Muse Spark Open 816B-A42B vs Qwen3.8 2.4T-A95B
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 1669.3 GB for Qwen3.8 2.4T-A95B's Q4_K_M, fitting on 2 GPUs natively.
Neither fits a consumer GPU? Run Qwen3.8 2.4T-A95B on 8× B300 288GB from $55.52/hr ↗
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
Qwen3.8 2.4T-A95B is almost three times the size of Muse Spark Open (2,446B against 820B total parameters, 95B against 42B active) and, on Artificial Analysis's Intelligence Index, one point ahead (40 against 39, ranked 5th and 8th of 117). This page is about what that extra size buys and what it costs, because the two sit on very different hardware tiers.
Qwen3.8 leads six of the nine rows charted below, mostly on knowledge and reasoning (GPQA Diamond, CritPt) and on agentic work (GDPval-AA, AutomationBench). Spark Open leads Humanity's Last Exam, SciCode and honesty about what it does not know (AA-Omniscience Index +25 against +4). Qwen3.8 is also text-only, always thinks, has a native window a quarter the size (262,144 against 1,048,576 tokens), and is slower and dearer on Artificial Analysis's measured API ($2.00 and $6.00 per million input and output tokens, 39.1 tokens per second). The decisive gap is memory. At Q4_K_M the weights alone are 1,489.7 GB against 499.4 GB. By this site's calculator no one or two 512 GB M5 Ultras hold Qwen3.8 at any quant, four B300s hold it only with offload, and eight B300s fit it at 4-bit (1,370.7 GB at 8k context) where four hold Spark Open (598.0 GB). The smallest real build is Unsloth's UD-Q1_0 at 397.3 GB, about 1.3 bits per weight, the same size as Spark Open's UD-Q3_K_XL (401.5 GB, about 3.9 bits).
Bottom line: Qwen3.8 2.4T-A95B is the stronger model by a small margin (one Index point, 4.5 points on GPQA Diamond), and worth its weight only if you already have an eight-B300-class node or are paying per token ($2.16 per Index task on Artificial Analysis's pricing; Spark Open has no listed API price yet). For that margin it asks for three times the weights and a quarter of the native context. For almost everyone running open weights themselves, Muse Spark Open is the practical pick: it fits one 512 GB Mac at Q3_K_M or two at Q6_K, accepts video and audio, keeps its 1M-token window, and finishes the same Index in over a third fewer output tokens (about 110M against 170M). The one-bit route to Qwen3.8 on a single 512 GB Mac exists, but we found no independent measurement of what 1.3 bits per weight does to a model like this, so treat it as an experiment. On licensing, Qwen3.8-Max is free for commercial use up to $20M a month in revenue, while Spark Open asks for a separate agreement from Model-as-a-Service operators above $20M a year, plus a "Built with Muse Spark Open" notice.
Active vs. total parameters: three times the weights, 2.3 times the work per token
Qwen3.8 has 3.0 times Spark Open's total parameters (the download and memory bill) but only 2.3 times its active parameters (the compute per token), so its extra size is cheaper to run than to store.
What Qwen3.8's 262,144-token ceiling costs each model
Qwen3.8 2.4T-A95B stops at 262,144 tokens natively, a quarter of Spark Open's window. Over that range Qwen caches 23 of its 92 layers at full length (the rest use a linear-attention design that keeps a fixed-size state), while Spark Open caps 46 of its 62 layers at a 2,048-token window.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At 262,144 tokens Spark Open's KV cache is 17.6 GB and Qwen3.8's is 24.7 GB, about 40% larger even though it caches a smaller share of its layers, because each cached layer is wide (4 KV heads of 256 dimensions). Either way the cache is small beside the weights: the totals at Q4_K_M are 579.0 GB for Spark Open and 1,696.2 GB for Qwen3.8, so the weights, not the context, decide which hardware you need.
Same harness, independent scores: Artificial Analysis's measurements
Meta's launch table includes Qwen3.8 2.4T-A95B as a comparator, but these rows are Artificial Analysis's own runs of both models under one harness (Intelligence Index v4.3.2), so the two models are measured the same way.
Artificial Analysis, Intelligence Index v4.3.2, model pages retrieved 15 October 2026: Muse Spark Open at xhigh reasoning effort, Qwen3.8 2.4T-A95B in its default always-on thinking mode. 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 Alibaba's own figures for Qwen3.8 (GPQA Diamond 92.6, Terminal-Bench 2.1 86.6), which differ from Artificial Analysis's runs charted here.
