WritingmateWritingmate
Model Comparison

Nemotron 3.5 Lightning vs Solar Pro 4Which Is Better in 2026?

NVIDIA: Nemotron 3.5 Lightning logoNVIDIA•
Upstage

Nemotron 3.5 Lightning vs Solar Pro 4: which should you choose in 2026?

Nemotron 3.5 Lightning (by NVIDIA) and Solar Pro 4 (by Upstage) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Solar Pro 4 is about 2.6× cheaper on a blended 3:1 input/output basis ($0.0525 vs $0.1375 per 1M tokens).

Solar Pro 4 has the larger context window (524,288 tokens vs 262,144 tokens).

Choose Nemotron 3.5 Lightning if…

  • • your own prompt tests favor its output; shared comparable evidence does not identify a unique advantage

Choose Solar Pro 4 if…

  • • lower blended API cost matters for your workload
  • • you work with longer documents, transcripts, or codebases

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

Chat with both models
vs

Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Nemotron 3.5 Lightning and Solar Pro 4 benchmark results
BenchmarkNemotron 3.5 LightningSolar Pro 4
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

——
BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

——
SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

——
Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

——
AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

——
GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

——
Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

——
Arena preference scores
Arena (Text)

Human preference score

—
1378 ±12
Arena (Code/WebDev)

Human preference score for code and web development

—
1373 ±17

Benchmark sources

Reviewed evidence last updated 2026-08-09. Arena data last refreshed Aug 12, 2026.

Pricing, capabilities, and model facts

Nemotron 3.5 Lightning and Solar Pro 4 model facts
FeatureNemotron 3.5 LightningSolar Pro 4
Context & model facts
DeveloperNVIDIAUpstage
API providerNVIDIAUpstage
Input context262,144 tokens524,288 tokens
Maximum output262,144 tokens131,072 tokens
Released——
Added to WritingmateAug 11, 2026Aug 10, 2026
LicenseNot availableNot available
Knowledge cutoff——
Capabilities
InputsTextText
OutputsTextText
Provider endpoint accepts tool parametersNoYes
ReasoningYesYes
VisionNoNo
Image GenerationNoNo
Video GenerationNoNo
API pricing
Input (per 1M tokens)$0.10$0.03
Output (per 1M tokens)$0.25$0.12
Blended 3:1 input/output$0.1375$0.0525
API performance
p95 latencyNot measuredNot measured
Output throughputNot measuredNot measured
Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

Data sources

  • Writingmate model catalog (pricing, limits, and availability)

Catalog data last updated Aug 11, 2026.

Why Pay for Multiple Subscriptions?

Comparing Nemotron 3.5 Lightning from NVIDIA with Solar Pro 4 from Upstage? Instead of managing separate API keys and subscriptions, get both with Writingmate.

Subscription-plan access for Nemotron 3.5 Lightning and Solar Pro 4
PlanPriceNemotron 3.5 LightningSolar Pro 4AI ImagesAI Video
Writingmate Pro
Most popular
$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
Writingmate Ultimate
Power users
$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreSora 2, VEO 3.1
View Plans & Pricing

Nemotron 3.5 Lightning vs Solar Pro 4 FAQ

Which is better, Nemotron 3.5 Lightning or Solar Pro 4?

There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Solar Pro 4 is about 2.6× cheaper on a blended 3:1 input/output basis ($0.0525 vs $0.1375 per 1M tokens). Solar Pro 4 has the larger context window (524,288 tokens vs 262,144 tokens).

Which model is cheaper to use through an API?

Solar Pro 4 is about 2.6× cheaper on a blended 3:1 input/output basis ($0.0525 vs $0.1375 per 1M tokens).

Which model supports more context?

Solar Pro 4 has the larger context window (524,288 tokens vs 262,144 tokens).

Can I switch between Nemotron 3.5 Lightning and Solar Pro 4?

Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.