nvidia · Open weights
Nvidia Nemotron 3.5 Lightning Thinking
Nvidia Nemotron 3.5 Lightning is an open-weight 30B-A3B hybrid Mamba-Transformer mixture-of-experts model for high-throughput agentic, coding, tool-use, instruction-following, and long-context workloads. This variant enables its reasoning trace.
- Reasoning
- Tools
- Parallel tools
- Input
- Text
- Output
- Text
- Reasoning effort
- none · high
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.05
- Output
- $0.20
- Cache read
- $0.01
Reference prices as published by the model provider. API example
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Average output (7 days)
- 6.1K tokens
- Longer than 99% of 468 models
Side by side
Nvidia Nemotron 3.5 Lightning Thinking compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Nvidia Nemotron 3.5 Lightning ThinkingnvidiaThis model | GLM 4.5Z.aiRemove | |
|---|---|---|
| Provider | nvidia | Z.ai |
| Discovery tier | Open weights | Open weights |
| Added | Aug 12, 2026 | Apr 15, 2025 |
| Limits | ||
| Context window | 1MBest | 128K |
| Max output | 65.54K | 65.54K |
| Average output | 6.1K tokens | 696 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.05Best | $0.30 |
| Output | $0.20Best | $1.30 |
| Cache read | $0.01 | — |
| Performance | ||
| Throughput | — | 35.7 tok/s |
| Time to first token | — | 9.6 s |
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 12.9Best | 12.8 |
| Coding Index | 26.8 | — |
| Math Index | — | 73.7 |
| Capabilities | ||
| Reasoning | Yes | No |
| Tools | Yes | No |
| Parallel tools | Yes | No |
| Reasoning effort | none, high | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Nvidia Nemotron 3.5 Lightning Thinking
- GLM 4.5
Intelligence Index
- Nvidia Nemotron 3.5 Lightning Thinking12.9
- GLM 4.512.8
Nvidia Nemotron 3.5 Lightning Thinking: Better than 30% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Nvidia Nemotron 3.5 Lightning Thinking: 26.8
- GLM 4.5: —
Math Index
Composite of the math evaluations
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 73.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Nvidia Nemotron 3.5 Lightning Thinking: 0.8%
- GLM 4.5: —
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Nvidia Nemotron 3.5 Lightning Thinking: 0.0%
- GLM 4.5: —
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Nvidia Nemotron 3.5 Lightning Thinking: 506 Elo
- GLM 4.5: —
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Nvidia Nemotron 3.5 Lightning Thinking: 638 Elo
- GLM 4.5: —
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Nvidia Nemotron 3.5 Lightning Thinking: 3.0%
- GLM 4.5: —
AA-LCRv1.1
Reasoning across long documents
- Nvidia Nemotron 3.5 Lightning Thinking: 60.3%Top
- GLM 4.5: 52.7%
MLCR-AA
Long-context reasoning over medical records
- Nvidia Nemotron 3.5 Lightning Thinking: 2.2%
- GLM 4.5: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Nvidia Nemotron 3.5 Lightning Thinking: 10.6%
- GLM 4.5: 13.0%Top
GPQA Diamond
Graduate-level science questions
- Nvidia Nemotron 3.5 Lightning Thinking: 74.3%
- GLM 4.5: 78.2%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 83.5%
IFBench
Following precise output instructions
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 44.1%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Nvidia Nemotron 3.5 Lightning Thinking: 0.5%
- GLM 4.5: —
SciCode
Scientific computing in Python
- Nvidia Nemotron 3.5 Lightning Thinking: 32.1%
- GLM 4.5: —
LiveCodeBench
Recent competitive programming problems
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 73.8%
Math
AIME 2025
Competition mathematics, 2025 problems
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 73.7%
AIME
Competition mathematics
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 87.3%
MATH-500
Competition math problems
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 97.9%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 22.0%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Nvidia Nemotron 3.5 Lightning Thinking: —
- GLM 4.5: 43.0%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Nvidia Nemotron 3.5 Lightning Thinking: 60.3%Top
- GLM 4.5: 52.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 0.0%
- About $0.05 per 1M input tokens with caching
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "nvidia/nemotron-3.5-lightning:thinking",
"reasoning_effort": "none",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="nvidia/nemotron-3.5-lightning:thinking",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)nvidia
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