LLM Comparison

Kimi K2 0905 vs Nemotron 3.5 Lightning 30B A3B

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

Price vs Intelligence

584531173$0.37$0.67$0.96$1.26$1.56Avg Price per M tokensIntelligenceKimi K2 090548.3 / $1.20Nemotron 3.5 Lightning 30B A3B13.4 / $0.53

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2/6 models
Same tier:
 Moonshot AIKimi K2 0905NVIDIANemotron 3.5 Lightning 30B A3B
OrganizationMoonshot AIMoonshot AINVIDIANVIDIA
OpenTools Score
48
40.3
13
25.5
FamilyKimiNemotron
StatusCurrentCurrent
Release DateSep 2025Aug 2026
Context Window262K tokens1.0M tokens
Input Price$0.40/M tokens$0.10/M tokens
Output Price$2.00/M tokens$0.95/M tokens
Pricing Notes—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 Output262K tokens33K tokens
API Identifiermoonshotai/kimi-k2-0905nvidia/nemotron-3.5-lightning-30b-a3b
Benchmarks
GPQA Diamond
76.6667artificial-analysis
74.3434artificial-analysis
AIME 2025
57.3333artificial-analysis
—
Artificial Analysis Intelligence Index v4.1.1—
24artificial-analysis
Artificial Analysis Intelligence Index v4.3—
14https://artificialanalysis.ai/models/nemotron-3-5-lightning
SciCode—
32.0602artificial-analysis
Terminal-Bench 2.1—
24.3446artificial-analysis
 View Kimi K2 0905View 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—
Kimi K2 0905$0.40$1.00$1.40+143%

Moonshot AI

Kimi K2 0905

Kimi K2 0905 is a large language model from Moonshot AI. Supports up to 262,144 token context window. 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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