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Phi 4 vs Nemotron 3.5 Lightning

Nemotron 3.5 Lightning leads Phi 4 on the Artificial Analysis Intelligence Index, 12.9 to 5.9, at about the same output price ($0.147 vs $0.14 per million). Updated October 21, 2026; 4 benchmarks have a result for both.

Phi 4Microsoft
Coding
No data
Overall
5.9
Output $/M
$0.14
0
Nemotron 3.5 LightningNVIDIA
Coding
22.6
Overall
12.9
Output $/M
$0.147
4
vs boards won of 4 shared

Phi 4 or Nemotron 3.5 Lightning: which to pick

Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; Phi 4 has none, so the two cannot be ranked on coding.

Pick Phi 4 for output tokens 1.0x cheaper ($0.14 vs $0.147 per million).

Shared results
4
Phi 4 only
41
Nemotron 3.5 Lightning only
16
Like-for-like categories
3 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
Phi 4 not benchmarked, Nemotron 3.5 Lightning 22.6 from 2 benchmarks.
Widest category
General, 60.3 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): Phi 4 0.0% against Nemotron 3.5 Lightning 60.3%, 60.3 points apart on a 0 to 100 scale.
Output price
Phi 4 $0.14 against Nemotron 3.5 Lightning $0.147 per million output tokens, 1.0x apart.
Context window
Phi 4 takes 16K tokens, Nemotron 3.5 Lightning 262K.

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
CategoryPhi 4BarsNemotron 3.5 LightningTies
Intelligence01
Reasoning02
General01
All shared boards04
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
Coding benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
AA Coding IndexNot measured26.8Only Nemotron 3.5 Lightning measured
SWE-bench Verified (Vals AI)Not measured52.8%Only Nemotron 3.5 Lightning measured
Vibe Code Bench (Vals AI)Not measured0.0%Only Nemotron 3.5 Lightning measured
Code migration (Vals AI)Not measured3.0%Only Nemotron 3.5 Lightning measured
ProgramBench (Vals AI)Not measured0.0%Only Nemotron 3.5 Lightning measured
Terminal-Bench 2.1 (Vals AI)Not measured10.9%Only Nemotron 3.5 Lightning measured
CyberBench (Vals AI)Not measured29.5%Only Nemotron 3.5 Lightning measured
LiveCodeBench (Artificial Analysis run)23.1%Not measuredOnly Phi 4 measured
SciCodeNot measured32.1%Only Nemotron 3.5 Lightning measured
Terminal-Bench Hard (Artificial Analysis run)3.8%Not measuredOnly Phi 4 measured
ALE-BenchNot measured769.2Only Nemotron 3.5 Lightning measured
LMArena Text: coding1231Not measuredOnly Phi 4 measured
LMArena Text: software and IT1228Not measuredOnly Phi 4 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
AA Intelligence Index5.912.9Nemotron 3.5 Lightning
Epoch Capabilities Index130.5Not measuredOnly Phi 4 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
AA Agentic IndexNot measured3.5Only Nemotron 3.5 Lightning measured
Finance Agent (Vals AI)Not measured18.5%Only Nemotron 3.5 Lightning measured
Tax Agent Bench (Vals AI)Not measured2.6%Only Nemotron 3.5 Lightning measured
Legal Research Bench (Vals AI)Not measured2.9%Only Nemotron 3.5 Lightning measured
Excel Modeling Benchmark (Vals AI)Not measured17.0%Only Nemotron 3.5 Lightning measured
τ²-Bench Telecom0.0%Not measuredOnly Phi 4 measured
BALROG11.6%Not measuredOnly Phi 4 measured
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
GPQA Diamond (Artificial Analysis run)57.5%74.3%Nemotron 3.5 Lightning
Humanity's Last Exam (Artificial Analysis run)3.8%10.6%Nemotron 3.5 Lightning
GPQA Diamond (Epoch AI run)56.1%Not measuredOnly Phi 4 measured
LMArena Text: hard prompts1220Not measuredOnly Phi 4 measured
LMArena Text: expert1203Not measuredOnly Phi 4 measured
LMArena Text: hard prompts in English1230Not measuredOnly Phi 4 measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
AA Math Index18.0Not measuredOnly Phi 4 measured
AIME 2025 (Artificial Analysis run)18.0%Not measuredOnly Phi 4 measured
OTIS Mock AIME13.8%Not measuredOnly Phi 4 measured
MATH level 564.9%Not measuredOnly Phi 4 measured
LMArena Text: math1246Not measuredOnly Phi 4 measured
LMArena Text: mathematical work1254Not measuredOnly Phi 4 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Knowledge benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
CorpFin (Vals AI)Not measured54.2%Only Nemotron 3.5 Lightning measured
MMLU Pro (Artificial Analysis run)71.4%Not measuredOnly Phi 4 measured
MMLU84.8%Not measuredOnly Phi 4 measured
LMArena Text: science1206Not measuredOnly Phi 4 measured
LMArena Text: medicine and healthcare1202Not measuredOnly Phi 4 measured
LMArena Text: legal and government1241Not measuredOnly Phi 4 measured
LMArena Text: business and finance1199Not measuredOnly Phi 4 measured
Neither model has a result on the other 10 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
General benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
MedScribe (Vals AI)Not measured4.3%Only Nemotron 3.5 Lightning measured
IFBench23.5%Not measuredOnly Phi 4 measured
Long context reasoning (AA-LCR)0.0%60.3%Nemotron 3.5 Lightning
Creative writing (Lech Mazur)62.6%Not measuredOnly Phi 4 measured
LMArena Text1217Not measuredOnly Phi 4 measured
LMArena Text: instruction following1201Not measuredOnly Phi 4 measured
LMArena Text: creative writing1181Not measuredOnly Phi 4 measured
LMArena Text: English1231Not measuredOnly Phi 4 measured
LMArena Text: non-English1197Not measuredOnly Phi 4 measured
LMArena Text: Chinese1212Not measuredOnly Phi 4 measured
LMArena Text: German1222Not measuredOnly Phi 4 measured
LMArena Text: Japanese1158Not measuredOnly Phi 4 measured
LMArena Text: Korean1151Not measuredOnly Phi 4 measured
LMArena Text: French1224Not measuredOnly Phi 4 measured
LMArena Text: Spanish1234Not measuredOnly Phi 4 measured
LMArena Text: Russian1209Not measuredOnly Phi 4 measured
LMArena Text: multi-turn1206Not measuredOnly Phi 4 measured
LMArena Text: longer queries1217Not measuredOnly Phi 4 measured
LMArena Text: writing and language1198Not measuredOnly Phi 4 measured
LMArena Text: entertainment and media1162Not measuredOnly Phi 4 measured
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Phi 4 and Nemotron 3.5 Lightning
BenchmarkPhi 4Nemotron 3.5 LightningLeader
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

