1 GB at Q4_K_M
Also does function-calling
DeepSeek V3.2 doesn't list it
DeepSeek V3.2: no clear edge over GLM 4.7 in the data we have.
On your hardware
Each model at its default quant (Q4_K_M), a smaller one when it must. Estimates, ±20%.
8 GB
RTX 4060 · 8 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
12 GB
RTX 3060 12GB · 12 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
16 GB
RTX 5060 Ti 16GB · 16 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
24 GBreference
RTX 4090 · 24 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
32 GB
RTX 5090 · 32 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
48 GB
RTX A6000 · 48 GB tier
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
Mac 32 GB
Mac Mini M4 · Macs
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
Mac 64 GB
Mac Studio M4 Max · Macs
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
Doesn't fit
smallest quant needs 101.4 GB
Mac 128 GB
Mac Studio M4 Max · Macs
DeepSeek V3.2
Doesn't fit
smallest quant needs 189.9 GB
GLM 4.7
IQ2_M · ~20 tok/s
123.8 GB · up to 16Kfits
Reference GPU: RTX 4090. Tinted cell = the better pick on that machine (fits, more headroom, or at least 10% faster).
Overall
DeepSeek V3.2
GLM 4.7
Coding
DeepSeek V3.2
no data
GLM 4.7
Agents
DeepSeek V3.2
GLM 4.7
Math
DeepSeek V3.2
no data
GLM 4.7
Science
DeepSeek V3.2
no data
GLM 4.7
Reasoning
DeepSeek V3.2
GLM 4.7
DeepSeek V3.2 · #25 of 100 open models · #22 of 96 that fit ≤512 GB
GLM 4.7 · #29 of 100 open models · #18 of 84 that fit ≤256 GB
DeepSeek V3.2
39.6%#5/16 open
Lab-reportedEpoch AI · Dec 2025
GLM 4.7
33.4%#8/16 open
Lab-reportedEpoch AI · Dec 2025
DeepSeek V3.2
12.4%#6/7 open
Lab-reportedEpoch AI · Dec 2025
GLM 4.7
15.9%#4/7 open
Lab-reportedEpoch AI · Dec 2025
DeepSeek V3.2
7.4%#5/6 open
Lab-reportedEpoch AI · Dec 2025
GLM 4.7
10.9%#3/6 open
Lab-reportedEpoch AI · Dec 2025
DeepSeek V3.2
8.0%#18/26 open
Lab-reportedEpoch AI · Dec 2025
GLM 4.7
6.0%#20/26 open
Lab-reportedEpoch AI · Dec 2025
21 more boards (other shared and single-model boards)
DeepSeek V3.2
1424.6#29/187 open
ArenaLMArena · Oct 2026
GLM 4.7
1441.3#18/187 open
ArenaLMArena · Oct 2026
DeepSeek V3.2
1362.4#33/52 open
ArenaLMArena · Oct 2026
GLM 4.7
1435.3#25/52 open
ArenaLMArena · Oct 2026
Only DeepSeek V3.2 (10)
- SWE-bench Verified70.0%
- ARC-AGI-24.0%
- MMLU-Pro85.0%
- SWE-bench Multilingual59.0%
- SWE-bench Bash Only60.0%
- ARC-AGI57.0%
- SWE-bench Lite30.7%
- APEX-Agents21.3%
- DTBench85.6%
- LMCA28.8%
Only GLM 4.7 (9)
- GPQA Diamond83.3%
- AIME 2024/202583.3%
- SciCode45.1%
- SimpleQA32.2%
- SimpleBench47.7%
- Chess Puzzles21.0%
- CritPt1.7%
- Vending-Bench 22376.8
- ALE-Bench399.5
VRAM by quantization
Memory to load the model and 2K context; below it, the total at 32K and 128K context. Lighter side in bold.
