LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

R1 0528 vs GLM 5.2

Updated October 21, 2026. 49 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 GLM 5.2
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
0
GLM 5.2Z.ai
Coding
57.7
Overall
33.7
Output $/M
$7.00
49
vs boards won of 49 shared

Decision reading

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 57.7 for GLM 5.2.

Pick GLM 5.2 when the context window is larger, 1.05M against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.

Model AR1 0528DeepSeek71.4/1001 independent benchmarkNo cost per solved taskModel BGLM 5.2Z.ai57.7/1001 independent benchmark$0.86 per solved task
Shared results
49
R1 0528 only
10
GLM 5.2 only
48
Like-for-like categories
8 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, GLM 5.2 57.7 from 1 benchmark.
Widest category
Agentic work, 62.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against GLM 5.2 99.1%, 62.6 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against GLM 5.2 $7.00 per million output tokens, 3.3x apart.
Context window
R1 0528 takes 164K tokens, GLM 5.2 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 5.2Ties
Coding05
Intelligence01
Agentic work01
Reasoning09
Math03
Knowledge04
Web design07
General019
All shared boards049
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LMArena WebDevNot measured1604Only GLM 5.2 measured
AA Coding IndexNot measured68.8Only GLM 5.2 measured
LiveCodeBench (Vals AI)Not measured69.5%Only GLM 5.2 measured
SWE-bench Verified (Vals AI)Not measured82.8%Only GLM 5.2 measured
Vibe Code Bench (Vals AI)Not measured64.0%Only GLM 5.2 measured
Code migration (Vals AI)Not measured37.9%Only GLM 5.2 measured
ProgramBench (Vals AI)Not measured0.5%Only GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured67.8%Only GLM 5.2 measured
SkillsBench (Vals AI)Not measured45.1%Only GLM 5.2 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
SciCodeNot measured51.2%Only GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%50.8%GLM 5.2
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdML41.6%70.1%GLM 5.2
ALE-Bench804.11047.0GLM 5.2
LMArena Text: coding14271487GLM 5.2
LMArena Text: software and IT14351486GLM 5.2
LMArena WebDev: ReactNot measured1617Only GLM 5.2 measured
LMArena WebDev: HTMLNot measured1565Only GLM 5.2 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
AA Intelligence Index13.133.7GLM 5.2
Epoch Capabilities IndexNot measured151.9Only GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
AA Agentic IndexNot measured38.4Only GLM 5.2 measured
Design Arena: agents building web appsNot measured1222Only GLM 5.2 measured
Design Arena: agents building full-stack appsNot measured1241Only GLM 5.2 measured
Design Arena: agents building mobile appsNot measured1160Only GLM 5.2 measured
Design Arena: agents building Android appsNot measured1184Only GLM 5.2 measured
Design Arena: agents building Godot gamesNot measured1142Only GLM 5.2 measured
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1175Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1195Only GLM 5.2 measured
SREBench (Vals AI)Not measured0.0%Only GLM 5.2 measured
Finance Agent (Vals AI)Not measured49.7%Only GLM 5.2 measured
Legal Research Bench (Vals AI)Not measured31.3%Only GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)Not measured61.5%Only GLM 5.2 measured
τ²-Bench Telecom36.5%99.1%GLM 5.2
APEX-AgentsNot measured45.2%Only GLM 5.2 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2Not measured$8,314Only GLM 5.2 measured
Neither model has a result on the other 20 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
Reasoning benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
GPQA DiamondNot measured85.6%Only GLM 5.2 measured
GPQA Diamond (Artificial Analysis run)81.3%89.5%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)15.8%41.1%GLM 5.2
GPQA Diamond (Epoch AI run)76.3%91.9%GLM 5.2
ARC-AGI21.2%77.0%GLM 5.2
ARC-AGI-21.1%22.8%GLM 5.2
SimpleBench40.8%58.8%GLM 5.2
Chess puzzlesNot measured22.0%Only GLM 5.2 measured
Mystery game puzzlesNot measured19.0%Only GLM 5.2 measured
