LLM Comparison
MiniMax M2-her vs Nemotron 3.5 Lightning 30B A3B
Side-by-side specs, pricing & capabilities · Updated September 2026
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2/6 models| Organization | ||
| OpenTools Score | 13 25.5 | |
| Family | MiniMax | Nemotron |
| Status | Current | Current |
| Release Date | Jan 2026 | Aug 2026 |
| Context Window | 66K tokens | 1.0M tokens |
| Input Price | $0.30/M tokens | $0.10/M tokens |
| Output Price | $1.20/M tokens | $0.95/M tokens |
| Pricing Notes | Cache read: $0.0300/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 | 2K tokens | 33K tokens |
| API Identifier | minimax/minimax-m2-her | nvidia/nemotron-3.5-lightning-30b-a3b |
| Benchmarks | ||
| Artificial Analysis Intelligence Index v4.1.1 | — | 24artificial-analysis |
| Artificial Analysis Intelligence Index v4.3 | — | 14https://artificialanalysis.ai/models/nemotron-3-5-lightning |
| GPQA Diamond | — | 74.3434artificial-analysis |
| SciCode | — | 32.0602artificial-analysis |
| Terminal-Bench 2.1 | — | 24.3446artificial-analysis |
| View MiniMax M2-her | 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 | — |
| MiniMax M2-her | $0.30 | $0.60 | $0.90 | +57% |
MiniMax
MiniMax M2-her
MiniMax M2-her is a language-model variant designed for character role-play and interactive storytelling. MiniMax describes work on world consistency, story progression and adaptation to user preferences. It is distinct from the general-purpose MiniMax M2 family.
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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