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Nvidia Nemotron 3.5 Lightning

Added Aug 12, 2026Open weights

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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. Thinking is disabled on this variant.

  • 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)
228 tokens
Longer than 24% of 468 models

Side by side

Nvidia Nemotron 3.5 Lightning compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Nvidia Nemotron 3.5 LightningnvidiaThis modelGLM 4.5Z.aiRemove
ProvidernvidiaZ.ai
Discovery tierOpen weightsOpen weights
AddedAug 12, 2026Apr 15, 2025
Limits
Context window1MBest128K
Max output65.54K65.54K
Average output228 tokens696 tokens
Pricing, USD per 1M tokens
Input$0.05Best$0.30
Output$0.20Best$1.30
Cache read$0.01—
Performance
Throughput536.8 tok/sBest35.7 tok/s
Time to first token0.9 sBest9.6 s
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index12.9Best12.8
Coding Index26.8—
Math Index—73.7
Capabilities
ReasoningYesNo
ToolsYesNo
Parallel toolsYesNo
Reasoning effortnone, high—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Nvidia Nemotron 3.5 Lightning
  • GLM 4.5

Intelligence Index

  • Nvidia Nemotron 3.5 Lightning12.9
  • GLM 4.512.8

Nvidia Nemotron 3.5 Lightning: Better than 30% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Nvidia Nemotron 3.5 Lightning: 26.8
    • GLM 4.5: —

  • Math Index

    Composite of the math evaluations

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 73.7

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Nvidia Nemotron 3.5 Lightning: 0.8%
    • GLM 4.5: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Nvidia Nemotron 3.5 Lightning: 0.0%
    • GLM 4.5: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Nvidia Nemotron 3.5 Lightning: 506 Elo
    • GLM 4.5: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Nvidia Nemotron 3.5 Lightning: 638 Elo
    • GLM 4.5: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Nvidia Nemotron 3.5 Lightning: 3.0%
    • GLM 4.5: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron 3.5 Lightning: 60.3%Top
    • GLM 4.5: 52.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Nvidia Nemotron 3.5 Lightning: 2.2%
    • GLM 4.5: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron 3.5 Lightning: 10.6%
    • GLM 4.5: 13.0%Top

  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron 3.5 Lightning: 74.3%
    • GLM 4.5: 78.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 83.5%

  • IFBench

    Following precise output instructions

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 44.1%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Nvidia Nemotron 3.5 Lightning: 0.5%
    • GLM 4.5: —

  • SciCode

    Scientific computing in Python

    • Nvidia Nemotron 3.5 Lightning: 32.1%
    • GLM 4.5: —

  • LiveCodeBench

    Recent competitive programming problems

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 73.8%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 73.7%

  • AIME

    Competition mathematics

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 87.3%

  • MATH-500

    Competition math problems

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 97.9%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 22.0%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Nvidia Nemotron 3.5 Lightning: —
    • GLM 4.5: 43.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron 3.5 Lightning: 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.

Throughput
536.8 tok/s
Faster than 98% of 253 models
Time to first token
0.9 s
Quicker than 96% of 253 models
Prompt cache hits
84.6%
About $0.0162 per 1M input tokens with caching
Request success (24h)
100.0%
Reliable

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.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

nvidia

Other nvidia models, starting with the same family.