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Nova Pro 1.0 vs DeepSeek V4 Flash 0423

Updated October 21, 2026. 29 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Nova Pro 1.0Amazon
Coding
No data
Overall
7.0
Output $/M
$3.20
0
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
24.4
Output $/M
$0.28
29
vs boards won of 29 shared

Decision reading

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

Nova Pro 1.0 has the edge where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.

Shared results
29
Nova Pro 1.0 only
13
DeepSeek V4 Flash 0423 only
19
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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
Nova Pro 1.0 not benchmarked, DeepSeek V4 Flash 0423 not benchmarked.
Widest category
Web design, 51.5 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: websites: Nova Pro 1.0 796 against DeepSeek V4 Flash 0423 1208, 51.5 points apart on a 0 to 100 scale.
Output price
Nova Pro 1.0 $3.20 against DeepSeek V4 Flash 0423 $0.28 per million output tokens, 11.4x apart.
Context window
Nova Pro 1.0 takes 300K tokens, DeepSeek V4 Flash 0423 1.05M.

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
CategoryNova Pro 1.0BarsDeepSeek V4 Flash 0423Ties
Coding02
Intelligence01
Reasoning03
Math02
Knowledge04
Web design01
General016
All shared boards029
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
LMArena WebDevNot measured1582Only DeepSeek V4 Flash 0423 measured
AA Coding IndexNot measured52.0Only DeepSeek V4 Flash 0423 measured
LiveCodeBench (Artificial Analysis run)23.3%Not measuredOnly Nova Pro 1.0 measured
Terminal-Bench Hard (Artificial Analysis run)6.1%Not measuredOnly Nova Pro 1.0 measured
WeirdMLNot measured45.6%Only DeepSeek V4 Flash 0423 measured
ALE-BenchNot measured678.2Only DeepSeek V4 Flash 0423 measured
LMArena Text: coding12701457DeepSeek V4 Flash 0423
LMArena Text: software and IT12711453DeepSeek V4 Flash 0423
LMArena WebDev: ReactNot measured1585Only DeepSeek V4 Flash 0423 measured
LMArena WebDev: HTMLNot measured1558Only DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
AA Intelligence Index7.024.4DeepSeek V4 Flash 0423
Epoch Capabilities IndexNot measured146.1Only DeepSeek V4 Flash 0423 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
AA Agentic IndexNot measured26.3Only DeepSeek V4 Flash 0423 measured
τ²-Bench Telecom14.0%Not measuredOnly Nova Pro 1.0 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
GPQA Diamond (Artificial Analysis run)49.9%Not measuredOnly Nova Pro 1.0 measured
Humanity's Last Exam (Artificial Analysis run)3.2%Not measuredOnly Nova Pro 1.0 measured
SimpleBenchNot measured46.3%Only DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts12461444DeepSeek V4 Flash 0423
LMArena Text: expert12111441DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English12591449DeepSeek V4 Flash 0423
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
AA Math Index7.0Not measuredOnly Nova Pro 1.0 measured
AIME 2025 (Artificial Analysis run)7.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: math12521426DeepSeek V4 Flash 0423
LMArena Text: mathematical work12611432DeepSeek V4 Flash 0423
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
MMLU Pro (Artificial Analysis run)69.1%Not measuredOnly Nova Pro 1.0 measured
MMLU82.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: science12551446DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare12601448DeepSeek V4 Flash 0423
LMArena Text: legal and government12811439DeepSeek V4 Flash 0423
LMArena Text: business and finance12291425DeepSeek V4 Flash 0423
Neither model has a result on the other 11 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index01
Web design benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
Design Arena: websites7961208DeepSeek V4 Flash 0423
Design Arena: UI componentsNot measured1165Only DeepSeek V4 Flash 0423 measured
Design Arena: game devNot measured1197Only DeepSeek V4 Flash 0423 measured
Design Arena: data vizNot measured1127Only DeepSeek V4 Flash 0423 measured
Design Arena: 3DNot measured1189Only DeepSeek V4 Flash 0423 measured
Design Arena: all code categoriesNot measured1205Only DeepSeek V4 Flash 0423 measured
Design Arena: SVGNot measured1155Only DeepSeek V4 Flash 0423 measured
Design Arena: ASCII artNot measured1109Only 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 index016
General benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
IFBench38.1%Not measuredOnly Nova Pro 1.0 measured
Long context reasoning (AA-LCR)21.0%Not measuredOnly Nova Pro 1.0 measured
DTBenchNot measured86.4%Only DeepSeek V4 Flash 0423 measured
LMCANot measured35.9%Only DeepSeek V4 Flash 0423 measured
LMArena Text12591432DeepSeek V4 Flash 0423
LMArena Text: instruction following12351421DeepSeek V4 Flash 0423
LMArena Text: creative writing12121403DeepSeek V4 Flash 0423
LMArena Vision980Not measuredOnly Nova Pro 1.0 measured
LMArena Text: English12761441DeepSeek V4 Flash 0423
LMArena Text: non-English12331419DeepSeek V4 Flash 0423
LMArena Text: Chinese12441463DeepSeek V4 Flash 0423
LMArena Text: German12431419DeepSeek V4 Flash 0423
LMArena Text: Japanese12001389DeepSeek V4 Flash 0423
LMArena Text: Korean12031383DeepSeek V4 Flash 0423
LMArena Text: French12711439DeepSeek V4 Flash 0423
LMArena Text: Spanish11821436DeepSeek V4 Flash 0423
LMArena Text: Russian12401427DeepSeek V4 Flash 0423
LMArena Text: PolishNot measured1436Only DeepSeek V4 Flash 0423 measured
LMArena Text: multi-turn12461449DeepSeek V4 Flash 0423
LMArena Text: longer queries12551434DeepSeek V4 Flash 0423
LMArena Text: writing and language12471410DeepSeek V4 Flash 0423
LMArena Text: entertainment and media12101400DeepSeek V4 Flash 0423
LMArena Vision: English987Not measuredOnly Nova Pro 1.0 measured
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Nova Pro 1.0 and DeepSeek V4 Flash 0423
BenchmarkNova Pro 1.0DeepSeek V4 Flash 0423Leader
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

