Muse Spark Open 816B-A42B vs GLM-5.3 753B
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 560.3 GB for Muse Spark Open 816B-A42B's Q4_K_M, fitting on 2 GPUs natively.
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Prices as of Oct 13, 2026. Rates change; check RunPod for current pricing. Affiliate link: CanItRun may earn a commission. Doesn't affect the fit calculation above.
Analysis
Muse Spark Open and GLM-5.3 are near twins on paper: 820B against 753B total parameters, 42B against 40B active per token, the same native 1M-token window, and releases seven weeks apart (GLM-5.3's weights on August 25, Spark Open's on October 15, 2026). They also split the evidence. Meta's own launch table has Spark Open ahead on tool-use suites such as MCP-Atlas (89.2 against 84.2, Meta's figures, not independently reproduced), but Artificial Analysis, which ran both through one harness, puts GLM-5.3 six points higher on its Intelligence Index, 45 against 39.
Hardware does not separate them much. On one 512 GB Apple M5 Ultra both land on Q3_K_M (407.1 GB for GLM-5.3, 442.8 GB for Spark Open), and on two of them GLM-5.3 reaches Q8_0 (898.0 GB) where Spark Open stops at Q6_K (755.0 GB), because its weights are smaller at the same quant (458.6 GB against 499.4 GB at Q4_K_M). Context length flips that: Spark Open caches only 16 of its 62 layers at full length while GLM-5.3 caches all 78, so at long context GLM-5.3 needs more memory (the chart below has the numbers). On quality, GLM-5.3 is ahead on seven of the nine rows charted below, most sharply on Artificial Analysis's harder Terminal-Bench 4.0 and on GDPval-AA, a test of real-world work tasks. Spark Open leads Humanity's Last Exam and AA-Omniscience accuracy, guesses less when it does not know (when it cannot answer correctly it gives a wrong answer 24.1% of the time instead of declining, against 29.6% for GLM-5.3), and finished the Index in about 110M output tokens against GLM-5.3's 210M. Per Meta it also takes video and audio input, which text-only GLM-5.3 does not.
Bottom line: Pick GLM-5.3 if raw capability on coding and agentic work is the goal: it leads the independent composite and seven of the nine rows, and by this site's calculator it stretches further on two Mac Studios (Q8_0 against Q6_K). Pick Muse Spark Open if you need video or audio input, expect very long contexts, or care about token economy and calibration: it used roughly half the output tokens to finish the Index, and its cache overtakes GLM-5.3's memory bill beyond about 437,000 tokens. On licensing, GLM-5.3 asks for a Z.ai security review only from Model-as-a-Service operators above $10 billion in revenue; Spark Open requires a separate agreement with Meta above $20 million, plus a "Built with Muse Spark Open" notice. The hardware does not decide this one; the workload does.
What the full 1M-token window costs each model
Both models reach 1,048,576 tokens natively, but they shrink the KV cache (the memory a model keeps of the conversation so far) in different ways. Spark Open caps 46 of its 62 layers at a 2,048-token window and leaves 16 at full length. GLM-5.3 caches all 78 layers, but stores each one as a compressed latent.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At the full window GLM-5.3's KV cache is 167.5 GB against Spark Open's 69.1 GB, 2.4 times larger. GLM-5.3 starts lighter, with smaller weights (458.6 GB against 499.4 GB at Q4_K_M, and 515.1 GB against 560.3 GB in total at 8k context), but its cache grows 2.4 times faster per token, so the two totals cross at about 437,000 tokens and Spark Open is the lighter model after that: 636.7 GB against 701.2 GB at 1M.
Same harness, independent scores: Artificial Analysis's measurements
Meta's table and Z.ai's own report each lead with the suites their model does best on. Artificial Analysis ran both models through one harness (Intelligence Index v4.3.2), so these are the fairest like-for-like numbers available, and they favour GLM-5.3 on most rows.
Artificial Analysis, Intelligence Index v4.3.2, model pages retrieved 15 October 2026: Muse Spark Open at xhigh reasoning effort, GLM-5.3 at max. Scores are percentages; the composite is Artificial Analysis's index of ten evaluations. These are third-party measurements, not this site's own, and may be rescored as its index changes. The benchmark table above shows Z.ai's own Terminal-Bench 2.1 figure for GLM-5.3 (88.2), which is higher than Artificial Analysis's run (83.9) charted here.
