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GLM 4.6 vs Granite 4.2 8B

GLM 4.6 leads Granite 4.2 8B on the Artificial Analysis Intelligence Index, 18.5 to 11.1, and Granite 4.2 8B costs 11.0x less per output token ($0.20 vs $2.20 per million). Updated October 22, 2026; 25 benchmarks have a result for both.

Replace GLM 4.6 Replace Granite 4.2 8B
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
25
Granite 4.2 8BIBM Granite
Coding
No data
Overall
No data
Output $/M
$0.20
0
vs boards won of 25 shared

GLM 4.6 or Granite 4.2 8B: which to pick

Only GLM 4.6 has a coding score, 8.8 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 11.0x cheaper ($0.20 vs $2.20 per million).

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BGranite 4.2 8BIBM GraniteNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
25
GLM 4.6 only
44
Granite 4.2 8B only
2
Like-for-like categories
5 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 4.6 8.8 from 2 benchmarks, Granite 4.2 8B not benchmarked.
Widest category
General, 20.4 points apart on average over 12 shared benchmarks.
Widest single gap
LMArena Text: creative writing: GLM 4.6 1412 against Granite 4.2 8B 1236, 29.3 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Granite 4.2 8B $0.20 per million output tokens, 11.0x apart.
Context window
GLM 4.6 takes 205K 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 4.6BarsGranite 4.2 8BTies
Coding30
Intelligence10
Reasoning50
Knowledge40
General120
All shared boards250
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
Coding benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev1341Not measuredOnly GLM 4.6 measured
AA Coding Index45.822.4GLM 4.6
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured31.5%Only Granite 4.2 8B measured
Terminal-Bench Hard (Artificial Analysis run)28.8%Not measuredOnly GLM 4.6 measured
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491378GLM 4.6
LMArena Text: software and IT14551367GLM 4.6
LMArena WebDev: HTML1350Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
AA Intelligence Index18.511.1GLM 4.6
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Agentic work benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
AA Agentic IndexNot measured1.3Only Granite 4.2 8B measured
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%Not measuredOnly GLM 4.6 measured
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%63.1%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%9.7%GLM 4.6
LMArena Text: hard prompts14401327GLM 4.6
LMArena Text: expert14321386GLM 4.6
LMArena Text: hard prompts in English14451345GLM 4.6
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math1432Not measuredOnly GLM 4.6 measured
LMArena Text: mathematical work1447Not measuredOnly GLM 4.6 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 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561336GLM 4.6
LMArena Text: medicine and healthcare14551336GLM 4.6
LMArena Text: legal and government14481327GLM 4.6
LMArena Text: business and finance14161322GLM 4.6
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1145Not measuredOnly GLM 4.6 measured
Design Arena: game dev1150Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1128Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.6 measured
Design Arena: SVG1108Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
General benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 4.2 8BLeader
IFBench43.4%Not measuredOnly GLM 4.6 measured
Long context reasoning (AA-LCR)54.0%45.0%GLM 4.6
LMArena Text14401319GLM 4.6
LMArena Text: instruction following14101300GLM 4.6
LMArena Text: creative writing14121236GLM 4.6
LMArena Text: English14491337GLM 4.6
LMArena Text: non-English14261301GLM 4.6
LMArena Text: Chinese14991367GLM 4.6
LMArena Text: German1447Not measuredOnly GLM 4.6 measured
LMArena Text: Japanese1393Not measuredOnly GLM 4.6 measured
LMArena Text: Korean1400Not measuredOnly GLM 4.6 measured
LMArena Text: French1459Not measuredOnly GLM 4.6 measured
LMArena Text: Spanish1436Not measuredOnly GLM 4.6 measured
LMArena Text: Russian14181282GLM 4.6
LMArena Text: Polish1447Not measuredOnly GLM 4.6 measured
LMArena Text: multi-turn14271301GLM 4.6
LMArena Text: longer queries14221325GLM 4.6
LMArena Text: writing and language14151269GLM 4.6
LMArena Text: entertainment and media14091271GLM 4.6
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Granite 4.2 8B
BenchmarkGLM 4.6Granite 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 4.6$0.0017Fits 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 4.6$0.037Fits 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 4.6$0.142Exceeds the context window
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 4.6 and Granite 4.2 8B
SpecificationZ.ai GLM 4.6IBM Granite 4.2 8B
MakerZ.aiIBM Granite
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025August 31, 2026
Context window205K131K
Input $/Mtok$0.60$0.08
Output $/Mtok$2.20$0.20
Coding index8.8Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Granite 4.2 8B for coding?

Only GLM 4.6 has a coding score, 8.8 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 11.0x cheaper ($0.20 vs $2.20 per million).

Which is cheaper, GLM 4.6 or Granite 4.2 8B?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Granite 4.2 8B costs $0.08 and $0.20. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or Granite 4.2 8B?

GLM 4.6, with 205K tokens against 131K.

How many benchmarks is the GLM 4.6 vs Granite 4.2 8B comparison based on?

25 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 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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