Muse Spark Open 816B-A42B vs DeepSeek V4.1 Flash 552B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
Muse Spark Open 816B-A42B is more hardware-efficient: it needs 560.3 GB at its Q4_K_M build vs 571.6 GB for DeepSeek V4.1 Flash 552B'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 DeepSeek V4.1 Flash tie on Artificial Analysis's Intelligence Index (39 each, ranked 8th and 7th of 117) but get there in opposite ways. DeepSeek V4.1 Flash is the smaller model, 552B total and only 16B active per token against 820B and 42B, with a tiny KV cache and an MIT license. Spark Open is the more careful one, with far fewer confident wrong answers and a lighter appetite for output tokens.
The tie hides opposite profiles. DeepSeek is ahead on six of the ten rows charted below, led by the agentic ones and long-context reasoning; Spark Open leads Humanity's Last Exam, SciCode, CritPt and the visual MMMU-Pro. The sharpest gap is honesty about what a model does not know. On AA-Omniscience DeepSeek has the higher raw accuracy but, when it cannot answer correctly, gives a wrong answer 96.5% of the time instead of declining, against 24.1% for Spark Open, which puts their Omniscience Index at -5 against +25. DeepSeek also spent about 250M output tokens to finish the Index against Spark Open's 110M, though at Artificial Analysis's measured API price ($0.30 per million input tokens, $1.20 per million output, 179.7 tokens per second) an Index task still costs $0.27 on average; Artificial Analysis lists no public API for Spark Open yet. DeepSeek's own report gives GPQA Diamond 90.9 and Terminal-Bench 2.1 90.6 (the table above), but Artificial Analysis has no score for either, so those two cannot be compared like for like.
Bottom line: Pick DeepSeek V4.1 Flash for an agentic workload on a budget: the same Index score, a fraction of the per-task price, an MIT license, a KV cache that barely grows, and faster decoding because only 16B parameters run per token (about 1.7 times faster at the same Q4_K_M quant by this site's estimate, 39.9 against 23.4 tokens per second on two 512 GB M5 Ultras). Pick Muse Spark Open when wrong answers are costly, when you need video or audio input (DeepSeek takes images only), or when you want to run on a single 512 GB Mac: Spark Open fits at Q3_K_M (442.8 GB), while this site counts only DeepSeek's native 510.3 GB checkpoint, which does not fit. That last point is probably too pessimistic for DeepSeek: that file includes about 196B parameters of Engram lookup memory that DeepSeek's card describes as sparsely accessed, NVIDIA's own NVFP4 repository is 429.0 GB as listed on Hugging Face (stock vLLM cannot load that revision yet), and community builds go lower (a 238.8 GB MLX 2-bit and a 210.6 GB EXL3, with one engine streaming the Engram tables from SSD), none of which this site has verified.
Active vs. total parameters: 16B against 42B working per token
Both are mixture-of-experts models, so total parameters set the download size and active parameters set the work done per token. Spark Open has 49% more total parameters than DeepSeek V4.1 Flash but 2.6 times as many active ones, which is why DeepSeek decodes faster on the same hardware.
What the full 1M-token window costs each model
Both models reach 1,048,576 tokens natively. Spark Open does it by capping 46 of its 62 layers at a 2,048-token window; DeepSeek V4.1 Flash compresses the KV cache (the memory a model keeps of the conversation so far) of every layer instead, by a ratio this site estimates from DeepSeek's own published figures rather than an independent measurement.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At 1,048,576 tokens Spark Open's KV cache is 69.1 GB and DeepSeek V4.1 Flash's is 2.3 GB, about 30 times smaller. The totals show what that means for planning: DeepSeek's barely moves between 8k and 1M tokens (571.6 GB to 574.1 GB), while Spark Open's grows from 560.3 GB to 636.7 GB.
Same harness, independent scores: Artificial Analysis's measurements
These are the suites Artificial Analysis ran on both models under one harness (Intelligence Index v4.3.2). DeepSeek is ahead on the composite by half a point and on most agentic rows; Spark Open is ahead on the reasoning, science and visual rows.
Artificial Analysis, Intelligence Index v4.3.2, model pages retrieved 15 October 2026: Muse Spark Open at xhigh reasoning effort, DeepSeek V4.1 Flash 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.
