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GLM 5.3 vs Granite 4.2 8B

GLM 5.3 leads Granite 4.2 8B on the Artificial Analysis Intelligence Index, 44.8 to 11.1, and Granite 4.2 8B costs 22.0x less per output token ($0.20 vs $4.40 per million). Updated October 22, 2026; 27 benchmarks have a result for both.

Replace GLM 5.3 Replace Granite 4.2 8B
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
27
Granite 4.2 8BIBM Granite
Coding
No data
Overall
No data
Output $/M
$0.20
0
vs boards won of 27 shared

GLM 5.3 or Granite 4.2 8B: which to pick

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Granite 4.2 8B has none, so the two cannot be ranked on coding.

Pick Granite 4.2 8B for output tokens 22.0x cheaper ($0.20 vs $4.40 per million).

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BGranite 4.2 8BIBM GraniteNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
27
GLM 5.3 only
73
Granite 4.2 8B only
0
Like-for-like categories
6 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
GLM 5.3 70.6 from 6 benchmarks, Granite 4.2 8B not benchmarked.
Widest category
Agentic work, 51.8 points apart on average over 1 shared benchmark.
Widest single gap
AA Coding Index: GLM 5.3 74.8 against Granite 4.2 8B 22.4, 52.4 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against Granite 4.2 8B $0.20 per million output tokens, 22.0x apart.
Context window
GLM 5.3 takes 1.05M tokens, Granite 4.2 8B 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
CategoryGLM 5.3BarsGranite 4.2 8BTies
Coding40
Intelligence10
Agentic work10
Reasoning50
Knowledge40
General120
All shared boards270
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev1621Not measuredOnly GLM 5.3 measured
AA Coding Index74.822.4GLM 5.3
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 measured
SWE-bench Verified (Vals AI)95.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 4 (Vals AI)38.9%Not measuredOnly GLM 5.3 measured
Vibe Code Bench (Vals AI)78.1%Not measuredOnly GLM 5.3 measured
Code migration (Vals AI)44.2%Not measuredOnly GLM 5.3 measured
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%Not measuredOnly GLM 5.3 measured
SciCode59.0%31.5%GLM 5.3
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4Not measuredOnly GLM 5.3 measured
LMArena Text: coding14971378GLM 5.3
LMArena Text: software and IT14931367GLM 5.3
LMArena WebDev: React1641Not measuredOnly GLM 5.3 measured
LMArena WebDev: HTML1595Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
AA Intelligence Index44.811.1GLM 5.3
Epoch Capabilities Index156.0Not measuredOnly GLM 5.3 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
AA Agentic Index53.11.3GLM 5.3
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1230Not measuredOnly GLM 5.3 measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%Not measuredOnly GLM 5.3 measured
Tax Agent Bench (Vals AI)39.7%Not measuredOnly GLM 5.3 measured
Legal Research Bench (Vals AI)49.0%Not measuredOnly GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%Not measuredOnly GLM 5.3 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
GPQA Diamond88.1%Not measuredOnly GLM 5.3 measured
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)91.7%63.1%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%9.7%GLM 5.3
GPQA Diamond (Epoch AI run)90.9%Not measuredOnly GLM 5.3 measured
Chess puzzles37.0%Not measuredOnly GLM 5.3 measured
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901327GLM 5.3
LMArena Text: expert15141386GLM 5.3
LMArena Text: hard prompts in English14961345GLM 5.3
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
OTIS Mock AIME91.1%Not measuredOnly GLM 5.3 measured
FrontierMath, tiers 1 to 368.8%Not measuredOnly GLM 5.3 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%Not measuredOnly GLM 5.3 measured
LMArena Text: math1495Not measuredOnly GLM 5.3 measured
LMArena Text: mathematical work1497Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
MMLU Pro86.8%Not measuredOnly GLM 5.3 measured
LegalBench (Vals AI)84.8%Not measuredOnly GLM 5.3 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 5.3 measured
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
LMArena Text: science14981336GLM 5.3
LMArena Text: medicine and healthcare14811336GLM 5.3
LMArena Text: legal and government14731327GLM 5.3
LMArena Text: business and finance14591322GLM 5.3
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
Design Arena: websites1295Not measuredOnly GLM 5.3 measured
Design Arena: UI components1314Not measuredOnly GLM 5.3 measured
Design Arena: game dev1311Not measuredOnly GLM 5.3 measured
Design Arena: data viz1260Not measuredOnly GLM 5.3 measured
Design Arena: 3D1332Not measuredOnly GLM 5.3 measured
Design Arena: all code categories1304Not measuredOnly GLM 5.3 measured
Design Arena: SVG1294Not measuredOnly GLM 5.3 measured
Design Arena: ASCII art1208Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
General benchmarks for GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
MedScribe (Vals AI)88.8%Not measuredOnly GLM 5.3 measured
Long context reasoning (AA-LCR)79.7%45.0%GLM 5.3
DTBench87.7%Not measuredOnly GLM 5.3 measured
LMCA55.5%Not measuredOnly GLM 5.3 measured
LMArena Text14741319GLM 5.3
LMArena Text: instruction following14791300GLM 5.3
LMArena Text: creative writing14581236GLM 5.3
LMArena Text: English14821337GLM 5.3
LMArena Text: non-English14591301GLM 5.3
LMArena Text: Chinese15321367GLM 5.3
LMArena Text: German1488Not measuredOnly GLM 5.3 measured
LMArena Text: Japanese1460Not measuredOnly GLM 5.3 measured
LMArena Text: Korean1460Not measuredOnly GLM 5.3 measured
LMArena Text: French1503Not measuredOnly GLM 5.3 measured
LMArena Text: Spanish1462Not measuredOnly GLM 5.3 measured
LMArena Text: Russian14691282GLM 5.3
LMArena Text: Polish1466Not measuredOnly GLM 5.3 measured
LMArena Text: multi-turn14721301GLM 5.3
LMArena Text: longer queries14831325GLM 5.3
LMArena Text: writing and language14691269GLM 5.3
LMArena Text: entertainment and media14471271GLM 5.3
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 GLM 5.3 and Granite 4.2 8B
BenchmarkGLM 5.3Granite 4.2 8BLeader
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

GLM 5.3$0.0036Fits in one request
Granite 4.2 8B$0.0002Fits in one request

Granite 4.2 8B costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request
Granite 4.2 8B$0.0046Fits in one request

Granite 4.2 8B costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request
Granite 4.2 8B$0.018Exceeds the context window

Granite 4.2 8B 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 GLM 5.3 and Granite 4.2 8B
SpecificationZ.ai GLM 5.3IBM Granite 4.2 8B
MakerZ.aiIBM Granite
WeightsOpen weightsOpen weights
ReleasedAugust 18, 2026August 31, 2026
Context window1.05M131K
Input $/Mtok$1.40$0.08
Output $/Mtok$4.40$0.20
Coding index70.6Not benchmarked
Independent measurements behind it60
Cost per solved task$0.36Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than Granite 4.2 8B for coding?

Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; Granite 4.2 8B has none, so the two cannot be ranked on coding. Pick Granite 4.2 8B for output tokens 22.0x cheaper ($0.20 vs $4.40 per million).

Which is cheaper, GLM 5.3 or Granite 4.2 8B?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Granite 4.2 8B costs $0.08 and $0.20. Cost per solved task is known for GLM 5.3 only.

Which has the bigger context window, GLM 5.3 or Granite 4.2 8B?

GLM 5.3, with 1.05M tokens against 131K.

How many benchmarks is the GLM 5.3 vs Granite 4.2 8B comparison based on?

27 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Granite 4.2 8B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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