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R1 0528 vs GLM 4.6

GLM 4.6 leads R1 0528 on the Artificial Analysis Intelligence Index, 18.5 to 13.1, at about the same output price ($2.20 vs $2.18 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace R1 0528 Replace GLM 4.6
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
8
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
40
vs boards won of 48 shared

R1 0528 or GLM 4.6: which to pick

Only GLM 4.6 has a coding score, 8.8 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 LiveCodeBench (Artificial Analysis run) or ALE-Bench, where it leads.

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved task
Shared results
48
R1 0528 only
12
GLM 4.6 only
21
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 4.6 8.8 from 2 benchmarks.
Widest category
Agentic work, 36.1 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: R1 0528 36.5% against GLM 4.6 76.9%, 40.4 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against GLM 4.6 $2.20 per million output tokens, 1.0x apart.
Context window
R1 0528 takes 164K tokens, GLM 4.6 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 4.6Ties
Coding23
Intelligence01
Agentic work02
Reasoning23
Math04
Knowledge14
Web design16
General217
All shared boards840
CodingLike-for-likeSix of these boards feed the Vibe Coding Index23
Coding benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
SWE-bench VerifiedNot measured68.2%Only GLM 4.6 measured
LMArena WebDevNot measured1341Only GLM 4.6 measured
AA Coding IndexNot measured45.8Only GLM 4.6 measured
LiveCodeBench (Vals AI)Not measured81.0%Only GLM 4.6 measured
Vibe Code Bench (Vals AI)Not measured3.1%Only GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)77.0%69.5%R1 0528
Terminal-Bench Hard (Artificial Analysis run)15.9%28.8%GLM 4.6
Terminal-BenchNot measured24.5%Only GLM 4.6 measured
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1340.8R1 0528
LMArena Text: coding14271449GLM 4.6
LMArena Text: software and IT14351455GLM 4.6
LMArena WebDev: HTMLNot measured1350Only GLM 4.6 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 4.6
BenchmarkR1 0528GLM 4.6Leader
AA Intelligence Index13.118.5GLM 4.6
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
Design Arena: agents building full-stack appsNot measured1029Only GLM 4.6 measured
Design Arena: agents building mobile appsNot measured1096Only GLM 4.6 measured
Design Arena: agents building Android appsNot measured1084Only GLM 4.6 measured
Design Arena: agents building Godot gamesNot measured1180Only GLM 4.6 measured
τ²-Bench Telecom36.5%76.9%GLM 4.6
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured72.4%Only GLM 4.6 measured
BrowseComp-Plus16.4%48.2%GLM 4.6
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
Reasoning benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
GPQA DiamondNot measured74.5%Only GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)81.3%78.0%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%14.5%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 prompts14151440GLM 4.6
LMArena Text: expert13941432GLM 4.6
LMArena Text: hard prompts in English14221445GLM 4.6
Kagi LLM BenchmarkNot measured45.7%Only GLM 4.6 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
AIMENot measured92.7%Only GLM 4.6 measured
MGSM (Vals AI)Not measured89.8%Only GLM 4.6 measured
AA Math Index76.086.0GLM 4.6
AIME 2025 (Artificial Analysis run)76.0%86.0%GLM 4.6
OTIS Mock AIME66.4%Not measuredOnly R1 0528 measured
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001432GLM 4.6
LMArena Text: mathematical work14151447GLM 4.6
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
Knowledge benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
MMLU ProNot measured82.2%Only GLM 4.6 measured
LegalBench (Vals AI)Not measured79.6%Only GLM 4.6 measured
CorpFin (Vals AI)Not measured56.8%Only GLM 4.6 measured
TaxEval (Vals AI)Not measured66.2%Only GLM 4.6 measured
MedQA (Vals AI)Not measured92.2%Only GLM 4.6 measured
MMLU Pro (Artificial Analysis run)84.9%82.9%R1 0528
LMArena Text: science14301456GLM 4.6
LMArena Text: medicine and healthcare14451455GLM 4.6
LMArena Text: legal and government14191448GLM 4.6
LMArena Text: business and finance14021416GLM 4.6
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 4.6
BenchmarkR1 0528GLM 4.6Leader
Design Arena: websites11491174GLM 4.6
Design Arena: UI components11001145GLM 4.6
Design Arena: game dev10971150GLM 4.6
Design Arena: data viz11821161R1 0528
Design Arena: 3D11101128GLM 4.6
Design Arena: all code categories11381165GLM 4.6
Design Arena: SVG10381108GLM 4.6
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index217
General benchmarks for R1 0528 and GLM 4.6
BenchmarkR1 0528GLM 4.6Leader
IFBench39.6%43.4%GLM 4.6
Long context reasoning (AA-LCR)55.7%54.0%R1 0528
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text14281440GLM 4.6
LMArena Text: instruction following13821410GLM 4.6
LMArena Text: creative writing14051412GLM 4.6
LMArena Text: English14371449GLM 4.6
LMArena Text: non-English14121426GLM 4.6
LMArena Text: Chinese14421499GLM 4.6
LMArena Text: German14041447GLM 4.6
LMArena Text: Japanese13911393GLM 4.6
LMArena Text: Korean13601400GLM 4.6
LMArena Text: French14171459GLM 4.6
LMArena Text: Spanish14111436GLM 4.6
LMArena Text: Russian14231418R1 0528
LMArena Text: Polish14281447GLM 4.6
LMArena Text: multi-turn14051427GLM 4.6
LMArena Text: longer queries13911422GLM 4.6
LMArena Text: writing and language14021415GLM 4.6
LMArena Text: entertainment and media14001409GLM 4.6
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 4.6
BenchmarkR1 0528GLM 4.6Leader
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 4.6$0.0017Fits in one request

R1 0528 costs less

Repository review

50K input + 3K output tokens

R1 0528$0.032Fits in one request
GLM 4.6$0.037Fits in one request

R1 0528 costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.122Exceeds the context window
GLM 4.6$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 4.6
SpecificationDeepSeek R1 0528Z.ai GLM 4.6
MakerDeepSeekZ.ai
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025September 30, 2025
Context window164K205K
Input $/Mtok$0.50$0.60
Output $/Mtok$2.18$2.20
Coding indexNot benchmarked8.8
Independent measurements behind it02
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than GLM 4.6 for coding?

Only GLM 4.6 has a coding score, 8.8 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 LiveCodeBench (Artificial Analysis run) or ALE-Bench, where it leads.

Which is cheaper, R1 0528 or GLM 4.6?

R1 0528 costs $0.50 per million input tokens and $2.18 per million output tokens; GLM 4.6 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 4.6?

GLM 4.6, with 205K tokens against 164K.

How many benchmarks is the R1 0528 vs GLM 4.6 comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. R1 0528's coding index rests on 0 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.

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