GLM-5.3 753B vs DeepSeek V4 Pro 0813 1.6T
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
GLM-5.3 753B is more hardware-efficient: it needs 515.1 GB at its Q4_K_M build vs 1091.4 GB for DeepSeek V4 Pro 0813 1.6T's Q4_K_M, fitting on 2 GPUs natively.
Analysis
GLM-5.3 and DeepSeek V4 Pro 0813 are both frontier, text-only, datacenter-only open-weight releases from August 2026, and DeepSeek is the bigger model on paper: 1,600B total parameters against GLM-5.3's 753B. On the one benchmark both self-report using the same suite name, Terminal-Bench 2.1, they land within a third of a point of each other, and each model wins a real share of the other agentic benchmarks both vendors happen to report.
GLM-5.3 caches all 78 of its layers with Multi-head Latent Attention and no further compression. DeepSeek V4 Pro 0813 caches all 61 of its layers too, but its hybrid Compressed Sparse Attention/Heavily Compressed Attention design squeezes each one to roughly a tenth of an uncompressed cache. That compression wins decisively: at the 1,048,576-token context both models reach natively, GLM-5.3's KV cache is 167.5 GB against DeepSeek's 13.1 GB, less than a tenth as much, even though DeepSeek caches nearly as many layers (61 against 78). Weights track total parameter count more closely: at each model's own recommended Q2_K estimate, GLM-5.3 needs about 286.9 GB against DeepSeek's roughly 609.6 GB, and the full picture at the shared 1,048,576-token context is 508.9 GB for GLM-5.3 against 697.4 GB for DeepSeek, 37% more. On Terminal-Bench 2.1, the two are close: 88.2 for GLM-5.3 against 87.9 for DeepSeek, each vendor's own comparison run. The rest of Z.ai's own benchmark table, which includes DeepSeek V4 Pro 0813 as a reference column, splits between the two: GLM-5.3 leads on DeepSWE v1.1 (66.9 against 62.7), CyberGym (84.5 against 83.3), and HLE with tools (62.5 against 60.0), while DeepSeek leads on NL2Repo (61.1 against 58.0) and Toolathlon Verified (74.1 against 73.0), suites closer to conventional repository-scale software engineering than GLM-5.3's coding-and-security focus. DeepSeek was not evaluated on ExploitBench at all in Z.ai's table, so no comparison is possible on the suite where GLM-5.3's post-training gain is largest. Licensing favors DeepSeek clearly: unrestricted MIT, against GLM-5.3's custom GLM-5.3 License, which only bites a Model-as-a-Service business over $10 billion in trailing 12-month revenue but is still not plain MIT. Neither model accepts image or video input.
Bottom line: These two are close enough on shared benchmarks that the choice comes down to what each model is actually good at and what license you can accept, not raw capability. DeepSeek V4 Pro 0813 is the more conventional software-engineering agent (NL2Repo, Toolathlon Verified) with a cleaner MIT license, while GLM-5.3 is the more security-capable of the two (CyberGym, and the ExploitBench suite DeepSeek wasn't even tested on) at meaningfully less VRAM, 508.9 GB against 697.4 GB at matching context. Neither is remotely single-GPU territory: both need a genuine multi-GPU datacenter server regardless of which you pick.
Active vs. total parameters: both MoE, different ratios
Both models are MoE. DeepSeek is the larger model on both axes, 1,600B total against GLM-5.3's 753B and 49B active against 40B, but GLM-5.3 activates the larger share of its own total (5.3% against 3.1%).
What the shared 1,048,576-token context costs each model
GLM-5.3 and DeepSeek V4 Pro 0813 both natively reach the same 1,048,576-token context and cache nearly all of their layers (78 of 78 against 61 of 61), but DeepSeek's hybrid attention compresses what it stores while GLM-5.3's does not.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At the full 1,048,576-token window, GLM-5.3's KV cache is 167.5 GB against DeepSeek V4 Pro 0813's 13.1 GB, less than a tenth as much, purely from DeepSeek's roughly 10x per-layer compression rather than any difference in how many layers cache. Add each model's own recommended-quant weights and the full picture is 508.9 GB for GLM-5.3 against 697.4 GB for DeepSeek, 37% more, with the gap narrower than the KV-cache difference alone would suggest because DeepSeek's much larger weight footprint (609.6 GB against 286.9 GB) dominates its own total.
CyberGym and NL2Repo: each model wins one
Neither suite has a field in this site's benchmark schema. Both come from Z.ai's own GLM-5.3 model card, which includes DeepSeek V4 Pro 0813 as a reference column; DeepSeek's own figures there match what DeepSeek itself has separately published, so they're each vendor's own reported score, not an independent re-run.
Z.ai, GLM-5.3 model card benchmark table (huggingface.co/zai-org/GLM-5.3, 25 August 2026). CyberGym and NL2Repo scores are each vendor's own self-reported figures; independent reproduction was not yet available at time of writing.