Nova Pro 1.0$0.0024Fits in one request
DeepSeek V4 Flash 0423$0.0003Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

Nova Pro 1.0$0.050Fits in one request
DeepSeek V4 Flash 0423$0.0078Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

Nova Pro 1.0$0.192Fits in one request
DeepSeek V4 Flash 0423$0.031Fits in one request

DeepSeek V4 Flash 0423 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 Nova Pro 1.0 and DeepSeek V4 Flash 0423
SpecificationAmazon Nova Pro 1.0DeepSeek V4 Flash 0423
MakerAmazonDeepSeek
WeightsNo public weights listedOpen weights
ReleasedDecember 5, 2024April 24, 2026
Context window300K1.05M
Input $/Mtok$0.80$0.14
Output $/Mtok$3.20$0.28
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Nova Pro 1.0 better than DeepSeek V4 Flash 0423 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Nova Pro 1.0 has the edge where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, Nova Pro 1.0 or DeepSeek V4 Flash 0423?

Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; DeepSeek V4 Flash 0423 costs $0.14 and $0.28. Cost per solved task is known for neither of them.

Which has the bigger context window, Nova Pro 1.0 or DeepSeek V4 Flash 0423?

DeepSeek V4 Flash 0423, with 1.05M tokens against 300K.

How many benchmarks is the Nova Pro 1.0 vs DeepSeek V4 Flash 0423 comparison based on?

29 benchmarks have a result for both models as of October 21, 2026. Nova Pro 1.0's coding index rests on 0 independent benchmarks and DeepSeek V4 Flash 0423's on 1. Where only one model has a result, the page says so instead of naming a winner.

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