LLM Comparison

DeepSeek V3.1 Terminus vs Nemotron 3.5 Lightning 30B A3B

Side-by-side specs, pricing & capabilities · Updated August 2026

Price vs Intelligence

433222110$0.35$0.43$0.52$0.60$0.68Avg Price per M tokensIntelligenceDeepSeek V3.1 Terminus33.2 / $0.50Nemotron 3.5 Lightning 30B A3B6.3 / $0.53

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2/6 models
Same tier:
 DeepSeekDeepSeek V3.1 TerminusNVIDIANemotron 3.5 Lightning 30B A3B
OrganizationDeepSeekDeepSeekNVIDIANVIDIA
OpenTools Score
33
66.4
6
12.0
FamilyDeepSeekNemotron
StatusCurrentCurrent
Release DateSep 2025Aug 2026
Context Window164K tokens1.0M tokens
Input Price$0.21/M tokens$0.10/M tokens
Output Price$0.79/M tokens$0.95/M tokens
Pricing NotesCache read: $0.1300/M tokensNVIDIA 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—33K tokens
API Identifierdeepseek/deepseek-v3.1-terminusnvidia/nemotron-3.5-lightning-30b-a3b
Benchmarks
MMLU-Pro
83.6deepseek
—
GPQA Diamond
75.1deepseek
—
AIME 2025
53.7deepseek
—
LiveCodeBench
52.9deepseek
—
SWE-bench Verified
66deepseek
—
Terminal-Bench Hard
31.8deepseek
—
HLE
8.4deepseek
—
Artificial Analysis Intelligence Index v4.1.1—
24artificial-analysis
 View DeepSeek V3.1 TerminusView Nemotron 3.5 Lightning 30B A3B

Cost Calculator

Enter your expected monthly token usage to compare costs.

ModelInputOutputTotal / movs Best
Nemotron 3.5 Lightning 30B A3BCheapest$0.10$0.48$0.58—
DeepSeek V3.1 Terminus$0.21$0.40$0.61+5%

DeepSeek

DeepSeek V3.1 Terminus

DeepSeek V3.1 Terminus is a large language model from DeepSeek. Supports up to 163,840 token context window. Achieves 87.1% on MMLU. Available from $0.21/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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