LLM Comparison

GLM 5.1 vs Nemotron 3.5 Lightning 30B A3B

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

Price vs Intelligence

655034193$0.37$0.94$1.52$2.09$2.67Avg Price per M tokensIntelligenceGLM 5.155.4 / $2.05Nemotron 3.5 Lightning 30B A3B13.4 / $0.53

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2/6 models
Same tier:
 Z.aiGLM 5.1NVIDIANemotron 3.5 Lightning 30B A3B
OrganizationZ.aiZ.aiNVIDIANVIDIA
OpenTools Score
55
27.0
13
25.5
FamilyGLMNemotron
StatusCurrentCurrent
Release DateApr 2026Aug 2026
Context Window203K tokens1.0M tokens
Input Price$0.95/M tokens$0.10/M tokens
Output Price$3.15/M tokens$0.95/M tokens
Pricing NotesCache read: $0.4750/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 Output66K tokens33K tokens
API Identifierz-ai/glm-5.1nvidia/nemotron-3.5-lightning-30b-a3b
Benchmarks
GPQA Diamond
86.2https://huggingface.co/zai-org/GLM-5.1
74.3434artificial-analysis
Artificial Analysis Intelligence Index v4.3
26.4455artificial-analysis
14https://artificialanalysis.ai/models/nemotron-3-5-lightning
SciCode
44.7917artificial-analysis
32.0602artificial-analysis
Terminal-Bench 2.1
61.7978artificial-analysis
24.3446artificial-analysis
Artificial Analysis Intelligence Index v4.1.1—
24artificial-analysis
 View GLM 5.1View 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 5.1$0.95$1.58$2.53+339%

Z.ai

GLM 5.1

GLM 5.1 is a large language model from Z.ai. Supports up to 202,752 token context window. Available from $0.95/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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