Q2_K
DeepSeek V3.2
295 GB245.4 GB file
32K 294 GB · 128K 301 GB
GLM 4.7
153 GB131.2 GB file
32K 158 GB · 128K 173 GB
Q3_K_S
DeepSeek V3.2
304 GB290.4 GB file
32K 302 GB · 128K 309 GB
GLM 4.7
157 GB155.0 GB file
32K 162 GB · 128K 178 GB
Q3_K_M
DeepSeek V3.2
338 GB320.2 GB file
32K 337 GB · 128K 344 GB
GLM 4.7
175 GB171.3 GB file
32K 180 GB · 128K 196 GB
Q4_0
DeepSeek V3.2
347 GB380.1 GB file
32K 345 GB · 128K 352 GB
GLM 4.7
180 GB203.0 GB file
32K 185 GB · 128K 200 GB
Q4_K_M
DeepSeek V3.2
415 GB405.4 GB file
32K 414 GB · 128K 421 GB
GLM 4.7
216 GB216.5 GB file
32K 220 GB · 128K 236 GB
Q5_K_M
DeepSeek V3.2
492 GB476.2 GB file
32K 491 GB · 128K 498 GB
GLM 4.7
256 GB254.2 GB file
32K 261 GB · 128K 276 GB
Q6_K
DeepSeek V3.2
569 GB551.3 GB file
32K 568 GB · 128K 575 GB
GLM 4.7
296 GB294.4 GB file
32K 301 GB · 128K 317 GB
Q8_0
DeepSeek V3.2
689 GB713.3 GB file
32K 688 GB · 128K 695 GB
GLM 4.7
359 GB381.0 GB file
32K 364 GB · 128K 379 GB
16 more quants
IQ2_XXS
DeepSeek V3.2
192 GB217.1 GB file
32K 191 GB · 128K 198 GB
GLM 4.7
99.2 GB115.9 GB file
32K 104 GB · 128K 119 GB
IQ2_XS
DeepSeek V3.2
not listed
GLM 4.7
108 GB101.5 GB file
32K 113 GB · 128K 128 GB
IQ2_S
DeepSeek V3.2
not listed
GLM 4.7
113 GB102.3 GB file
32K 117 GB · 128K 133 GB
IQ2_M
DeepSeek V3.2
235 GB228.1 GB file
32K 234 GB · 128K 241 GB
GLM 4.7
122 GB122.4 GB file
32K 126 GB · 128K 142 GB
IQ3_XXS
DeepSeek V3.2
269 GB273.1 GB file
32K 268 GB · 128K 275 GB
GLM 4.7
139 GB145.1 GB file
32K 144 GB · 128K 160 GB
IQ3_XS
DeepSeek V3.2
not listed
GLM 4.7
148 GB148.7 GB file
32K 153 GB · 128K 169 GB
IQ3_S
DeepSeek V3.2
not listed
GLM 4.7
153 GB154.6 GB file
32K 158 GB · 128K 173 GB
IQ3_M
DeepSeek V3.2
not listed
GLM 4.7
162 GB164.8 GB file
32K 167 GB · 128K 182 GB
Q3_K_L
DeepSeek V3.2
not listed
GLM 4.7
184 GB170.6 GB file
32K 189 GB · 128K 205 GB
IQ4_XS
DeepSeek V3.2
372 GB358.3 GB file
32K 371 GB · 128K 378 GB
GLM 4.7
193 GB191.6 GB file
32K 198 GB · 128K 213 GB
IQ4_NL
DeepSeek V3.2
389 GB379.1 GB file
32K 388 GB · 128K 395 GB
GLM 4.7
202 GB202.5 GB file
32K 207 GB · 128K 222 GB
Q4_1
DeepSeek V3.2
389 GB420.8 GB file
32K 388 GB · 128K 395 GB
GLM 4.7
202 GB224.6 GB file
32K 207 GB · 128K 222 GB
Q4_K_S
DeepSeek V3.2
389 GB381.1 GB file
32K 388 GB · 128K 395 GB
GLM 4.7
202 GB203.7 GB file
32K 207 GB · 128K 222 GB
Q4_K_L
DeepSeek V3.2
not listed
GLM 4.7
220 GB219.1 GB file
32K 225 GB · 128K 240 GB
Q5_K_S
DeepSeek V3.2
475 GB462.6 GB file
32K 474 GB · 128K 481 GB
GLM 4.7
247 GB247.0 GB file
32K 252 GB · 128K 267 GB
BF16
DeepSeek V3.2
1375 GB1342.3 GB file
32K 1373 GB · 128K 1380 GB
GLM 4.7
717 GB716.8 GB file
32K 722 GB · 128K 738 GB
DeepSeek V3.2
Ollama · Q4_K_M
ollama run hf.co/unsloth/DeepSeek-V3.2-GGUF:Q4_K_MGLM 4.7
Ollama · Q4_K_M
ollama run hf.co/unsloth/GLM-4.7-GGUF:Q4_K_M| Spec | DeepSeek V3.2 | GLM 4.7 |
|---|---|---|
| Publisher | DeepSeek | Z.ai |
| Parameters | 685.4B | 358.3B |
| Active parameters | 41B per token | 36B per token |
| Architecture | DeepseekV32ForCausalLM | Glm4MoeForCausalLM |
| Context | 160K tokens | 198K tokens |
| Modalities | Chat | Chat, Function-calling |
| License | MIT | MIT |
| Released | Dec 2025 | Dec 2025 |
Frequently Asked Questions
Which is better, DeepSeek V3.2 or GLM 4.7?
DeepSeek V3.2 and GLM 4.7 match in llmrun Score, with values of 110.4 versus 109.1; neither fits 24 GB. Choose GLM 4.7 for lighter on memory and also does function-calling.
Which needs less VRAM, DeepSeek V3.2 or GLM 4.7?
At Q4_K_M, DeepSeek V3.2 requires 415.1 GB while GLM 4.7 needs 215.6 GB, making GLM 4.7 the easier model to run on your local setup.
Can I run DeepSeek V3.2 or GLM 4.7 on a 16 GB GPU?
DeepSeek V3.2 does not fit (smallest quant needs 189.9 GB); GLM 4.7 does not fit (smallest quant needs 101.4 GB).
Which has a longer context window, DeepSeek V3.2 or GLM 4.7?
DeepSeek V3.2 supports 163,840 tokens and GLM 4.7 supports 202,752 tokens.