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DeepSeek V4 Flash 0423 vs Nemotron 3 Super

DeepSeek V4 Flash 0423 leads Nemotron 3 Super on the Artificial Analysis Intelligence Index, 24.4 to 12.8, and DeepSeek V4 Flash 0423 costs 1.6x less per output token ($0.28 vs $0.45 per million). Updated October 22, 2026; 31 benchmarks have a result for both.

DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
31
Nemotron 3 SuperNVIDIA
Coding
No data
Overall
No data
Output $/M
$0.45
0
vs boards won of 31 shared

DeepSeek V4 Flash 0423 or Nemotron 3 Super: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V4 Flash 0423 for output tokens 1.6x cheaper ($0.28 vs $0.45 per million) and a larger context window (1.05M against 262K tokens).

Shared results
31
DeepSeek V4 Flash 0423 only
18
Nemotron 3 Super only
7
Like-for-like categories
7 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
DeepSeek V4 Flash 0423 not benchmarked, Nemotron 3 Super not benchmarked.
Widest category
Agentic work, 24.6 points apart on average over 1 shared benchmark.
Widest single gap
AA Agentic Index: DeepSeek V4 Flash 0423 26.3 against Nemotron 3 Super 1.7, 24.6 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Nemotron 3 Super $0.45 per million output tokens, 1.6x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Nemotron 3 Super 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
CategoryDeepSeek V4 Flash 0423BarsNemotron 3 SuperTies
Coding50
Intelligence10
Agentic work10
Reasoning30
Math20
Knowledge40
General150
All shared boards310
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.037.7DeepSeek V4 Flash 0423
SciCodeNot measured36.2%Only Nemotron 3 Super measured
Terminal-Bench Hard (Artificial Analysis run)Not measured28.8%Only Nemotron 3 Super measured
WeirdML45.6%38.0%DeepSeek V4 Flash 0423
ALE-Bench678.2213.9DeepSeek V4 Flash 0423
LMArena Text: coding14571403DeepSeek V4 Flash 0423
LMArena Text: software and IT14531409DeepSeek V4 Flash 0423
LMArena WebDev: React1585Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena WebDev: HTML1558Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
AA Intelligence Index24.412.8DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V4 Flash 0423 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
AA Agentic Index26.31.7DeepSeek V4 Flash 0423
τ²-Bench TelecomNot measured67.8%Only Nemotron 3 Super measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
GPQA Diamond (Artificial Analysis run)Not measured80.0%Only Nemotron 3 Super measured
Humanity's Last Exam (Artificial Analysis run)Not measured20.8%Only Nemotron 3 Super measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441386DeepSeek V4 Flash 0423
LMArena Text: expert14411398DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491397DeepSeek V4 Flash 0423
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
LMArena Text: math14261380DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321405DeepSeek V4 Flash 0423
Neither model has a result on the other 11 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
LMArena Text: science14461387DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481383DeepSeek V4 Flash 0423
LMArena Text: legal and government14391392DeepSeek V4 Flash 0423
LMArena Text: business and finance14251364DeepSeek V4 Flash 0423
Neither model has a result on the other 14 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
Design Arena: websites1207Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: UI components1162Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: game dev1196Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: data viz1127Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: 3D1188Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: all code categories1205Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: SVG1155Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: ASCII art1109Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
General benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
IFBenchNot measured71.5%Only Nemotron 3 Super measured
Long context reasoning (AA-LCR)Not measured65.7%Only Nemotron 3 Super measured
DTBench86.4%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMCA35.9%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text14321378DeepSeek V4 Flash 0423
LMArena Text: instruction following14211347DeepSeek V4 Flash 0423
LMArena Text: creative writing14031317DeepSeek V4 Flash 0423
LMArena Text: English14411400DeepSeek V4 Flash 0423
LMArena Text: non-English14191356DeepSeek V4 Flash 0423
LMArena Text: Chinese14631431DeepSeek V4 Flash 0423
LMArena Text: German14191341DeepSeek V4 Flash 0423
LMArena Text: Japanese1389Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Korean1383Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: French14391403DeepSeek V4 Flash 0423
LMArena Text: Spanish14361418DeepSeek V4 Flash 0423
LMArena Text: Russian14271336DeepSeek V4 Flash 0423
LMArena Text: Polish14361346DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491369DeepSeek V4 Flash 0423
LMArena Text: longer queries14341362DeepSeek V4 Flash 0423
LMArena Text: writing and language14101332DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001336DeepSeek V4 Flash 0423
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Flash 0423 and Nemotron 3 Super
BenchmarkDeepSeek V4 Flash 0423Nemotron 3 SuperLeader
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

DeepSeek V4 Flash 0423$0.0003Fits in one request
Nemotron 3 Super$0.0003Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0078Fits in one request
Nemotron 3 Super$0.0056Fits in one request

Nemotron 3 Super costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
Nemotron 3 Super$0.021Fits in one request

Nemotron 3 Super 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 DeepSeek V4 Flash 0423 and Nemotron 3 Super
SpecificationDeepSeek V4 Flash 0423NVIDIA Nemotron 3 Super
MakerDeepSeekNVIDIA
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026March 11, 2026
Context window1.05M262K
Input $/Mtok$0.14$0.085
Output $/Mtok$0.28$0.45
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Nemotron 3 Super for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 1.6x cheaper ($0.28 vs $0.45 per million) and a larger context window (1.05M against 262K tokens).

Which is cheaper, DeepSeek V4 Flash 0423 or Nemotron 3 Super?

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; Nemotron 3 Super costs $0.085 and $0.45. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Nemotron 3 Super?

DeepSeek V4 Flash 0423, with 1.05M tokens against 262K.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Nemotron 3 Super comparison based on?

31 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Nemotron 3 Super's on 0. Where only one model has a result, the page says so instead of naming a winner.

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