LLM Comparison
Muse Spark 1.1 vs Nemotron 3.5 Lightning 30B A3B
Side-by-side specs, pricing & capabilities · Updated August 2026
Price vs Intelligence
Add to comparison
2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 63 22.7 | 6 12.0 |
| Family | Muse Spark | Nemotron |
| Status | Current | Current |
| Release Date | Jul 2026 | Aug 2026 |
| Context Window | 1.0M tokens | 1.0M tokens |
| Input Price | $1.25/M tokens | $0.10/M tokens |
| Output Price | $4.25/M tokens | $0.95/M tokens |
| Pricing Notes | Artificial Analysis reports Meta API pricing at $1.25 input, $4.25 output, and $0.15 cached input per 1M tokens for the xhigh variant. | NVIDIA build lists serverless NIM pricing at $0.10 input and $0.95 output per million tokens. Self-hosted weights are also available; infrastructure costs vary. |
| Capabilities | reasoningtool-usecomputer-usecodingmultimodallong-contextstructured-output | textreasoningcodingtool-usefunction-callinglong-contextagenticlocal-deploymentopen-weights |
| Training Cutoff | — | May 2026 post-training; September 2025 pre-training |
| Max Output | 131K tokens | 33K tokens |
| API Identifier | muse-spark-1.1 | nvidia/nemotron-3.5-lightning-30b-a3b |
| Benchmarks | ||
| Artificial Analysis Intelligence Index v4.1 | 51artificial-analysis | — |
| Artificial Analysis Intelligence Index v4.1.1 | — | 24artificial-analysis |
| View Muse Spark 1.1 | View Nemotron 3.5 Lightning 30B A3B | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Nemotron 3.5 Lightning 30B A3BCheapest | $0.10 | $0.48 | $0.58 | — |
| Muse Spark 1.1 | $1.25 | $2.13 | $3.38 | +487% |
Meta
Muse Spark 1.1
Muse Spark 1.1 is Meta's multimodal reasoning model for agentic coding, computer-use workflows, tool calling, and long-context tasks through the Meta Model API.
NVIDIA
Nemotron 3.5 Lightning 30B A3B
Nemotron 3.5 Lightning is NVIDIA open-weight 30B-A3B reasoning model for fast, long-running agents. Its hybrid Mamba-2, mixture-of-experts, and attention architecture supports function calling, coding, tool use, long context, and efficient local or serverless deployment.
More Comparisons
Looking for more AI models?
Browse All LLMs