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DeepSeek V4 Flash 0423 vs Beam 1.0 501B A23B

Updated October 21, 2026. 28 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Flash 0423 Replace Beam 1.0 501B A23B
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
17
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
11
vs boards won of 28 shared

Decision reading

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.

Pick Beam 1.0 501B A23B when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
28
DeepSeek V4 Flash 0423 only
20
Beam 1.0 501B A23B only
0
Like-for-like categories
6 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
DeepSeek V4 Flash 0423 54.6 from 1 benchmark, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Coding, 13.7 points apart on average over 5 shared benchmarks.
Widest single gap
LMArena WebDev: React: DeepSeek V4 Flash 0423 1585 against Beam 1.0 501B A23B 1443, 23.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against Beam 1.0 501B A23B $1.00 per million output tokens, 5.5x apart.
Context window
Both take 1.05M tokens.

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 0423BarsBeam 1.0 501B A23BTies
Coding32
Reasoning12
Math02
Knowledge31
Web design02
General102
All shared boards1711
CodingLike-for-likeFour of these boards feed the Vibe Coding Index32
Coding benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
LMArena WebDev15821437DeepSeek V4 Flash 0423
AA Coding Index52.0Not measuredOnly DeepSeek V4 Flash 0423 measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571473Beam 1.0 501B A23B
LMArena Text: software and IT14531461Beam 1.0 501B A23B
LMArena WebDev: React15851443DeepSeek V4 Flash 0423
LMArena WebDev: HTML15581436DeepSeek V4 Flash 0423
Neither model has a result on the other 30 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
AA Intelligence Index24.4Not measuredOnly DeepSeek V4 Flash 0423 measured
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V4 Flash 0423 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441442DeepSeek V4 Flash 0423
LMArena Text: expert14411447Beam 1.0 501B A23B
LMArena Text: hard prompts in English14491451Beam 1.0 501B A23B
Neither model has a result on the other 17 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
LMArena Text: math14261459Beam 1.0 501B A23B
LMArena Text: mathematical work14321475Beam 1.0 501B A23B
Neither model has a result on the other 11 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
LMArena Text: science14461430DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481376DeepSeek V4 Flash 0423
LMArena Text: legal and government14391433DeepSeek V4 Flash 0423
LMArena Text: business and finance14251430Beam 1.0 501B A23B
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
Design Arena: websites12081253Beam 1.0 501B A23B
Design Arena: UI components1164Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: game dev11971222Beam 1.0 501B A23B
Design Arena: data viz1127Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: 3D1189Not 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 index102
General benchmarks for DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
DTBench86.4%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMCA35.9%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text14321425DeepSeek V4 Flash 0423
LMArena Text: instruction following14211430Beam 1.0 501B A23B
LMArena Text: creative writing14031390DeepSeek V4 Flash 0423
LMArena Text: English14411436DeepSeek V4 Flash 0423
LMArena Text: non-English14191413DeepSeek V4 Flash 0423
LMArena Text: Chinese14631424DeepSeek V4 Flash 0423
LMArena Text: German1419Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Japanese1389Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Korean1383Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: French14391461Beam 1.0 501B A23B
LMArena Text: Spanish1436Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Russian14271412DeepSeek V4 Flash 0423
LMArena Text: Polish1436Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: multi-turn14491414DeepSeek V4 Flash 0423
LMArena Text: longer queries14341429DeepSeek V4 Flash 0423
LMArena Text: writing and language14101400DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001399DeepSeek V4 Flash 0423
Neither model has a result on the other 27 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 Beam 1.0 501B A23B
BenchmarkDeepSeek V4 Flash 0423Beam 1.0 501B A23BLeader
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$0.0002Fits in one request
Beam 1.0 501B A23B$0.0008Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0051Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.020Fits in one request
Beam 1.0 501B A23B$0.070Fits 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 DeepSeek V4 Flash 0423 and Beam 1.0 501B A23B
SpecificationDeepSeek V4 Flash 0423Reflection Beam 1.0 501B A23B
MakerDeepSeekReflection
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026October 16, 2026
Context window1.05M1.05M
Input $/Mtok$0.091$0.30
Output $/Mtok$0.182$1.00
Coding index54.633.8
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Beam 1.0 501B A23B for coding?

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, DeepSeek V4 Flash 0423 or Beam 1.0 501B A23B?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Beam 1.0 501B A23B?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Beam 1.0 501B A23B comparison based on?

28 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Beam 1.0 501B A23B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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