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R1 0528 vs Qwen3.8 27B

Updated October 21, 2026. 34 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 Qwen3.8 27B
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
5
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
29
vs boards won of 34 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 71.4 for R1 0528.

Pick R1 0528 when what matters is LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.

Model AR1 0528DeepSeek71.4/1001 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
34
R1 0528 only
25
Qwen3.8 27B only
51
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
R1 0528 71.4 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Reasoning, 23.3 points apart on average over 7 shared benchmarks.
Widest single gap
ARC-AGI: R1 0528 21.2% against Qwen3.8 27B 87.5%, 66.3 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against Qwen3.8 27B $1.35 per million output tokens, 1.6x apart.
Context window
R1 0528 takes 164K tokens, Qwen3.8 27B 1M.

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 0528BarsQwen3.8 27BTies
Coding02
Intelligence01
Reasoning07
Math02
Knowledge13
General414
All shared boards529
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
Coding benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDevNot measured1593Only Qwen3.8 27B measured
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)15.9%Not measuredOnly R1 0528 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271483Qwen3.8 27B
LMArena Text: software and IT14351478Qwen3.8 27B
LMArena WebDev: ReactNot measured1602Only Qwen3.8 27B measured
LMArena WebDev: HTMLNot measured1570Only Qwen3.8 27B measured
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
AA Intelligence Index13.133.7Qwen3.8 27B
Epoch Capabilities IndexNot measured151.7Only Qwen3.8 27B measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Agentic work benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom36.5%Not measuredOnly R1 0528 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)81.3%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)15.8%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)76.3%Not measuredOnly R1 0528 measured
ARC-AGI21.2%87.5%Qwen3.8 27B
ARC-AGI-21.1%42.4%Qwen3.8 27B
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151459Qwen3.8 27B
LMArena Text: expert13941479Qwen3.8 27B
LMArena Text: hard prompts in English14221461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 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
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001455Qwen3.8 27B
LMArena Text: mathematical work14151458Qwen3.8 27B
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
LMArena Text: science14301467Qwen3.8 27B
LMArena Text: medicine and healthcare14451437R1 0528
LMArena Text: legal and government14191447Qwen3.8 27B
LMArena Text: business and finance14021438Qwen3.8 27B
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 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
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index414
General benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%82.0%Qwen3.8 27B
DTBenchNot measured88.0%Only Qwen3.8 27B measured
LMCANot measured41.4%Only Qwen3.8 27B measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14281440Qwen3.8 27B
LMArena Text: instruction following13821436Qwen3.8 27B
LMArena Text: creative writing14051383R1 0528
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14371448Qwen3.8 27B
LMArena Text: non-English14121428Qwen3.8 27B
LMArena Text: Chinese14421501Qwen3.8 27B
LMArena Text: German14041443Qwen3.8 27B
LMArena Text: Japanese13911387R1 0528
LMArena Text: Korean13601384Qwen3.8 27B
LMArena Text: French14171465Qwen3.8 27B
LMArena Text: Spanish14111449Qwen3.8 27B
LMArena Text: Russian14231415R1 0528
LMArena Text: Polish14281439Qwen3.8 27B
LMArena Text: multi-turn14051440Qwen3.8 27B
LMArena Text: longer queries13911448Qwen3.8 27B
LMArena Text: writing and language14021405Qwen3.8 27B
LMArena Text: entertainment and media14001393R1 0528
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and Qwen3.8 27B
BenchmarkR1 0528Qwen3.8 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
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
Qwen3.8 27B$0.021Fits in one request

Qwen3.8 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 Qwen3.8 27B
SpecificationDeepSeek R1 0528Qwen3.8 27B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025August 14, 2026
Context window164K1M
Input $/Mtok$0.50$0.04
Output $/Mtok$2.15$1.35
Coding index71.473.6
Independent measurements behind it12
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 71.4 for R1 0528. Pick R1 0528 when what matters is LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.

Which is cheaper, R1 0528 or Qwen3.8 27B?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, R1 0528 or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 164K.

How many benchmarks is the R1 0528 vs Qwen3.8 27B comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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