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DeepSeek V3.2 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 V3.2 Replace Beam 1.0 501B A23B
DeepSeek V3.2DeepSeek
Coding
54.4
Overall
21.5
Output $/M
$0.42
8
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
20
vs boards won of 28 shared

Decision reading

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B.

Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Model ADeepSeek V3.2DeepSeek54.4/1002 independent benchmarks$0.99 per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
28
DeepSeek V3.2 only
47
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 V3.2 54.4 from 2 benchmarks, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Web design, 10.5 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena WebDev: React: DeepSeek V3.2 1350 against Beam 1.0 501B A23B 1443, 15.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.42 against Beam 1.0 501B A23B $1.00 per million output tokens, 2.4x apart.
Context window
DeepSeek V3.2 takes 164K tokens, Beam 1.0 501B A23B 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
CategoryDeepSeek V3.2BarsBeam 1.0 501B A23BTies
Coding05
Reasoning03
Math02
Knowledge22
Web design02
General66
All shared boards820
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621437Beam 1.0 501B A23B
AA Coding Index44.2Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
Terminal-Bench Hard (Artificial Analysis run)35.6%Not measuredOnly DeepSeek V3.2 measured
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: coding14531473Beam 1.0 501B A23B
LMArena Text: software and IT14491461Beam 1.0 501B A23B
LMArena WebDev: React13501443Beam 1.0 501B A23B
LMArena WebDev: HTML13711436Beam 1.0 501B A23B
Neither model has a result on the other 24 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
AA Intelligence Index21.5Not measuredOnly DeepSeek V3.2 measured
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
τ²-Bench Telecom90.6%Not measuredOnly DeepSeek V3.2 measured
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%Not measuredOnly DeepSeek V3.2 measured
Humanity's Last Exam (Artificial Analysis run)24.6%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341442Beam 1.0 501B A23B
LMArena Text: expert14361447Beam 1.0 501B A23B
LMArena Text: hard prompts in English14461451Beam 1.0 501B A23B
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: math14351459Beam 1.0 501B A23B
LMArena Text: mathematical work14401475Beam 1.0 501B A23B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
Knowledge benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: science14461430DeepSeek V3.2
LMArena Text: medicine and healthcare14401376DeepSeek V3.2
LMArena Text: legal and government14251433Beam 1.0 501B A23B
LMArena Text: business and finance14101430Beam 1.0 501B A23B
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index02
Web design benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
Design Arena: websites11751253Beam 1.0 501B A23B
Design Arena: UI components1149Not measuredOnly DeepSeek V3.2 measured
Design Arena: game dev11321222Beam 1.0 501B A23B
Design Arena: data viz1158Not measuredOnly DeepSeek V3.2 measured
Design Arena: 3D1133Not measuredOnly DeepSeek V3.2 measured
Design Arena: all code categories1164Not measuredOnly DeepSeek V3.2 measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1082Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index66
General benchmarks for DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 1.0 501B A23BLeader
IFBench60.7%Not measuredOnly DeepSeek V3.2 measured
Long context reasoning (AA-LCR)73.3%Not measuredOnly DeepSeek V3.2 measured
DTBench85.6%Not measuredOnly DeepSeek V3.2 measured
LMCA28.8%Not measuredOnly DeepSeek V3.2 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251425Beam 1.0 501B A23B
LMArena Text: instruction following14131430Beam 1.0 501B A23B
LMArena Text: creative writing14001390DeepSeek V3.2
LMArena Text: English14391436DeepSeek V3.2
LMArena Text: non-English14091413Beam 1.0 501B A23B
LMArena Text: Chinese14551424DeepSeek V3.2
LMArena Text: German1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Japanese1374Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Korean1370Not measuredOnly DeepSeek V3.2 measured
LMArena Text: French14291461Beam 1.0 501B A23B
LMArena Text: Spanish1424Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Russian14241412DeepSeek V3.2
LMArena Text: Polish1419Not measuredOnly DeepSeek V3.2 measured
LMArena Text: multi-turn14271414DeepSeek V3.2
LMArena Text: longer queries14281429Beam 1.0 501B A23B
LMArena Text: writing and language14071400DeepSeek V3.2
LMArena Text: entertainment and media13951399Beam 1.0 501B A23B
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 DeepSeek V3.2 and Beam 1.0 501B A23B
BenchmarkDeepSeek V3.2Beam 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 V3.2$0.0005Fits in one request
Beam 1.0 501B A23B$0.0008Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.015Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.060Exceeds the context window
Beam 1.0 501B A23B$0.070Fits in one request

DeepSeek V3.2 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 V3.2 and Beam 1.0 501B A23B
SpecificationDeepSeek V3.2Reflection Beam 1.0 501B A23B
MakerDeepSeekReflection
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025October 16, 2026
Context window164K1.05M
Input $/Mtok$0.28$0.30
Output $/Mtok$0.42$1.00
Coding index54.433.8
Independent measurements behind it21
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Beam 1.0 501B A23B for coding?

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 33.8 for Beam 1.0 501B A23B. Pick Beam 1.0 501B A23B when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or LMArena Text: coding, where it leads.

Which is cheaper, DeepSeek V3.2 or Beam 1.0 501B A23B?

DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Beam 1.0 501B A23B costs $0.30 and $1.00. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Beam 1.0 501B A23B?

Beam 1.0 501B A23B, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Beam 1.0 501B A23B comparison based on?

28 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks 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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