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Qwen3.6 27B vs Beam 1.0 501B A23B

Beam 1.0 501B A23B costs 2.3x less per output token than Qwen3.6 27B ($1.20 vs $2.70 per million). Updated October 22, 2026; no benchmark has tested both models yet.

Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
R Beam 1.0 501B A23BReflection
Coding
No data
Overall
No data
Output $/M
$1.20
vs $2.70 vs $1.20 per 1M output tokens. Beam 1.0 501B A23B is 2.3x cheaper.

Qwen3.6 27B or Beam 1.0 501B A23B: which to pick

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding.

Pick Beam 1.0 501B A23B for output tokens 2.3x cheaper ($1.20 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).

No benchmark has tested both yet. Qwen3.6 27B has results on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI); Beam 1.0 501B A23B on LMArena WebDev, LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead.

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
Qwen3.6 27B 36.8 from 2 benchmarks, Beam 1.0 501B A23B not benchmarked.
Widest category
None yet: no category has been tested on both models.
Widest single gap
None yet: no benchmark has tested both models.
Output price
Qwen3.6 27B $2.70 against Beam 1.0 501B A23B $1.20 per million output tokens, 2.3x apart.
Context window
Qwen3.6 27B takes 262K tokens, Beam 1.0 501B A23B 1.05M.

Benchmark by benchmark

Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.

CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards measured
Coding benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
LMArena WebDevNot measured1437Only Beam 1.0 501B A23B measured
AA Coding Index53.7Not measuredOnly Qwen3.6 27B measured
SWE-bench Verified (Vals AI)70.0%Not measuredOnly Qwen3.6 27B measured
Vibe Code Bench (Vals AI)11.9%Not measuredOnly Qwen3.6 27B measured
SciCode42.8%Not measuredOnly Qwen3.6 27B measured
Terminal-Bench Hard (Artificial Analysis run)34.8%Not measuredOnly Qwen3.6 27B measured
LMArena Text: codingNot measured1473Only Beam 1.0 501B A23B measured
LMArena Text: software and ITNot measured1461Only Beam 1.0 501B A23B measured
LMArena WebDev: ReactNot measured1443Only Beam 1.0 501B A23B measured
LMArena WebDev: HTMLNot measured1436Only Beam 1.0 501B A23B measured
Neither model has a result on the other 33 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Intelligence benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
AA Intelligence Index21.4Not measuredOnly Qwen3.6 27B measured
Epoch Capabilities Index146.2Not measuredOnly Qwen3.6 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Agentic work benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
AA Agentic Index18.5Not measuredOnly Qwen3.6 27B measured
τ²-Bench Telecom94.2%Not measuredOnly Qwen3.6 27B measured
Neither model has a result on the other 40 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
Reasoning benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
GPQA Diamond (Artificial Analysis run)84.2%Not measuredOnly Qwen3.6 27B measured
Humanity's Last Exam (Artificial Analysis run)23.1%Not measuredOnly Qwen3.6 27B measured
GPQA Diamond (Epoch AI run)85.9%Not measuredOnly Qwen3.6 27B measured
Mystery game puzzles7.0%Not measuredOnly Qwen3.6 27B measured
LMArena Text: hard promptsNot measured1442Only Beam 1.0 501B A23B measured
LMArena Text: expertNot measured1447Only Beam 1.0 501B A23B measured
LMArena Text: hard prompts in EnglishNot measured1451Only Beam 1.0 501B A23B measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
Math benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
OTIS Mock AIME91.1%Not measuredOnly Qwen3.6 27B measured
FrontierMath, tiers 1 to 335.1%Not measuredOnly Qwen3.6 27B measured
LMArena Text: mathNot measured1459Only Beam 1.0 501B A23B measured
LMArena Text: mathematical workNot measured1475Only Beam 1.0 501B A23B measured
Neither model has a result on the other 10 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
Knowledge benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
CaseLaw (Vals AI)53.2%Not measuredOnly Qwen3.6 27B measured
CorpFin (Vals AI)62.3%Not measuredOnly Qwen3.6 27B measured
TaxEval (Vals AI)71.3%Not measuredOnly Qwen3.6 27B measured
MortgageTax (Vals AI)68.3%Not measuredOnly Qwen3.6 27B measured
LMArena Text: scienceNot measured1430Only Beam 1.0 501B A23B measured
LMArena Text: medicine and healthcareNot measured1376Only Beam 1.0 501B A23B measured
LMArena Text: legal and governmentNot measured1433Only Beam 1.0 501B A23B measured
LMArena Text: business and financeNot measured1430Only Beam 1.0 501B A23B measured
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
Web design benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
Design Arena: websitesNot measured1245Only Beam 1.0 501B A23B measured
Design Arena: game devNot measured1220Only Beam 1.0 501B A23B measured
Neither model has a result on the other 7 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards measured
General benchmarks for Qwen3.6 27B and Beam 1.0 501B A23B
BenchmarkQwen3.6 27BBeam 1.0 501B A23BLeader
SAGE (Vals AI)45.6%Not measuredOnly Qwen3.6 27B measured
IFBench67.6%Not measuredOnly Qwen3.6 27B measured
Long context reasoning (AA-LCR)77.3%Not measuredOnly Qwen3.6 27B measured
DTBench78.1%Not measuredOnly Qwen3.6 27B measured
LMCA34.5%Not measuredOnly Qwen3.6 27B measured
LMArena TextNot measured1425Only Beam 1.0 501B A23B measured
LMArena Text: instruction followingNot measured1430Only Beam 1.0 501B A23B measured
LMArena Text: creative writingNot measured1390Only Beam 1.0 501B A23B measured
LMArena Text: EnglishNot measured1436Only Beam 1.0 501B A23B measured
LMArena Text: non-EnglishNot measured1413Only Beam 1.0 501B A23B measured
LMArena Text: ChineseNot measured1424Only Beam 1.0 501B A23B measured
LMArena Text: FrenchNot measured1461Only Beam 1.0 501B A23B measured
LMArena Text: RussianNot measured1412Only Beam 1.0 501B A23B measured
LMArena Text: multi-turnNot measured1414Only Beam 1.0 501B A23B measured
LMArena Text: longer queriesNot measured1429Only Beam 1.0 501B A23B measured
LMArena Text: writing and languageNot measured1400Only Beam 1.0 501B A23B measured
LMArena Text: entertainment and mediaNot measured1399Only Beam 1.0 501B A23B measured
EQ-Bench 41026Not measuredOnly Qwen3.6 27B measured
Neither model has a result on the other 30 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