VRAM at each quantization (8k context)
| Quant | Muse Spark Open 816B-A42B | Qwen3.8 2.4T-A95B | Diff |
|---|---|---|---|
| FP32 | 3674.6 GB | 10959.8 GB | -66% |
| BF16 | 1837.8 GB | 5480.3 GB | -66% |
| FP16 | 1837.8 GB | 5480.3 GB | -66% |
| Q8_0 | 977.3 GB | 2913.2 GB | -66% |
| Q6_K | 755.0 GB | 2250.2 GB | -66% |
| Q5_K_M | 654.9 GB | 1951.5 GB | -66% |
| Q4_K_M | 560.3 GB | 1669.3 GB | -66% |
| Q3_K_M | 442.8 GB | 1318.7 GB | -66% |
| Q2_K | 350.9 GB | 1044.7 GB | -66% |
| NVFP4 | 598.0 GB | 1370.7 GB | -56% |
Diff is Muse Spark Open 816B-A42B relative to Qwen3.8 2.4T-A95B. Green = lower VRAM (fits more GPUs).
Model specifications
| Spec | Muse Spark Open 816B-A42B | Qwen3.8 2.4T-A95B |
|---|---|---|
| Org | Meta | Alibaba |
| Parameters | 820B | 2446.18B |
| Architecture | MoE (42B active) | MoE (95B active) |
| Context | 1024k tokens | 256k tokens |
| Modalities | text, vision, video, audio | text |
| License | Muse Model License | Qwen3.8-Max |
| Commercial | Yes | Yes |
| Released | 2026-10-15 | 2026-08-08 |
| GPUs (native) | 2 / 121 | 0 / 121 |
Benchmark scores
| Benchmark | Muse Spark Open 816B-A42B | Qwen3.8 2.4T-A95B |
|---|---|---|
| GPQA Diamond | 89.0 | 92.6 |
| Terminal-Bench 2.1 | 76.4 | 86.6 |
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 Qwen3.8 2.4T-A95B(0)
Every GPU that runs Qwen3.8 2.4T-A95B also runs Muse Spark Open 816B-A42B.
Which should you use?
- • You have limited VRAM: it's a smaller model needing 560.3 GB vs 1669.3 GB
- • Long context matters: it supports 1024k tokens vs 256k
- • You need vision/image understanding
- • It's the newer release (2026-10-15 vs 2026-08-08); check the benchmark table above for what actually improved
- • You want maximum capability and have a 1670 GB+ GPU
Frequently asked questions
- Which is better, Muse Spark Open 816B-A42B or Qwen3.8 2.4T-A95B?
- Muse Spark Open 816B-A42B has 820B parameters vs 2446.18B for Qwen3.8 2.4T-A95B, so Qwen3.8 2.4T-A95B is the larger model. Muse Spark Open 816B-A42B is more hardware-efficient, needing 560.3 GB at its Q4_K_M build vs 1669.3 GB for Qwen3.8 2.4T-A95B'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 Qwen3.8 2.4T-A95B?
- At 8k context, Muse Spark Open 816B-A42B needs approximately 560.3 GB of VRAM at its Q4_K_M build, while Qwen3.8 2.4T-A95B needs 1669.3 GB at its Q4_K_M build. At the largest build each ships, Muse Spark Open 816B-A42B requires 1837.8 GB (FP16) vs 5480.3 GB (FP16) for Qwen3.8 2.4T-A95B.
- Can you run Muse Spark Open 816B-A42B on the same GPUs as Qwen3.8 2.4T-A95B?
- 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 Qwen3.8 2.4T-A95B?
- Muse Spark Open 816B-A42B has 820B parameters (42B active, MoE) with a 1024k context window. Qwen3.8 2.4T-A95B has 2446.18B parameters (95B active, MoE) with a 256k context window. Licensing differs: Muse Spark Open 816B-A42B is Muse Model License while Qwen3.8 2.4T-A95B is Qwen3.8-Max.
- Which model fits in 24 GB of VRAM, Muse Spark Open 816B-A42B or Qwen3.8 2.4T-A95B?
- Neither fits in 24 GB: Muse Spark Open 816B-A42B needs 560.3 GB at Q4_K_M and Qwen3.8 2.4T-A95B needs 1669.3 GB at Q4_K_M. Both require a multi-GPU server with 1670 GB+ of combined VRAM.
- Which handles long context better, Muse Spark Open 816B-A42B or Qwen3.8 2.4T-A95B?
- At 262,144 tokens Spark Open's KV cache is 17.6 GB and Qwen3.8's is 24.7 GB, about 40% larger even though it caches a smaller share of its layers, because each cached layer is wide (4 KV heads of 256 dimensions). Either way the cache is small beside the weights: the totals at Q4_K_M are 579.0 GB for Spark Open and 1,696.2 GB for Qwen3.8, so the weights, not the context, decide which hardware you need.