VRAM at each quantization (8k context)
| Quant | Muse Spark Open 816B-A42B | DeepSeek V4.1 Flash 552B | Diff |
|---|---|---|---|
| FP32 | 3674.6 GB | 2473.0 GB | +49% |
| BF16 | 1837.8 GB | 1236.5 GB | +49% |
| FP16 | 1837.8 GB | 1236.5 GB | +49% |
| Q8_0 | 977.3 GB | 657.2 GB | +49% |
| Q6_K | 755.0 GB | 571.6 GB | +32% |
| Q5_K_M | 654.9 GB | 571.6 GB | +15% |
| Q4_K_M | 560.3 GB | 571.6 GB | -2% |
| Q3_K_M | 442.8 GB | 571.6 GB | -23% |
| Q2_K | 350.9 GB | 571.6 GB | -39% |
| NVFP4 | 598.0 GB | 571.6 GB | +5% |
Diff is Muse Spark Open 816B-A42B relative to DeepSeek V4.1 Flash 552B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Muse Spark Open 816B-A42B | DeepSeek V4.1 Flash 552B |
|---|---|---|
| Org | Meta | DeepSeek |
| Parameters | 820B | 552B |
| Architecture | MoE (42B active) | MoE (16B active) |
| Context | 1024k tokens | 1024k tokens |
| Modalities | text, vision, video, audio | text, vision |
| License | Muse Model License | MIT |
| Commercial | Yes | Yes |
| Released | 2026-10-15 | 2026-09-10 |
| GPUs (native) | 2 / 121 | 0 / 121 |
Benchmark scores
| Benchmark | Muse Spark Open 816B-A42B | DeepSeek V4.1 Flash 552B |
|---|---|---|
| GPQA Diamond | 89.0 | 90.9 |
| Terminal-Bench 2.1 | 76.4 | 90.6 |
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(2)
- Apple M5 Ultra (512GB)512 GB
- Apple M3 Ultra (512GB)512 GB
GPUs that run only DeepSeek V4.1 Flash 552B(0)
Every GPU that runs DeepSeek V4.1 Flash 552B also runs Muse Spark Open 816B-A42B.
Which should you use?
- • You want maximum capability and have a 561 GB+ GPU
- • It's the newer release (2026-10-15 vs 2026-09-10); check the benchmark table above for what actually improved
- • You have limited VRAM: it's a smaller model needing 571.6 GB vs 560.3 GB
Frequently asked questions
- Which is better, Muse Spark Open 816B-A42B or DeepSeek V4.1 Flash 552B?
- Muse Spark Open 816B-A42B has 820B parameters vs 552B for DeepSeek V4.1 Flash 552B, so Muse Spark Open 816B-A42B is the larger model. Muse Spark Open 816B-A42B is more hardware-efficient, needing 560.3 GB at its Q4_K_M build vs 571.6 GB for DeepSeek V4.1 Flash 552B's Q4_K_M. Muse Spark Open 816B-A42B runs on more GPUs natively (2 vs 0).
- How much VRAM does Muse Spark Open 816B-A42B need vs DeepSeek V4.1 Flash 552B?
- At 8k context, Muse Spark Open 816B-A42B needs approximately 560.3 GB of VRAM at its Q4_K_M build, while DeepSeek V4.1 Flash 552B needs 571.6 GB at its Q4_K_M build. At the largest build each ships, Muse Spark Open 816B-A42B requires 1837.8 GB (FP16) vs 1236.5 GB (FP16) for DeepSeek V4.1 Flash 552B.
- Can you run Muse Spark Open 816B-A42B on the same GPUs as DeepSeek V4.1 Flash 552B?
- These models have very different VRAM requirements, so they do not share the same compatible GPU set.
- What is the difference between Muse Spark Open 816B-A42B and DeepSeek V4.1 Flash 552B?
- Muse Spark Open 816B-A42B has 820B parameters (42B active, MoE) with a 1024k context window. DeepSeek V4.1 Flash 552B has 552B parameters (16B active, MoE) with a 1024k context window. Licensing differs: Muse Spark Open 816B-A42B is Muse Model License while DeepSeek V4.1 Flash 552B is MIT.
- Which model fits in 24 GB of VRAM, Muse Spark Open 816B-A42B or DeepSeek V4.1 Flash 552B?
- Neither fits in 24 GB: Muse Spark Open 816B-A42B needs 560.3 GB at Q4_K_M and DeepSeek V4.1 Flash 552B needs 571.6 GB at Q4_K_M. Both require a multi-GPU server with 572 GB+ of combined VRAM.
- Which handles long context better, Muse Spark Open 816B-A42B or DeepSeek V4.1 Flash 552B?
- At 1,048,576 tokens Spark Open's KV cache is 69.1 GB and DeepSeek V4.1 Flash's is 2.3 GB, about 30 times smaller. The totals show what that means for planning: DeepSeek's barely moves between 8k and 1M tokens (571.6 GB to 574.1 GB), while Spark Open's grows from 560.3 GB to 636.7 GB.