Qwen3.6 27B$0.0018Fits in one request
Beam 1.0 501B A23B$0.0010Fits in one request

Beam 1.0 501B A23B costs less

Repository review

50K input + 3K output tokens

Qwen3.6 27B$0.031Fits in one request
Beam 1.0 501B A23B$0.024Fits in one request

Beam 1.0 501B A23B costs less

Agent loop

200K input + 10K output tokens

Qwen3.6 27B$0.117Fits in one request
Beam 1.0 501B A23B$0.092Fits 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 Qwen3.6 27B and Beam 1.0 501B A23B
SpecificationQwen3.6 27BReflection Beam 1.0 501B A23B
MakerQwenReflection
WeightsOpen weightsOpen weights
ReleasedApril 27, 2026October 16, 2026
Context window262K1.05M
Input $/Mtok$0.45$0.40
Output $/Mtok$2.70$1.20
Coding index36.8Not benchmarked
Independent measurements behind it21
Cost per solved task$1.30Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3.6 27B better than Beam 1.0 501B A23B for coding?

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Beam 1.0 501B A23B has none, so the two cannot be ranked on coding. Pick Beam 1.0 501B A23B for output tokens 2.3x cheaper ($1.20 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).

Which is cheaper, Qwen3.6 27B or Beam 1.0 501B A23B?

Qwen3.6 27B costs $0.45 per million input tokens and $2.70 per million output tokens; Beam 1.0 501B A23B costs $0.40 and $1.20. Cost per solved task is known for Qwen3.6 27B only.

Which has the bigger context window, Qwen3.6 27B or Beam 1.0 501B A23B?

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

Has any benchmark tested both Qwen3.6 27B and Beam 1.0 501B A23B?

No benchmark has tested both yet. Qwen3.6 27B has results on AA Coding Index, SWE-bench Verified (Vals AI) and Vibe Code Bench (Vals AI); Beam 1.0 501B A23B on LMArena WebDev, LMArena Text: coding and LMArena Text: software and IT. Compare them on price and context instead. Data as of October 22, 2026.

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