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R1 0528 vs GLM 5.3

GLM 5.3 leads R1 0528 on the Artificial Analysis Intelligence Index, 44.8 to 13.1, and R1 0528 costs 2.0x less per output token ($2.18 vs $4.40 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace R1 0528 Replace GLM 5.3
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
0
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
43
vs boards won of 43 shared

R1 0528 or GLM 5.3: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding.

Pick R1 0528 for output tokens 2.0x cheaper ($2.18 vs $4.40 per million).

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
43
R1 0528 only
17
GLM 5.3 only
57
Like-for-like categories
7 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, GLM 5.3 70.6 from 6 benchmarks.
Widest category
Intelligence, 31.7 points apart on average over 1 shared benchmark.
Widest single gap
WeirdML: R1 0528 41.6% against GLM 5.3 75.4%, 33.8 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against GLM 5.3 $4.40 per million output tokens, 2.0x apart.
Context window
R1 0528 takes 164K tokens, GLM 5.3 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
CategoryR1 0528BarsGLM 5.3Ties
Coding04
Intelligence01
Reasoning06
Math03
Knowledge04
Web design07
General018
All shared boards043
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDevNot measured1621Only GLM 5.3 measured
AA Coding IndexNot measured74.8Only GLM 5.3 measured
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)Not measured78.1%Only GLM 5.3 measured
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
SciCodeNot measured59.0%Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%Not measuredOnly R1 0528 measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdML41.6%75.4%GLM 5.3
ALE-Bench804.11317.4GLM 5.3
LMArena Text: coding14271497GLM 5.3
LMArena Text: software and IT14351493GLM 5.3
LMArena WebDev: ReactNot measured1641Only GLM 5.3 measured
LMArena WebDev: HTMLNot measured1595Only GLM 5.3 measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
AA Intelligence Index13.144.8GLM 5.3
Epoch Capabilities IndexNot measured156.0Only GLM 5.3 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
Agentic work benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1230Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom36.5%Not measuredOnly R1 0528 measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)81.3%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)15.8%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)76.3%90.9%GLM 5.3
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts14151490GLM 5.3
LMArena Text: expert13941514GLM 5.3
LMArena Text: hard prompts in English14221496GLM 5.3
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%91.1%GLM 5.3
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001495GLM 5.3
LMArena Text: mathematical work14151497GLM 5.3
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)Not measured84.8%Only GLM 5.3 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science14301498GLM 5.3
LMArena Text: medicine and healthcare14451481GLM 5.3
LMArena Text: legal and government14191473GLM 5.3
LMArena Text: business and finance14021459GLM 5.3
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
Design Arena: websites11491295GLM 5.3
Design Arena: UI components11001314GLM 5.3
Design Arena: game dev10971311GLM 5.3
Design Arena: data viz11821260GLM 5.3
Design Arena: 3D11101332GLM 5.3
Design Arena: all code categories11381304GLM 5.3
Design Arena: SVG10381294GLM 5.3
Design Arena: ASCII artNot measured1208Only GLM 5.3 measured
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
IFBench39.6%Not measuredOnly R1 0528 measured
Long context reasoning (AA-LCR)55.7%79.7%GLM 5.3
DTBenchNot measured87.7%Only GLM 5.3 measured
LMCANot measured55.5%Only GLM 5.3 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text14281474GLM 5.3
LMArena Text: instruction following13821479GLM 5.3
LMArena Text: creative writing14051458GLM 5.3
LMArena Text: English14371482GLM 5.3
LMArena Text: non-English14121459GLM 5.3
LMArena Text: Chinese14421532GLM 5.3
LMArena Text: German14041488GLM 5.3
LMArena Text: Japanese13911460GLM 5.3
LMArena Text: Korean13601460GLM 5.3
LMArena Text: French14171503GLM 5.3
LMArena Text: Spanish14111462GLM 5.3
LMArena Text: Russian14231469GLM 5.3
LMArena Text: Polish14281466GLM 5.3
LMArena Text: multi-turn14051472GLM 5.3
LMArena Text: longer queries13911483GLM 5.3
LMArena Text: writing and language14021469GLM 5.3
LMArena Text: entertainment and media14001447GLM 5.3
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and GLM 5.3
BenchmarkR1 0528GLM 5.3Leader
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
GLM 5.3$0.0036Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
GLM 5.3$0.083Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
GLM 5.3$0.324Fits in one request

R1 0528 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 GLM 5.3
SpecificationDeepSeek R1 0528Z.ai GLM 5.3
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025August 18, 2026
Context window164K1.05M
Input $/Mtok$0.50$1.40
Output $/Mtok$2.18$4.40
Coding indexNot benchmarked70.6
Independent measurements behind it06
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 5.3 for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; R1 0528 has none, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 2.0x cheaper ($2.18 vs $4.40 per million).

Which is cheaper, R1 0528 or GLM 5.3?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, R1 0528 or GLM 5.3?

GLM 5.3, with 1.05M tokens against 164K.

How many benchmarks is the R1 0528 vs GLM 5.3 comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and GLM 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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