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R1 0528 vs Qwen3.5-27B

Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace R1 0528 Replace Qwen3.5-27B
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
21
Qwen3.5-27BQwen
Coding
22.6
Overall
22.9
Output $/M
$2.16
16
vs boards won of 37 shared

Decision reading

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.

Pick Qwen3.5-27B when the context window is larger, 262K against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Model AR1 0528DeepSeek71.4/1001 independent benchmarkNo cost per solved taskModel BQwen3.5-27BQwen22.6/1001 independent benchmarkNo cost per solved task
Shared results
37
R1 0528 only
22
Qwen3.5-27B only
16
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
R1 0528 71.4 from 1 benchmark, Qwen3.5-27B 22.6 from 1 benchmark.
Widest category
Agentic work, 57.3 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against Qwen3.5-27B 93.9%, 57.3 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against Qwen3.5-27B $2.16 per million output tokens, 1.0x apart.
Context window
R1 0528 takes 164K tokens, Qwen3.5-27B 262K.

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
CategoryR1 0528BarsQwen3.5-27BTies
Coding41
Intelligence01
Agentic work01
Reasoning14
Math02
Knowledge31
General136
All shared boards2116
CodingLike-for-likeFour of these boards feed the Vibe Coding Index41
Coding benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%32.6%Qwen3.5-27B
WeirdML41.6%39.5%R1 0528
ALE-Bench804.1349.4R1 0528
LMArena Text: coding14271427R1 0528
LMArena Text: software and IT14351431R1 0528
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B 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 R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
AA Intelligence Index13.122.9Qwen3.5-27B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
τ²-Bench Telecom36.5%93.9%Qwen3.5-27B
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
GPQA Diamond (Artificial Analysis run)81.3%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)15.8%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)76.3%Not measuredOnly R1 0528 measured
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151415R1 0528
LMArena Text: expert13941427Qwen3.5-27B
LMArena Text: hard prompts in English14221429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001429Qwen3.5-27B
LMArena Text: mathematical work14151429Qwen3.5-27B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
Knowledge benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
LMArena Text: science14301437Qwen3.5-27B
LMArena Text: medicine and healthcare14451425R1 0528
LMArena Text: legal and government14191405R1 0528
LMArena Text: business and finance14021399R1 0528
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
Design Arena: websites1149Not measuredOnly R1 0528 measured
Design Arena: UI components1102Not measuredOnly R1 0528 measured
Design Arena: game dev1098Not measuredOnly R1 0528 measured
Design Arena: data viz1182Not measuredOnly R1 0528 measured
Design Arena: 3D1111Not measuredOnly R1 0528 measured
Design Arena: all code categories1139Not measuredOnly R1 0528 measured
Design Arena: SVG1038Not measuredOnly R1 0528 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index136
General benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
IFBench39.6%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)55.7%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text14281409R1 0528
LMArena Text: instruction following13821393Qwen3.5-27B
LMArena Text: creative writing14051362R1 0528
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English14371425R1 0528
LMArena Text: non-English14121390R1 0528
LMArena Text: Chinese14421478Qwen3.5-27B
LMArena Text: German14041392R1 0528
LMArena Text: Japanese13911346R1 0528
LMArena Text: Korean13601359R1 0528
LMArena Text: French14171410R1 0528
LMArena Text: Spanish14111407R1 0528
LMArena Text: Russian14231390R1 0528
LMArena Text: Polish14281375R1 0528
LMArena Text: multi-turn14051410Qwen3.5-27B
LMArena Text: longer queries13911413Qwen3.5-27B
LMArena Text: writing and language14021380R1 0528
LMArena Text: entertainment and media14001363R1 0528
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and Qwen3.5-27B
BenchmarkR1 0528Qwen3.5-27BLeader
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

R1 0528$0.0016Fits in one request
Qwen3.5-27B$0.0014Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
Qwen3.5-27B$0.020Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
Qwen3.5-27B$0.076Fits in one request

Qwen3.5-27B 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 R1 0528 and Qwen3.5-27B
SpecificationDeepSeek R1 0528Qwen3.5-27B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025February 25, 2026
Context window164K262K
Input $/Mtok$0.50$0.27
Output $/Mtok$2.15$2.16
Coding index71.422.6
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Qwen3.5-27B for coding?

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when the context window is larger, 262K against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.

Which is cheaper, R1 0528 or Qwen3.5-27B?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for neither of them.

Which has the bigger context window, R1 0528 or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 164K.

How many benchmarks is the R1 0528 vs Qwen3.5-27B comparison based on?

37 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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