VRAM at each quantization (8k context)
| Quant | GLM-5.3 753B | DeepSeek V4 Pro 0813 1.6T | Diff |
|---|---|---|---|
| FP32 | 3374.9 GB | 7168.1 GB | -53% |
| BF16 | 1688.2 GB | 3584.1 GB | -53% |
| FP16 | 1688.2 GB | 3584.1 GB | -53% |
| Q8_0 | 898.0 GB | 1905.0 GB | -53% |
| Q6_K | 693.9 GB | 1471.3 GB | -53% |
| Q5_K_M | 601.9 GB | 1276.0 GB | -53% |
| Q4_K_M | 515.1 GB | 1091.4 GB | -53% |
| Q3_K_M | 407.1 GB | 862.1 GB | -53% |
| Q2_K | 322.8 GB | 682.9 GB | -53% |
| NVFP4 | 423.1 GB | 896.1 GB | -53% |
Diff is GLM-5.3 753B relative to DeepSeek V4 Pro 0813 1.6T. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | GLM-5.3 753B | DeepSeek V4 Pro 0813 1.6T |
|---|---|---|
| Org | Z.ai | DeepSeek |
| Parameters | 753B | 1600B |
| Architecture | MoE (40B active) | MoE (49B active) |
| Context | 1024k tokens | 1024k tokens |
| Modalities | text | text |
| License | GLM-5.3 | MIT |
| Commercial | Yes | Yes |
| Released | 2026-08-25 | 2026-08-12 |
| GPUs (native) | 2 / 119 | 0 / 119 |
Benchmark scores
| Benchmark | GLM-5.3 753B | DeepSeek V4 Pro 0813 1.6T |
|---|---|---|
| GPQA Diamond | 91.7 | N/A |
| Terminal-Bench 2.1 | 88.2 | 87.9 |
Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.
GPUs that run only GLM-5.3 753B(2)
- Apple M5 Ultra (512GB)512 GB
- Apple M3 Ultra (512GB)512 GB
GPUs that run only DeepSeek V4 Pro 0813 1.6T(0)
Every GPU that runs DeepSeek V4 Pro 0813 1.6T also runs GLM-5.3 753B.
Which should you use?
- • You have limited VRAM: it's a smaller model needing 515.1 GB vs 1091.4 GB
- • You're running coding tasks
- • You need chain-of-thought reasoning
- • It's the newer release (2026-08-25 vs 2026-08-12); check the benchmark table above for what actually improved
- • You want maximum capability and have a 1092 GB+ GPU
Frequently asked questions
- Which is better, GLM-5.3 753B or DeepSeek V4 Pro 0813 1.6T?
- GLM-5.3 753B has 753B parameters vs 1600B for DeepSeek V4 Pro 0813 1.6T, so DeepSeek V4 Pro 0813 1.6T is the larger model. GLM-5.3 753B is more hardware-efficient, needing 515.1 GB at its Q4_K_M build vs 1091.4 GB for DeepSeek V4 Pro 0813 1.6T's Q4_K_M. GLM-5.3 753B runs on more GPUs natively (2 vs 0).
- How much VRAM does GLM-5.3 753B need vs DeepSeek V4 Pro 0813 1.6T?
- At 8k context, GLM-5.3 753B needs approximately 515.1 GB of VRAM at its Q4_K_M build, while DeepSeek V4 Pro 0813 1.6T needs 1091.4 GB at its Q4_K_M build. At the largest build each ships, GLM-5.3 753B requires 1688.2 GB (FP16) vs 3584.1 GB (FP16) for DeepSeek V4 Pro 0813 1.6T.
- Can you run GLM-5.3 753B on the same GPUs as DeepSeek V4 Pro 0813 1.6T?
- These models have very different VRAM requirements, so they do not share the same compatible GPU set.
- What is the difference between GLM-5.3 753B and DeepSeek V4 Pro 0813 1.6T?
- GLM-5.3 753B has 753B parameters (40B active, MoE) with a 1024k context window. DeepSeek V4 Pro 0813 1.6T has 1600B parameters (49B active, MoE) with a 1024k context window. Licensing differs: GLM-5.3 753B is GLM-5.3 while DeepSeek V4 Pro 0813 1.6T is MIT.
- Which model fits in 24 GB of VRAM, GLM-5.3 753B or DeepSeek V4 Pro 0813 1.6T?
- Neither fits in 24 GB: GLM-5.3 753B needs 515.1 GB at Q4_K_M and DeepSeek V4 Pro 0813 1.6T needs 1091.4 GB at Q4_K_M. Both require a multi-GPU server with 1092 GB+ of combined VRAM.
- Which handles long context better, GLM-5.3 753B or DeepSeek V4 Pro 0813 1.6T?
- At the full 1,048,576-token window, GLM-5.3's KV cache is 167.5 GB against DeepSeek V4 Pro 0813's 13.1 GB, less than a tenth as much, purely from DeepSeek's roughly 10x per-layer compression rather than any difference in how many layers cache. Add each model's own recommended-quant weights and the full picture is 508.9 GB for GLM-5.3 against 697.4 GB for DeepSeek, 37% more, with the gap narrower than the KV-cache difference alone would suggest because DeepSeek's much larger weight footprint (609.6 GB against 286.9 GB) dominates its own total.