VRAM at each quantization (8k context)
| Quant | Muse Spark Open 816B-A42B | GLM-5.3 753B | Diff |
|---|---|---|---|
| FP32 | 3674.6 GB | 3374.9 GB | +9% |
| BF16 | 1837.8 GB | 1688.2 GB | +9% |
| FP16 | 1837.8 GB | 1688.2 GB | +9% |
| Q8_0 | 977.3 GB | 898.0 GB | +9% |
| Q6_K | 755.0 GB | 693.9 GB | +9% |
| Q5_K_M | 654.9 GB | 601.9 GB | +9% |
| Q4_K_M | 560.3 GB | 515.1 GB | +9% |
| Q3_K_M | 442.8 GB | 407.1 GB | +9% |
| Q2_K | 350.9 GB | 322.8 GB | +9% |
| NVFP4 | 598.0 GB | 423.1 GB | +41% |
Diff is Muse Spark Open 816B-A42B relative to GLM-5.3 753B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Muse Spark Open 816B-A42B | GLM-5.3 753B |
|---|---|---|
| Org | Meta | Z.ai |
| Parameters | 820B | 753B |
| Architecture | MoE (42B active) | MoE (40B active) |
| Context | 1024k tokens | 1024k tokens |
| Modalities | text, vision, video, audio | text |
| License | Muse Model License | GLM-5.3 |
| Commercial | Yes | Yes |
| Released | 2026-10-15 | 2026-08-25 |
| GPUs (native) | 2 / 121 | 2 / 121 |
Benchmark scores
| Benchmark | Muse Spark Open 816B-A42B | GLM-5.3 753B |
|---|---|---|
| GPQA Diamond | 89.0 | 91.7 |
| Terminal-Bench 2.1 | 76.4 | 88.2 |
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 Muse Spark Open 816B-A42B(0)
Every GPU that runs Muse Spark Open 816B-A42B also runs GLM-5.3 753B.
GPUs that run only GLM-5.3 753B(0)
Every GPU that runs GLM-5.3 753B also runs Muse Spark Open 816B-A42B.
GPUs that run both natively(2)
- Apple M5 Ultra (512GB)512 GB
- Apple M3 Ultra (512GB)512 GB
Which should you use?
- • You want maximum capability and have a 561 GB+ GPU
- • You need vision/image understanding
- • It's the newer release (2026-10-15 vs 2026-08-25); check the benchmark table above for what actually improved
- • You have limited VRAM: it's a smaller model needing 515.1 GB vs 560.3 GB
Frequently asked questions
- Which is better, Muse Spark Open 816B-A42B or GLM-5.3 753B?
- Muse Spark Open 816B-A42B has 820B parameters vs 753B for GLM-5.3 753B, so Muse Spark Open 816B-A42B is the larger model. GLM-5.3 753B is more hardware-efficient, needing 515.1 GB at its Q4_K_M build vs 560.3 GB for Muse Spark Open 816B-A42B's Q4_K_M.
- How much VRAM does Muse Spark Open 816B-A42B need vs GLM-5.3 753B?
- At 8k context, Muse Spark Open 816B-A42B needs approximately 560.3 GB of VRAM at its Q4_K_M build, while GLM-5.3 753B needs 515.1 GB at its Q4_K_M build. At the largest build each ships, Muse Spark Open 816B-A42B requires 1837.8 GB (FP16) vs 1688.2 GB (FP16) for GLM-5.3 753B.
- Can you run Muse Spark Open 816B-A42B on the same GPUs as GLM-5.3 753B?
- Yes, 2 GPUs can run both natively in VRAM, including Apple M5 Ultra (512GB), Apple M3 Ultra (512GB). However, no GPU can run Muse Spark Open 816B-A42B without also fitting GLM-5.3 753B, and no GPU can run GLM-5.3 753B without also fitting Muse Spark Open 816B-A42B.
- What is the difference between Muse Spark Open 816B-A42B and GLM-5.3 753B?
- Muse Spark Open 816B-A42B has 820B parameters (42B active, MoE) with a 1024k context window. GLM-5.3 753B has 753B parameters (40B active, MoE) with a 1024k context window. Licensing differs: Muse Spark Open 816B-A42B is Muse Model License while GLM-5.3 753B is GLM-5.3.
- Which model fits in 24 GB of VRAM, Muse Spark Open 816B-A42B or GLM-5.3 753B?
- Neither fits in 24 GB: Muse Spark Open 816B-A42B needs 560.3 GB at Q4_K_M and GLM-5.3 753B needs 515.1 GB at Q4_K_M. Both require a multi-GPU server with 561 GB+ of combined VRAM.
- Which handles long context better, Muse Spark Open 816B-A42B or GLM-5.3 753B?
- At the full window GLM-5.3's KV cache is 167.5 GB against Spark Open's 69.1 GB, 2.4 times larger. GLM-5.3 starts lighter, with smaller weights (458.6 GB against 499.4 GB at Q4_K_M, and 515.1 GB against 560.3 GB in total at 8k context), but its cache grows 2.4 times faster per token, so the two totals cross at about 437,000 tokens and Spark Open is the lighter model after that: 636.7 GB against 701.2 GB at 1M.