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R1 0528 vs Qwen3 30B A3B

R1 0528 leads Qwen3 30B A3B on the Artificial Analysis Intelligence Index, 13.1 to 7.6, and Qwen3 30B A3B costs 4.4x less per output token ($0.50 vs $2.18 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace R1 0528 Replace Qwen3 30B A3B
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
48
Qwen3 30B A3BQwen
Coding
No data
Overall
38.2
Output $/M
$0.50
1
vs boards won of 49 shared

R1 0528 or Qwen3 30B A3B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick R1 0528 for a larger context window (164K against 41K tokens) and work measured by IFBench, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 30B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
49
R1 0528 only
11
Qwen3 30B A3B only
3
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 not benchmarked, Qwen3 30B A3B not benchmarked.
Widest category
Web design, 24.1 points apart on average over 5 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): R1 0528 55.7% against Qwen3 30B A3B 0.0%, 55.7 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against Qwen3 30B A3B $0.50 per million output tokens, 4.4x apart.
Context window
R1 0528 takes 164K tokens, Qwen3 30B A3B 41K.

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 30B A3BTies
Coding50
Intelligence10
Agentic work10
Reasoning60
Math50
Knowledge50
Web design50
General201
All shared boards481
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LiveCodeBench (Artificial Analysis run)77.0%50.6%R1 0528
Terminal-Bench Hard (Artificial Analysis run)15.9%6.8%R1 0528
WeirdML41.6%29.8%R1 0528
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271337R1 0528
LMArena Text: software and IT14351340R1 0528
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
AA Intelligence Index13.17.6R1 0528
Epoch Capabilities IndexNot measured137.3Only Qwen3 30B A3B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
τ²-Bench Telecom36.5%26.0%R1 0528
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
GPQA Diamond (Artificial Analysis run)81.3%61.6%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%6.2%R1 0528
GPQA Diamond (Epoch AI run)76.3%61.7%R1 0528
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 prompts14151314R1 0528
LMArena Text: expert13941314R1 0528
LMArena Text: hard prompts in English14221329R1 0528
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
AA Math Index76.072.3R1 0528
AIME 2025 (Artificial Analysis run)76.0%72.3%R1 0528
OTIS Mock AIME66.4%62.8%R1 0528
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001355R1 0528
LMArena Text: mathematical work14151363R1 0528
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
MMLU Pro (Artificial Analysis run)84.9%77.7%R1 0528
LMArena Text: science14301321R1 0528
LMArena Text: medicine and healthcare14451318R1 0528
LMArena Text: legal and government14191314R1 0528
LMArena Text: business and finance14021309R1 0528
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
Design Arena: websites1149954R1 0528
Design Arena: UI components1100943R1 0528
Design Arena: game dev1097898R1 0528
Design Arena: data viz1182967R1 0528
Design Arena: 3D1110Not measuredOnly R1 0528 measured
Design Arena: all code categories1138941R1 0528
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 index201
General benchmarks for R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
IFBench39.6%41.5%Qwen3 30B A3B
Long context reasoning (AA-LCR)55.7%0.0%R1 0528
DTBenchNot measured60.3%Only Qwen3 30B A3B measured
LMCANot measured15.8%Only Qwen3 30B A3B measured
Fiction.LiveBench75.0%40.6%R1 0528
Creative writing (Lech Mazur)81.9%75.3%R1 0528
LMArena Text14281317R1 0528
LMArena Text: instruction following13821283R1 0528
LMArena Text: creative writing14051271R1 0528
LMArena Text: English14371335R1 0528
LMArena Text: non-English14121295R1 0528
LMArena Text: Chinese14421347R1 0528
LMArena Text: German14041307R1 0528
LMArena Text: Japanese13911254R1 0528
LMArena Text: Korean13601261R1 0528
LMArena Text: French14171352R1 0528
LMArena Text: Spanish14111317R1 0528
LMArena Text: Russian14231291R1 0528
LMArena Text: Polish14281299R1 0528
LMArena Text: multi-turn14051307R1 0528
LMArena Text: longer queries13911312R1 0528
LMArena Text: writing and language14021290R1 0528
LMArena Text: entertainment and media14001258R1 0528
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 R1 0528 and Qwen3 30B A3B
BenchmarkR1 0528Qwen3 30B A3BLeader
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 30B A3B$0.0004Fits in one request

Qwen3 30B A3B costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
Qwen3 30B A3B$0.0075Exceeds the context window

Qwen3 30B A3B costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
Qwen3 30B A3B$0.029Exceeds the context window

Qwen3 30B A3B 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 30B A3B
SpecificationDeepSeek R1 0528Qwen3 30B A3B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025April 28, 2025
Context window164K41K
Input $/Mtok$0.50$0.12
Output $/Mtok$2.18$0.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Qwen3 30B A3B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for a larger context window (164K against 41K tokens) and work measured by IFBench, where it leads.

Which is cheaper, R1 0528 or Qwen3 30B A3B?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; Qwen3 30B A3B costs $0.12 and $0.50. Cost per solved task is known for neither of them.

Which has the bigger context window, R1 0528 or Qwen3 30B A3B?

R1 0528, with 164K tokens against 41K.

How many benchmarks is the R1 0528 vs Qwen3 30B A3B comparison based on?

49 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and Qwen3 30B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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