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R1 0528 vs GLM 4.5

R1 0528 leads GLM 4.5 on the Artificial Analysis Intelligence Index, 13.1 to 12.8, at about the same output price ($2.18 vs $2.20 per million). Updated October 21, 2026; 49 benchmarks have a result for both.

Replace R1 0528 Replace GLM 4.5
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
16
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
33
vs boards won of 49 shared

R1 0528 or GLM 4.5: which to pick

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

Pick R1 0528 for output tokens 1.0x cheaper ($2.18 vs $2.20 per million) and a larger context window (164K against 131K tokens).

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
49
R1 0528 only
11
GLM 4.5 only
12
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, GLM 4.5 not benchmarked.
Widest category
Agentic work, 6.4 points apart on average over 1 shared benchmark.
Widest single gap
ALE-Bench: R1 0528 804.1 against GLM 4.5 344.8, 15.3 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against GLM 4.5 $2.20 per million output tokens, 1.0x apart.
Context window
R1 0528 takes 164K tokens, GLM 4.5 131K.

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 4.5Ties
Coding33
Intelligence10
Agentic work01
Reasoning23
Math22
Knowledge14
Web design16
General614
All shared boards1633
CodingLike-for-likeSix of these boards feed the Vibe Coding Index33
Coding benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
SWE-bench VerifiedNot measured64.2%Only GLM 4.5 measured
LiveCodeBench (Vals AI)Not measured67.5%Only GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)77.0%73.8%R1 0528
Terminal-Bench Hard (Artificial Analysis run)15.9%22.0%GLM 4.5
WeirdML41.6%40.6%R1 0528
ALE-Bench804.1344.8R1 0528
LMArena Text: coding14271434GLM 4.5
LMArena Text: software and IT14351440GLM 4.5
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
AA Intelligence Index13.112.8R1 0528
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 GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
τ²-Bench Telecom36.5%43.0%GLM 4.5
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 index23
Reasoning benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
GPQA DiamondNot measured72.2%Only GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)81.3%78.2%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%13.0%R1 0528
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 prompts14151429GLM 4.5
LMArena Text: expert13941433GLM 4.5
LMArena Text: hard prompts in English14221429GLM 4.5
Kagi LLM BenchmarkNot measured57.9%Only GLM 4.5 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index22
Math benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
AIMENot measured86.7%Only GLM 4.5 measured
MATH 500 (Vals AI)Not measured94.0%Only GLM 4.5 measured
MGSM (Vals AI)Not measured90.8%Only GLM 4.5 measured
AA Math Index76.073.7R1 0528
AIME 2025 (Artificial Analysis run)76.0%73.7%R1 0528
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001427GLM 4.5
LMArena Text: mathematical work14151430GLM 4.5
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
Knowledge benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
MMLU ProNot measured81.2%Only GLM 4.5 measured
LegalBench (Vals AI)Not measured75.6%Only GLM 4.5 measured
CorpFin (Vals AI)Not measured61.0%Only GLM 4.5 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 4.5 measured
MedQA (Vals AI)Not measured90.0%Only GLM 4.5 measured
MMLU Pro (Artificial Analysis run)84.9%83.5%R1 0528
LMArena Text: science14301444GLM 4.5
LMArena Text: medicine and healthcare14451455GLM 4.5
LMArena Text: legal and government14191438GLM 4.5
LMArena Text: business and finance14021420GLM 4.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
Design Arena: websites11491170GLM 4.5
Design Arena: UI components11031144GLM 4.5
Design Arena: game dev10971145GLM 4.5
Design Arena: data viz11821159R1 0528
Design Arena: 3D11111170GLM 4.5
Design Arena: all code categories11391165GLM 4.5
Design Arena: SVG10391096GLM 4.5
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index614
General benchmarks for R1 0528 and GLM 4.5
BenchmarkR1 0528GLM 4.5Leader
IFBench39.6%44.1%GLM 4.5
Long context reasoning (AA-LCR)55.7%52.7%R1 0528
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%73.4%R1 0528
LMArena Text14281430GLM 4.5
LMArena Text: instruction following13821404GLM 4.5
LMArena Text: creative writing14051395R1 0528
LMArena Text: English14371437R1 0528
LMArena Text: non-English14121417GLM 4.5
LMArena Text: Chinese14421465GLM 4.5
LMArena Text: German14041407GLM 4.5
LMArena Text: Japanese13911415GLM 4.5
LMArena Text: Korean13601380GLM 4.5
LMArena Text: French14171418GLM 4.5
LMArena Text: Spanish14111454GLM 4.5
LMArena Text: Russian14231413R1 0528
LMArena Text: Polish14281422R1 0528
LMArena Text: multi-turn14051415GLM 4.5
LMArena Text: longer queries13911412GLM 4.5
LMArena Text: writing and language14021403GLM 4.5
LMArena Text: entertainment and media14001404GLM 4.5
Neither model has a result on the other 25 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 4.5
BenchmarkR1 0528GLM 4.5Leader
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 4.5$0.0017Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
GLM 4.5$0.037Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
GLM 4.5$0.142Exceeds the context window

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 4.5
SpecificationDeepSeek R1 0528Z.ai GLM 4.5
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025July 25, 2025
Context window164K131K
Input $/Mtok$0.50$0.60
Output $/Mtok$2.18$2.20
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 4.5 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick R1 0528 for output tokens 1.0x cheaper ($2.18 vs $2.20 per million) and a larger context window (164K against 131K tokens).

Which is cheaper, R1 0528 or GLM 4.5?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; GLM 4.5 costs $0.60 and $2.20. Cost per solved task is known for neither of them.

Which has the bigger context window, R1 0528 or GLM 4.5?

R1 0528, with 164K tokens against 131K.

How many benchmarks is the R1 0528 vs GLM 4.5 comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 0 independent benchmarks and GLM 4.5's on 0. Where only one model has a result, the page says so instead of naming a winner.

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