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Nemotron 3.5 Lightning vs Sonar Pro

Nemotron 3.5 Lightning leads Sonar Pro on the Artificial Analysis Intelligence Index, 12.9 to 7.6, and Nemotron 3.5 Lightning costs 102.0x less per output token ($0.147 vs $15.00 per million). Updated October 21, 2026; 3 benchmarks have a result for both.

Nemotron 3.5 LightningNVIDIA
Coding
22.6
Overall
12.9
Output $/M
$0.147
3
Sonar ProPerplexity
Coding
No data
Overall
7.6
Output $/M
$15.00
0
vs boards won of 3 shared

Nemotron 3.5 Lightning or Sonar Pro: which to pick

Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; Sonar Pro has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Shared results
3
Nemotron 3.5 Lightning only
17
Sonar Pro only
2
Like-for-like categories
2 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Nemotron 3.5 Lightning 22.6 from 2 benchmarks, Sonar Pro not benchmarked.
Widest category
Reasoning, 10.3 points apart on average over 2 shared benchmarks.
Widest single gap
GPQA Diamond (Artificial Analysis run): Nemotron 3.5 Lightning 74.3% against Sonar Pro 57.8%, 16.5 points apart on a 0 to 100 scale.
Output price
Nemotron 3.5 Lightning $0.147 against Sonar Pro $15.00 per million output tokens, 102.0x apart.
Context window
Nemotron 3.5 Lightning takes 262K tokens, Sonar Pro 200K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryNemotron 3.5 LightningBarsSonar ProTies
Intelligence10
Reasoning20
All shared boards30
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
Coding benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
AA Coding Index26.8Not measuredOnly Nemotron 3.5 Lightning measured
SWE-bench Verified (Vals AI)52.8%Not measuredOnly Nemotron 3.5 Lightning measured
Vibe Code Bench (Vals AI)0.0%Not measuredOnly Nemotron 3.5 Lightning measured
Code migration (Vals AI)3.0%Not measuredOnly Nemotron 3.5 Lightning measured
ProgramBench (Vals AI)0.0%Not measuredOnly Nemotron 3.5 Lightning measured
Terminal-Bench 2.1 (Vals AI)10.9%Not measuredOnly Nemotron 3.5 Lightning measured
CyberBench (Vals AI)29.5%Not measuredOnly Nemotron 3.5 Lightning measured
LiveCodeBench (Artificial Analysis run)Not measured27.5%Only Sonar Pro measured
SciCode32.1%Not measuredOnly Nemotron 3.5 Lightning measured
ALE-Bench769.2Not measuredOnly Nemotron 3.5 Lightning measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
AA Intelligence Index12.97.6Nemotron 3.5 Lightning
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
AA Agentic Index3.5Not measuredOnly Nemotron 3.5 Lightning measured
Finance Agent (Vals AI)18.5%Not measuredOnly Nemotron 3.5 Lightning measured
Tax Agent Bench (Vals AI)2.6%Not measuredOnly Nemotron 3.5 Lightning measured
Legal Research Bench (Vals AI)2.9%Not measuredOnly Nemotron 3.5 Lightning measured
Excel Modeling Benchmark (Vals AI)17.0%Not measuredOnly Nemotron 3.5 Lightning measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
GPQA Diamond (Artificial Analysis run)74.3%57.8%Nemotron 3.5 Lightning
Humanity's Last Exam (Artificial Analysis run)10.6%6.6%Nemotron 3.5 Lightning
Neither model has a result on the other 19 boards here
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Math benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
Neither model has a result on 13 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Knowledge benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
CorpFin (Vals AI)54.2%Not measuredOnly Nemotron 3.5 Lightning measured
MMLU Pro (Artificial Analysis run)Not measured75.5%Only Sonar Pro measured
Neither model has a result on the other 15 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
Neither model has a result on 9 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
General benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
MedScribe (Vals AI)4.3%Not measuredOnly Nemotron 3.5 Lightning measured
Long context reasoning (AA-LCR)60.3%Not measuredOnly Nemotron 3.5 Lightning measured
Neither model has a result on the other 44 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Nemotron 3.5 Lightning and Sonar Pro
BenchmarkNemotron 3.5 LightningSonar ProLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Nemotron 3.5 Lightning$0.0001Fits in one request
Sonar Pro$0.011Fits in one request

Nemotron 3.5 Lightning costs less

Repository review

50K input + 3K output tokens

Nemotron 3.5 Lightning$0.0031Fits in one request
Sonar Pro$0.195Fits in one request

Nemotron 3.5 Lightning costs less

Agent loop

200K input + 10K output tokens

Nemotron 3.5 Lightning$0.012Fits in one request
Sonar Pro$0.750Exceeds the context window

Nemotron 3.5 Lightning costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Nemotron 3.5 Lightning and Sonar Pro
SpecificationNVIDIA Nemotron 3.5 LightningPerplexity Sonar Pro
MakerNVIDIAPerplexity
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2026March 7, 2025
Context window262K200K
Input $/Mtok$0.0534$3.00
Output $/Mtok$0.147$15.00
Coding index22.6Not benchmarked
Independent measurements behind it20
Cost per solved task$0.06Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Nemotron 3.5 Lightning better than Sonar Pro for coding?

Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; Sonar Pro has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Nemotron 3.5 Lightning or Sonar Pro?

Nemotron 3.5 Lightning costs $0.0534 per million input tokens and $0.147 per million output tokens; Sonar Pro costs $3.00 and $15.00. Cost per solved task is known for Nemotron 3.5 Lightning only.

Which has the bigger context window, Nemotron 3.5 Lightning or Sonar Pro?

Nemotron 3.5 Lightning, with 262K tokens against 200K.

How many benchmarks is the Nemotron 3.5 Lightning vs Sonar Pro comparison based on?

3 benchmarks have a result for both models as of October 21, 2026. Nemotron 3.5 Lightning's coding index rests on 2 independent benchmarks and Sonar Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.

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