DeepSeek V3.1 Terminus vs GLM 5.3
GLM 5.3 leads llmrun Score 130.1 to 107.3; neither fits 24 GB.
Model A · DeepSeek
DeepSeek V3.1 Terminus684.5B · 40.1B active · 160K context · MIT · Sep 2025
Model B · Z.ai
GLM 5.3753.3B · 41.8B active · 1M context · Other · Aug 2026
Choose DeepSeek V3.1 Terminus if you want…
More permissive license
MIT vs Other
Choose GLM 5.3 if you want…
Higher llmrun Score
130.1 vs 107.3
Stronger at science
130.6 vs 99.4
Stronger at coding
129.9 vs 107.1
Ahead on the benchmarks
5 of 5 shared boards
Longer context window
1M vs 160K tokens
Newer release
Aug 2026 vs Sep 2025
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 V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
12 GB
RTX 3060 12GB · 12 GB tier
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
16 GB
RTX 5060 Ti 16GB · 16 GB tier
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
24 GBreference
RTX 4090 · 24 GB tier
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
32 GB
RTX 5090 · 32 GB tier
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
48 GB
RTX A6000 · 48 GB tier
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
Mac 32 GB
Mac Mini M4 · Macs
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
Mac 64 GB
Mac Studio M4 Max · Macs
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 GB
GLM 5.3
Doesn't fit
smallest quant needs 256 GB
Mac 128 GB
Mac Studio M4 Max · Macs
DeepSeek V3.1 Terminus
Doesn't fit
smallest quant needs 189.7 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).
Overall
DeepSeek V3.1 Terminus
GLM 5.3
Coding
DeepSeek V3.1 Terminus
GLM 5.3
Agents
DeepSeek V3.1 Terminus
no data
GLM 5.3
Math
DeepSeek V3.1 Terminus
no data
GLM 5.3
Science
DeepSeek V3.1 Terminus
GLM 5.3
Reasoning
DeepSeek V3.1 Terminus
GLM 5.3
DeepSeek V3.1 Terminus · #40 of 106 open models · #36 of 102 that fit ≤512 GB
GLM 5.3 · #6 of 106 open models · #4 of 102 that fit ≤512 GB
Benchmarks
5 shared boards: DeepSeek V3.1 Terminus leads 0, GLM 5.3 leads 5 · Benchmarks imported Nov 2026
DeepSeek V3.1 Terminus
38.0%#45/72 open
Lab-reportedEpoch AI · Sep 2025
GLM 5.3
59.0%#4/72 open
Lab-reportedEpoch AI · Aug 2026
DeepSeek V3.1 Terminus
745.2#25/48 open
Lab-reportedEpoch AI · Sep 2025
GLM 5.3
1317.4#6/48 open
Lab-reportedEpoch AI · Aug 2026
DeepSeek V3.1 Terminus
1.7%#33/84 open
Lab-reportedEpoch AI · Sep 2025
GLM 5.3
19.1%#7/84 open
Lab-reportedEpoch AI · Aug 2026
DeepSeek V3.1 Terminus
81.3%#22/70 open
Lab-reportedEpoch AI · Sep 2025
GLM 5.3
87.7%#12/70 open
Lab-reportedEpoch AI · Aug 2026
DeepSeek V3.1 Terminus
28.6%#27/56 open
Lab-reportedEpoch AI · Sep 2025
GLM 5.3
55.5%#1/56 open
Lab-reportedEpoch AI · Aug 2026
27 more boards (other shared and single-model boards)
DeepSeek V3.1 Terminus
1416.1#39/188 open
ArenaLMArena · Oct 2026
GLM 5.3
1480.6#2/188 open
ArenaLMArena · Oct 2026
Only GLM 5.3 (26)
- GPQA Diamond90.9%
- LiveBench Coding79.0
- LiveBench Reasoning85.8
- AIME 2024/202591.1%
- FrontierMath68.8%
- LMArena WebDev1622.8
- LiveBench Math87.9
- SimpleQA41.0%
- FrontierMath Tier 429.3%
- WeirdML75.4%
- DeepSWE69.0%
- FrontierCode40.1%
- APEX-Agents56.6%
- Chess Puzzles37.0%
- LiveBench 2026 Reasoning85.8
- Mystery Game Puzzles33.0%
- Vending-Bench 28163.6
- LiveBench 2026 Browser0.0
- LiveBench 2026 Data Analysis70.2
- CursorBench42.6%
- FrontierSWE30.2%
- LiveBench 2026 Coding82.8
- LiveBench 2026 Long-Horizon35.9
- LiveBench 2026 Design77.3
- ProofBench49.0%
- LiveBench 2026 Research73.2
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 V3.1 Terminus
295 GB245.7 GB file
32K 294 GB · 128K 300 GB
GLM 5.3
324 GB~320.2 GB file
32K 323 GB · 128K 332 GB
Q3_K_S
DeepSeek V3.1 Terminus
