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R1 0528 vs GLM 4.7

GLM 4.7 leads R1 0528 on the Artificial Analysis Intelligence Index, 22.2 to 13.1, at about the same output price ($2.20 vs $2.18 per million). Updated October 22, 2026; 51 benchmarks have a result for both.

Replace R1 0528 Replace GLM 4.7
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
4
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
47
vs boards won of 51 shared

R1 0528 or GLM 4.7: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; R1 0528 has none, 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 work measured by ALE-Bench or LMArena Text: mathematical work, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
51
R1 0528 only
9
GLM 4.7 only
30
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 4.7 55.1 from 2 benchmarks.
Widest category
Agentic work, 53.8 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against GLM 4.7 95.9%, 59.4 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against GLM 4.7 $2.20 per million output tokens, 1.0x apart.
Context window
R1 0528 takes 164K tokens, GLM 4.7 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 4.7Ties
Coding14
Intelligence01
Agentic work02
Reasoning07
Math14
Knowledge05
Web design07
General217
All shared boards447
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
Coding benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)77.0%89.4%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)15.9%31.8%GLM 4.7
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1399.5R1 0528
LMArena Text: coding14271454GLM 4.7
LMArena Text: software and IT14351453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
AA Intelligence Index13.122.2GLM 4.7
Epoch Capabilities IndexNot measured143.6Only GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1112Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom36.5%95.9%GLM 4.7
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
BrowseComp-Plus16.4%64.7%GLM 4.7
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)81.3%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)15.8%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)76.3%83.3%GLM 4.7
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%47.7%GLM 4.7
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts14151443GLM 4.7
LMArena Text: expert13941424GLM 4.7
LMArena Text: hard prompts in English14221452GLM 4.7
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index14
Math benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index76.095.0GLM 4.7
AIME 2025 (Artificial Analysis run)76.0%95.0%GLM 4.7
OTIS Mock AIME66.4%83.3%GLM 4.7
ProofBenchNot measured6.0%Only GLM 4.7 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001424GLM 4.7
LMArena Text: mathematical work14151411R1 0528
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)84.9%85.6%GLM 4.7
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science14301457GLM 4.7
LMArena Text: medicine and healthcare14451469GLM 4.7
LMArena Text: legal and government14191444GLM 4.7
LMArena Text: business and finance14021429GLM 4.7
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 4.7
BenchmarkR1 0528GLM 4.7Leader
Design Arena: websites11491225GLM 4.7
Design Arena: UI components11001196GLM 4.7
Design Arena: game dev10971188GLM 4.7
Design Arena: data viz11821195GLM 4.7
Design Arena: 3D11101190GLM 4.7
Design Arena: all code categories11381216GLM 4.7
Design Arena: SVG10381142GLM 4.7
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index217
General benchmarks for R1 0528 and GLM 4.7
BenchmarkR1 0528GLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBench39.6%67.9%GLM 4.7
Long context reasoning (AA-LCR)55.7%71.0%GLM 4.7
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text14281435GLM 4.7
LMArena Text: instruction following13821412GLM 4.7
LMArena Text: creative writing14051401R1 0528
LMArena Text: English14371453GLM 4.7
LMArena Text: non-English14121417GLM 4.7
LMArena Text: Chinese14421495GLM 4.7
LMArena Text: German14041426GLM 4.7
LMArena Text: Japanese13911439GLM 4.7
LMArena Text: Korean13601399GLM 4.7
LMArena Text: French14171432GLM 4.7
LMArena Text: Spanish14111434GLM 4.7
LMArena Text: Russian14231423R1 0528
LMArena Text: Polish14281435GLM 4.7
LMArena Text: multi-turn14051446GLM 4.7
LMArena Text: longer queries13911432GLM 4.7
LMArena Text: writing and language14021411GLM 4.7
LMArena Text: entertainment and media14001400GLM 4.7
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 4.7
BenchmarkR1 0528GLM 4.7Leader
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 4.7$0.0017Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
GLM 4.7$0.037Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
GLM 4.7$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 4.7
SpecificationDeepSeek R1 0528Z.ai GLM 4.7
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025December 22, 2025
Context window164K205K
Input $/Mtok$0.50$0.60
Output $/Mtok$2.18$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it02
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; R1 0528 has none, 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 work measured by ALE-Bench or LMArena Text: mathematical work, where it leads.

Which is cheaper, R1 0528 or GLM 4.7?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, R1 0528 or GLM 4.7?

GLM 4.7, with 205K tokens against 164K.

How many benchmarks is the R1 0528 vs GLM 4.7 comparison based on?

51 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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