LLM Comparison

GLM 5 vs Nemotron 3.5 Lightning 30B A3B

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

Price vs Intelligence

745639213$0.37$0.77$1.17$1.56$1.96Avg Price per M tokensIntelligenceGLM 564 / $1.51Nemotron 3.5 Lightning 30B A3B13.4 / $0.53

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2/6 models
Same tier:
 Z.aiGLM 5NVIDIANemotron 3.5 Lightning 30B A3B
OrganizationZ.aiZ.aiNVIDIANVIDIA
OpenTools Score
64
42.4
13
25.5
FamilyGLMNemotron
StatusCurrentCurrent
Release DateFeb 2026Aug 2026
Context Window203K tokens1.0M tokens
Input Price$0.72/M tokens$0.10/M tokens
Output Price$2.30/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 Output131K tokens33K tokens
API Identifierz-ai/glm-5nvidia/nemotron-3.5-lightning-30b-a3b
Benchmarks
GPQA Diamond
86https://huggingface.co/zai-org/GLM-5
74.3434artificial-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 GLM 5View 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—
GLM 5$0.72$1.15$1.87+225%

Z.ai

GLM 5

GLM-5 is Z.ai's open-weight language model for agentic engineering, with 744B total and 40B active parameters. Its released checkpoint configuration supports 202,752 token positions; hosted service limits and prices depend on the provider.

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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