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Qwen3.5-27B vs Beam 1.0 501B A23B

Updated October 21, 2026. 26 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Qwen3.5-27B Replace Beam 1.0 501B A23B
Qwen3.5-27BQwen
Coding
22.6
Overall
22.9
Output $/M
$2.16
3
R Beam 1.0 501B A23BReflection
Coding
33.8
Overall
No data
Output $/M
$1.00
23
vs boards won of 26 shared

Decision reading

Pick Beam 1.0 501B A23B for coding: it scores 33.8 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.

Pick Qwen3.5-27B when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.

Model AQwen3.5-27BQwen22.6/1001 independent benchmarkNo cost per solved taskModel BBeam 1.0 501B A23BReflection33.8/1001 independent benchmarkNo cost per solved task
Shared results
26
Qwen3.5-27B only
27
Beam 1.0 501B A23B only
2
Like-for-like categories
5 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
Qwen3.5-27B 22.6 from 1 benchmark, Beam 1.0 501B A23B 33.8 from 1 benchmark.
Widest category
Coding, 9.7 points apart on average over 5 shared benchmarks.
Widest single gap
LMArena WebDev: React: Qwen3.5-27B 1346 against Beam 1.0 501B A23B 1443, 16.1 points apart on a 0 to 100 scale.
Output price
Qwen3.5-27B $2.16 against Beam 1.0 501B A23B $1.00 per million output tokens, 2.2x apart.
Context window
Qwen3.5-27B takes 262K 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
CategoryQwen3.5-27BBarsBeam 1.0 501B A23BTies
Coding05
Reasoning03
Math02
Knowledge22
General111
All shared boards323
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
LMArena WebDev13581437Beam 1.0 501B A23B
Terminal-Bench Hard (Artificial Analysis run)32.6%Not measuredOnly Qwen3.5-27B measured
WeirdML39.5%Not measuredOnly Qwen3.5-27B measured
ALE-Bench349.4Not measuredOnly Qwen3.5-27B measured
LMArena Text: coding14271473Beam 1.0 501B A23B
LMArena Text: software and IT14311461Beam 1.0 501B A23B
LMArena WebDev: React13461443Beam 1.0 501B A23B
LMArena WebDev: HTML13861436Beam 1.0 501B A23B
Neither model has a result on the other 30 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Intelligence benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
AA Intelligence Index22.9Not measuredOnly Qwen3.5-27B measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
τ²-Bench Telecom93.9%Not measuredOnly Qwen3.5-27B measured
Vending-Bench 2$202Not measuredOnly Qwen3.5-27B 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 Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
GPQA Diamond (Artificial Analysis run)85.8%Not measuredOnly Qwen3.5-27B measured
Humanity's Last Exam (Artificial Analysis run)23.9%Not measuredOnly Qwen3.5-27B measured
LMArena Text: hard prompts14151442Beam 1.0 501B A23B
LMArena Text: expert14271447Beam 1.0 501B A23B
LMArena Text: hard prompts in English14291451Beam 1.0 501B A23B
LMArena Vision: diagrams1249Not measuredOnly Qwen3.5-27B measured
LMArena Vision: homework1256Not measuredOnly Qwen3.5-27B measured
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
LMArena Text: math14291459Beam 1.0 501B A23B
LMArena Text: mathematical work14291475Beam 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 index22
Knowledge benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
LMArena Text: science14371430Qwen3.5-27B
LMArena Text: medicine and healthcare14251376Qwen3.5-27B
LMArena Text: legal and government14051433Beam 1.0 501B A23B
LMArena Text: business and finance13991430Beam 1.0 501B A23B
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Web design benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
Design Arena: websitesNot measured1253Only Beam 1.0 501B A23B measured
Design Arena: game devNot measured1222Only Beam 1.0 501B A23B measured
Neither model has a result on the other 7 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index111
General benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 1.0 501B A23BLeader
IFBench75.6%Not measuredOnly Qwen3.5-27B measured
Long context reasoning (AA-LCR)77.7%Not measuredOnly Qwen3.5-27B measured
DTBench82.4%Not measuredOnly Qwen3.5-27B measured
LMCA34.0%Not measuredOnly Qwen3.5-27B measured
LMArena Text14091425Beam 1.0 501B A23B
LMArena Text: instruction following13931430Beam 1.0 501B A23B
LMArena Text: creative writing13621390Beam 1.0 501B A23B
LMArena Vision1241Not measuredOnly Qwen3.5-27B measured
LMArena Text: English14251436Beam 1.0 501B A23B
LMArena Text: non-English13901413Beam 1.0 501B A23B
LMArena Text: Chinese14781424Qwen3.5-27B
LMArena Text: German1392Not measuredOnly Qwen3.5-27B measured
LMArena Text: Japanese1346Not measuredOnly Qwen3.5-27B measured
LMArena Text: Korean1359Not measuredOnly Qwen3.5-27B measured
LMArena Text: French14101461Beam 1.0 501B A23B
LMArena Text: Spanish1407Not measuredOnly Qwen3.5-27B measured
LMArena Text: Russian13901412Beam 1.0 501B A23B
LMArena Text: Polish1375Not measuredOnly Qwen3.5-27B measured
LMArena Text: multi-turn14101414Beam 1.0 501B A23B
LMArena Text: longer queries14131429Beam 1.0 501B A23B
LMArena Text: writing and language13801400Beam 1.0 501B A23B
LMArena Text: entertainment and media13631399Beam 1.0 501B A23B
LMArena Vision: English1246Not measuredOnly Qwen3.5-27B measured
LMArena Vision: Chinese1301Not measuredOnly Qwen3.5-27B measured
LMArena Vision: reading text1246Not measuredOnly Qwen3.5-27B measured
LMArena Vision: humor1227Not measuredOnly Qwen3.5-27B measured
LMArena Vision: captioning1170Not measuredOnly Qwen3.5-27B measured
LMArena Vision: recognizing things1210Not measuredOnly Qwen3.5-27B measured
LMArena Vision: creative writing1224Not measuredOnly Qwen3.5-27B measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Qwen3.5-27B and Beam 1.0 501B A23B
BenchmarkQwen3.5-27BBeam 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