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14151481GLM 5.2
LMArena Text: expert13941487GLM 5.2
LMArena Text: hard prompts in English14221481GLM 5.2
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 5.2
BenchmarkR1 0528GLM 5.2Leader
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%86.4%GLM 5.2
FrontierMath, tiers 1 to 3Not measured59.2%Only GLM 5.2 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.2 measured
ProofBenchNot measured35.0%Only GLM 5.2 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001483GLM 5.2
LMArena Text: mathematical work14151489GLM 5.2
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 5.2
BenchmarkR1 0528GLM 5.2Leader
MMLU ProNot measured86.7%Only GLM 5.2 measured
LegalBench (Vals AI)Not measured84.1%Only GLM 5.2 measured
CorpFin (Vals AI)Not measured66.1%Only GLM 5.2 measured
TaxEval (Vals AI)Not measured73.3%Only GLM 5.2 measured
MedCode (Vals AI)Not measured40.8%Only GLM 5.2 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
SimpleQA VerifiedNot measured34.2%Only GLM 5.2 measured
LMArena Text: science14301489GLM 5.2
LMArena Text: medicine and healthcare14451475GLM 5.2
LMArena Text: legal and government14191486GLM 5.2
LMArena Text: business and finance14021451GLM 5.2
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
Design Arena: websites11491285GLM 5.2
Design Arena: UI components11021296GLM 5.2
Design Arena: game dev10981268GLM 5.2
Design Arena: data viz11821285GLM 5.2
Design Arena: 3D11111288GLM 5.2
Design Arena: all code categories11391288GLM 5.2
Design Arena: SVG10381214GLM 5.2
Design Arena: ASCII artNot measured1190Only GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for R1 0528 and GLM 5.2
BenchmarkR1 0528GLM 5.2Leader
MedScribe (Vals AI)Not measured83.5%Only GLM 5.2 measured
IFBench39.6%73.3%GLM 5.2
Long context reasoning (AA-LCR)55.7%78.3%GLM 5.2
DTBenchNot measured93.6%Only GLM 5.2 measured
LMCANot measured45.8%Only GLM 5.2 measured
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
LMArena Text14281470GLM 5.2
LMArena Text: instruction following13821466GLM 5.2
LMArena Text: creative writing14051463GLM 5.2
LMArena Text: English14371475GLM 5.2
LMArena Text: non-English14121459GLM 5.2
LMArena Text: Chinese14421522GLM 5.2
LMArena Text: German14041470GLM 5.2
LMArena Text: Japanese13911450GLM 5.2
LMArena Text: Korean13601440GLM 5.2
LMArena Text: French14171479GLM 5.2
LMArena Text: Spanish14111478GLM 5.2
LMArena Text: Russian14231468GLM 5.2
LMArena Text: Polish14281456GLM 5.2
LMArena Text: multi-turn14051469GLM 5.2
LMArena Text: longer queries13911480GLM 5.2
LMArena Text: writing and language14021463GLM 5.2
LMArena Text: entertainment and media14001450GLM 5.2
Neither model has a result on the other 21 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 5.2
BenchmarkR1 0528GLM 5.2Leader
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 5.2$0.0035Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
GLM 5.2$0.023Fits in one request

GLM 5.2 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
GLM 5.2$0.077Fits in one request

GLM 5.2 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 5.2
SpecificationDeepSeek R1 0528Z.ai GLM 5.2
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025June 16, 2026
Context window164K1.05M
Input $/Mtok$0.50$0.0341
Output $/Mtok$2.15$7.00
Coding index71.457.7
Independent measurements behind it11
Cost per solved taskNot computable$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 5.2 for coding?

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 57.7 for GLM 5.2. Pick GLM 5.2 when the context window is larger, 1.05M against 164K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.

Which is cheaper, R1 0528 or GLM 5.2?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; GLM 5.2 costs $0.0341 and $7.00. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, R1 0528 or GLM 5.2?

GLM 5.2, with 1.05M tokens against 164K.

How many benchmarks is the R1 0528 vs GLM 5.2 comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and GLM 5.2's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next