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R1 0528 vs GLM 5.1

Updated October 21, 2026. 47 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 5.1
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
0
GLM 5.1Z.ai
Coding
44.0
Overall
26.1
Output $/M
$4.40
47
vs boards won of 47 shared

Decision reading

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 44.0 for GLM 5.1.

Pick GLM 5.1 when the context window is larger, 205K 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 5.1Z.ai44.0/1001 independent benchmark$0.60 per solved task
Shared results
47
R1 0528 only
12
GLM 5.1 only
42
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 5.1 44.0 from 1 benchmark.
Widest category
Agentic work, 61.1 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against GLM 5.1 97.7%, 61.1 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against GLM 5.1 $4.40 per million output tokens, 2.0x apart.
Context window
R1 0528 takes 164K tokens, GLM 5.1 205K.

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 5.1Ties
Coding05
Intelligence01
Agentic work01
Reasoning07
Math03
Knowledge04
Web design07
General019
All shared boards047
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LMArena WebDevNot measured1508Only GLM 5.1 measured
AA Coding IndexNot measured55.8Only GLM 5.1 measured
LiveCodeBench (Vals AI)Not measured81.4%Only GLM 5.1 measured
SWE-bench Verified (Vals AI)Not measured76.4%Only GLM 5.1 measured
Vibe Code Bench (Vals AI)Not measured31.5%Only GLM 5.1 measured
Code migration (Vals AI)Not measured25.8%Only GLM 5.1 measured
ProgramBench (Vals AI)Not measured0.0%Only GLM 5.1 measured
Terminal-Bench 2.1 (Vals AI)Not measured56.9%Only GLM 5.1 measured
LiveCodeBench (Artificial Analysis run)77.0%Not measuredOnly R1 0528 measured
SciCodeNot measured44.8%Only GLM 5.1 measured
Terminal-Bench Hard (Artificial Analysis run)15.9%43.2%GLM 5.1
WeirdML41.6%57.1%GLM 5.1
ALE-Bench804.1887.1GLM 5.1
LMArena Text: coding14271485GLM 5.1
LMArena Text: software and IT14351484GLM 5.1
LMArena WebDev: ReactNot measured1505Only GLM 5.1 measured
LMArena WebDev: HTMLNot measured1508Only GLM 5.1 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
AA Intelligence Index13.126.1GLM 5.1
Epoch Capabilities IndexNot measured149.8Only GLM 5.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
AA Agentic IndexNot measured23.9Only GLM 5.1 measured
Design Arena: agents building web appsNot measured1175Only GLM 5.1 measured
Design Arena: agents building full-stack appsNot measured1167Only GLM 5.1 measured
Design Arena: agents building mobile appsNot measured1162Only GLM 5.1 measured
Design Arena: agents building Android appsNot measured1186Only GLM 5.1 measured
Design Arena: agents building Godot gamesNot measured1053Only GLM 5.1 measured
Design Arena: agents building gamesNot measured1164Only GLM 5.1 measured
Design Arena: HTML slidesNot measured1170Only GLM 5.1 measured
Design Arena: PowerPoint slidesNot measured1189Only GLM 5.1 measured
Design Arena: spreadsheetsNot measured1315Only GLM 5.1 measured
Finance Agent (Vals AI)Not measured44.8%Only GLM 5.1 measured
Legal Research Bench (Vals AI)Not measured27.9%Only GLM 5.1 measured
τ²-Bench Telecom36.5%97.7%GLM 5.1
APEX-AgentsNot measured40.9%Only GLM 5.1 measured
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$5,634Only GLM 5.1 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
GPQA DiamondNot measured84.5%Only GLM 5.1 measured
GPQA Diamond (Artificial Analysis run)81.3%86.8%GLM 5.1
Humanity's Last Exam (Artificial Analysis run)15.8%30.1%GLM 5.1
GPQA Diamond (Epoch AI run)76.3%89.9%GLM 5.1
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%55.1%GLM 5.1
Chess puzzlesNot measured20.0%Only GLM 5.1 measured
LMArena Text: hard prompts14151473GLM 5.1
LMArena Text: expert13941478GLM 5.1
LMArena Text: hard prompts in English14221482GLM 5.1
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
AIMENot measured91.9%Only GLM 5.1 measured
AA Math Index76.0Not measuredOnly R1 0528 measured