Qwen3.5-27B$0.0014Fits in one request
Beam 1.0 501B A23B$0.0008Fits in one request

Beam 1.0 501B A23B costs less

Repository review

50K input + 3K output tokens

Qwen3.5-27B$0.020Fits in one request
Beam 1.0 501B A23B$0.018Fits in one request

Beam 1.0 501B A23B costs less

Agent loop

200K input + 10K output tokens

Qwen3.5-27B$0.076Fits in one request
Beam 1.0 501B A23B$0.070Fits in one request

Beam 1.0 501B A23B 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 Qwen3.5-27B and Beam 1.0 501B A23B
SpecificationQwen3.5-27BReflection Beam 1.0 501B A23B
MakerQwenReflection
WeightsOpen weightsOpen weights
ReleasedFebruary 25, 2026October 16, 2026
Context window262K1.05M
Input $/Mtok$0.27$0.30
Output $/Mtok$2.16$1.00
Coding index22.633.8
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3.5-27B better than Beam 1.0 501B A23B for coding?

Pick Beam 1.0 501B A23B for coding: it scores 33.8 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when what matters is LMArena Text: science or LMArena Text: medicine and healthcare, where it leads.

Which is cheaper, Qwen3.5-27B or Beam 1.0 501B A23B?

Qwen3.5-27B costs $0.27 per million input tokens and $2.16 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, Qwen3.5-27B or Beam 1.0 501B A23B?

Beam 1.0 501B A23B, with 1.05M tokens against 262K.

How many benchmarks is the Qwen3.5-27B vs Beam 1.0 501B A23B comparison based on?

26 benchmarks have a result for both models as of October 21, 2026. Qwen3.5-27B'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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