LLM Comparison
DeepSeek V3.2 Speciale vs Nemotron 3.5 Lightning 30B A3B
Side-by-side specs, pricing & capabilities · Updated August 2026
Price vs Intelligence
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 63 78.4 | 6 12.0 |
| Family | DeepSeek | Nemotron |
| Status | Current | Current |
| Release Date | Dec 2025 | Aug 2026 |
| Context Window | 164K tokens | 1.0M tokens |
| Input Price | $0.40/M tokens | $0.10/M tokens |
| Output Price | $1.20/M tokens | $0.95/M tokens |
| Pricing Notes | Cache read: $0.2000/M tokens | 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 | textcode | textreasoningcodingtool-usefunction-callinglong-contextagenticlocal-deploymentopen-weights |
| Training Cutoff | — | May 2026 post-training; September 2025 pre-training |
| Max Output | 164K tokens | 33K tokens |
| API Identifier | deepseek/deepseek-v3.2-speciale | nvidia/nemotron-3.5-lightning-30b-a3b |
| Benchmarks | ||
| MMLU-Pro | 85deepseek | — |
| AIME 2025 | 96deepseek | — |
| LiveCodeBench | 88.7deepseek | — |
| HLE | 30.6deepseek | — |
| HMMT 2025 | 99deepseek | — |
| SWE-bench Verified | 73deepseek | — |
| Terminal-Bench 2.0 | 46deepseek | — |
| Artificial Analysis Intelligence Index v4.1.1 | — | 24artificial-analysis |
| View DeepSeek V3.2 Speciale | View Nemotron 3.5 Lightning 30B A3B | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Nemotron 3.5 Lightning 30B A3BCheapest | $0.10 | $0.48 | $0.58 | — |
| DeepSeek V3.2 Speciale | $0.40 | $0.60 | $1.00 | +74% |
DeepSeek
DeepSeek V3.2 Speciale
DeepSeek V3.2 Speciale is a large language model from DeepSeek. Supports up to 163,840 token context window. Achieves 87.1% on MMLU. Available from $0.40/M input tokens.
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.
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