AIME 2025 (Artificial Analysis run)76.0%Not measuredOnly R1 0528 measured
OTIS Mock AIME66.4%93.3%GLM 5.1
FrontierMath, tiers 1 to 3Not measured36.8%Only GLM 5.1 measured
ProofBenchNot measured22.2%Only GLM 5.1 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001473GLM 5.1
LMArena Text: mathematical work14151470GLM 5.1
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 5.1
BenchmarkR1 0528GLM 5.1Leader
MMLU ProNot measured86.9%Only GLM 5.1 measured
LegalBench (Vals AI)Not measured84.4%Only GLM 5.1 measured
CaseLaw (Vals AI)Not measured51.5%Only GLM 5.1 measured
CorpFin (Vals AI)Not measured64.5%Only GLM 5.1 measured
TaxEval (Vals AI)Not measured71.2%Only GLM 5.1 measured
MedCode (Vals AI)Not measured41.6%Only GLM 5.1 measured
MMLU Pro (Artificial Analysis run)84.9%Not measuredOnly R1 0528 measured
SimpleQA VerifiedNot measured34.0%Only GLM 5.1 measured
LMArena Text: science14301478GLM 5.1
LMArena Text: medicine and healthcare14451474GLM 5.1
LMArena Text: legal and government14191465GLM 5.1
LMArena Text: business and finance14021452GLM 5.1
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for R1 0528 and GLM 5.1
BenchmarkR1 0528GLM 5.1Leader
Design Arena: websites11491290GLM 5.1
Design Arena: UI components11021292GLM 5.1
Design Arena: game dev10981265GLM 5.1
Design Arena: data viz11821366GLM 5.1
Design Arena: 3D11111336GLM 5.1
Design Arena: all code categories11391268GLM 5.1
Design Arena: SVG10381217GLM 5.1
Design Arena: ASCII artNot measured1142Only GLM 5.1 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 5.1
BenchmarkR1 0528GLM 5.1Leader
Public Benefits Bench (Vals AI)Not measured61.8%Only GLM 5.1 measured
MedScribe (Vals AI)Not measured72.3%Only GLM 5.1 measured
IFBench39.6%76.3%GLM 5.1
Long context reasoning (AA-LCR)55.7%73.7%GLM 5.1
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
ExploitBenchNot measured18.1%Only GLM 5.1 measured
LMArena Text14281463GLM 5.1
LMArena Text: instruction following13821453GLM 5.1
LMArena Text: creative writing14051453GLM 5.1
LMArena Text: English14371476GLM 5.1
LMArena Text: non-English14121448GLM 5.1
LMArena Text: Chinese14421515GLM 5.1
LMArena Text: German14041467GLM 5.1
LMArena Text: Japanese13911435GLM 5.1
LMArena Text: Korean13601424GLM 5.1
LMArena Text: French14171470GLM 5.1
LMArena Text: Spanish14111468GLM 5.1
LMArena Text: Russian14231454GLM 5.1
LMArena Text: Polish14281449GLM 5.1
LMArena Text: multi-turn14051473GLM 5.1
LMArena Text: longer queries13911467GLM 5.1
LMArena Text: writing and language14021454GLM 5.1
LMArena Text: entertainment and media14001442GLM 5.1
Neither model has a result on the other 22 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 5.1
BenchmarkR1 0528GLM 5.1Leader
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 5.1$0.0036Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
GLM 5.1$0.083Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
GLM 5.1$0.324Exceeds 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 5.1
SpecificationDeepSeek R1 0528Z.ai GLM 5.1
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025April 7, 2026
Context window164K205K
Input $/Mtok$0.50$1.40
Output $/Mtok$2.15$4.40
Coding index71.444.0
Independent measurements behind it11
Cost per solved taskNot computable$0.60
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 5.1 for coding?

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 44.0 for GLM 5.1. Pick GLM 5.1 when the context window is larger, 205K 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 5.1?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; GLM 5.1 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.1 only.

Which has the bigger context window, R1 0528 or GLM 5.1?

GLM 5.1, with 205K tokens against 164K.

How many benchmarks is the R1 0528 vs GLM 5.1 comparison based on?

47 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and GLM 5.1's on 1. Where only one model has a result, the page says so instead of naming a winner.

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