Phi 4$0.0001Fits in one request
Nemotron 3.5 Lightning$0.0001Fits in one request

Nemotron 3.5 Lightning costs less

Repository review

50K input + 3K output tokens

Phi 4$0.0039Exceeds the context window
Nemotron 3.5 Lightning$0.0031Fits in one request

Nemotron 3.5 Lightning costs less

Agent loop

200K input + 10K output tokens

Phi 4$0.015Exceeds the context window
Nemotron 3.5 Lightning$0.012Fits in one request

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 Phi 4 and Nemotron 3.5 Lightning
SpecificationMicrosoft Phi 4NVIDIA Nemotron 3.5 Lightning
MakerMicrosoftNVIDIA
WeightsOpen weightsOpen weights
ReleasedJanuary 10, 2025August 11, 2026
Context window16K262K
Input $/Mtok$0.07$0.0534
Output $/Mtok$0.14$0.147
Coding indexNot benchmarked22.6
Independent measurements behind it02
Cost per solved taskNot computable$0.06
SourceOpenRouter listingOpenRouter listing

Questions

Is Phi 4 better than Nemotron 3.5 Lightning for coding?

Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; Phi 4 has none, so the two cannot be ranked on coding. Pick Phi 4 for output tokens 1.0x cheaper ($0.14 vs $0.147 per million).

Which is cheaper, Phi 4 or Nemotron 3.5 Lightning?

Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens; Nemotron 3.5 Lightning costs $0.0534 and $0.147. Cost per solved task is known for Nemotron 3.5 Lightning only.

Which has the bigger context window, Phi 4 or Nemotron 3.5 Lightning?

Nemotron 3.5 Lightning, with 262K tokens against 16K.

How many benchmarks is the Phi 4 vs Nemotron 3.5 Lightning comparison based on?

4 benchmarks have a result for both models as of October 21, 2026. Phi 4's coding index rests on 0 independent benchmarks and Nemotron 3.5 Lightning's on 2. Where only one model has a result, the page says so instead of naming a winner.

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