303 GB290.4 GB file
32K 302 GB · 128K 309 GB
GLM 5.3
not listed
Q3_K_M
DeepSeek V3.1 Terminus
338 GB320.5 GB file
32K 336 GB · 128K 343 GB
GLM 5.3
371 GB~367.3 GB file
32K 370 GB · 128K 379 GB
Q4_0
DeepSeek V3.1 Terminus
346 GB380.1 GB file
32K 345 GB · 128K 352 GB
GLM 5.3
not listed
Q4_K_M
DeepSeek V3.1 Terminus
415 GB405.4 GB file
32K 413 GB · 128K 420 GB
GLM 5.3
456 GB~452.0 GB file
32K 455 GB · 128K 464 GB
Q5_K_M
DeepSeek V3.1 Terminus
492 GB476.2 GB file
32K 490 GB · 128K 497 GB
GLM 5.3
541 GB~536.8 GB file
32K 540 GB · 128K 549 GB
Q6_K
DeepSeek V3.1 Terminus
569 GB551.3 GB file
32K 567 GB · 128K 574 GB
GLM 5.3
626 GB~621.5 GB file
32K 625 GB · 128K 634 GB
Q8_0
DeepSeek V3.1 Terminus
688 GB713.3 GB file
32K 687 GB · 128K 694 GB
GLM 5.3
758 GB801.4 GB file
32K 757 GB · 128K 765 GB
14 more quants
IQ2_XXS
DeepSeek V3.1 Terminus
192 GB217.1 GB file
32K 191 GB · 128K 198 GB
GLM 5.3
not listed
IQ2_XS
DeepSeek V3.1 Terminus
209 GB188.4 GB file
32K 208 GB · 128K 215 GB
GLM 5.3
not listed
IQ2_S
DeepSeek V3.1 Terminus
218 GB189.6 GB file
32K 217 GB · 128K 223 GB
GLM 5.3
not listed
IQ2_M
DeepSeek V3.1 Terminus
235 GB228.9 GB file
32K 234 GB · 128K 241 GB
GLM 5.3
258 GB238.6 GB file
32K 257 GB · 128K 266 GB
IQ3_XXS
DeepSeek V3.1 Terminus
269 GB273.5 GB file
32K 268 GB · 128K 275 GB
GLM 5.3
296 GB281.7 GB file
32K 295 GB · 128K 304 GB
IQ3_XS
DeepSeek V3.1 Terminus
286 GB277.1 GB file
32K 285 GB · 128K 292 GB
GLM 5.3
not listed
IQ3_M
DeepSeek V3.1 Terminus
312 GB307.9 GB file
32K 311 GB · 128K 318 GB
GLM 5.3
not listed
Q3_K_L
DeepSeek V3.1 Terminus
355 GB319.7 GB file
32K 353 GB · 128K 360 GB
GLM 5.3
not listed
IQ4_XS
DeepSeek V3.1 Terminus
372 GB358.3 GB file
32K 371 GB · 128K 377 GB
GLM 5.3
409 GB365.3 GB file
32K 408 GB · 128K 417 GB
IQ4_NL
DeepSeek V3.1 Terminus
389 GB379.1 GB file
32K 388 GB · 128K 395 GB
GLM 5.3
not listed
Q4_1
DeepSeek V3.1 Terminus
389 GB420.8 GB file
32K 388 GB · 128K 395 GB
GLM 5.3
not listed
Q4_K_S
DeepSeek V3.1 Terminus
389 GB381.1 GB file
32K 388 GB · 128K 395 GB
GLM 5.3
not listed
Q5_K_S
DeepSeek V3.1 Terminus
475 GB462.6 GB file
32K 473 GB · 128K 480 GB
GLM 5.3
not listed
BF16
DeepSeek V3.1 Terminus
1373 GB1342.3 GB file
32K 1372 GB · 128K 1379 GB
GLM 5.3
1511 GB1508.0 GB file
32K 1510 GB · 128K 1519 GB
DeepSeek V3.1 Terminus
Ollama · Q4_K_M
ollama run hf.co/unsloth/DeepSeek-V3.1-Terminus-GGUF:Q4_K_MGLM 5.3
Ollama · Q4_K_M
ollama run hf.co/unsloth/GLM-5.3-GGUF| Spec | DeepSeek V3.1 Terminus | GLM 5.3 |
|---|---|---|
| Publisher | DeepSeek | Z.ai |
| Parameters | 684.5B | 753.3B |
| Active parameters | 40B per token | 42B per token |
| Architecture | DeepseekV3ForCausalLM | GlmMoeDsaForCausalLM |
| Context | 160K tokens | 1M tokens |
| Modalities | Chat, Function-calling | Chat, Function-calling |
| License | MIT | Other |
| Released | Sep 2025 | Aug 2026 |
Frequently Asked Questions
Which is better, DeepSeek V3.1 Terminus or GLM 5.3?
GLM 5.3 leads llmrun Score 130.1 to 107.3; neither fits 24 GB. Choose DeepSeek V3.1 Terminus for more permissive license; choose GLM 5.3 for higher llmrun Score and stronger at science.
Which needs less VRAM, DeepSeek V3.1 Terminus or GLM 5.3?
At Q4_K_M, DeepSeek V3.1 Terminus requires 414.6 GB while GLM 5.3 needs 456.2 GB, making DeepSeek V3.1 Terminus the easier model to run on your local setup.
Can I run DeepSeek V3.1 Terminus or GLM 5.3 on a 16 GB GPU?
DeepSeek V3.1 Terminus does not fit (smallest quant needs 189.7 GB); GLM 5.3 does not fit (smallest quant needs 256 GB).
Which has a longer context window, DeepSeek V3.1 Terminus or GLM 5.3?
DeepSeek V3.1 Terminus supports 163,840 tokens and GLM 5.3 supports 1,048,576 tokens.