GLM 4.6V vs Granite 4.2 8B
GLM 4.6V leads Granite 4.2 8B on the Artificial Analysis Intelligence Index, 11.2 to 11.1, and Granite 4.2 8B costs 4.5x less per output token ($0.20 vs $0.90 per million). Updated October 22, 2026; 24 benchmarks have a result for both.
GLM 4.6V or Granite 4.2 8B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.6V for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: expert, where it leads.
- Shared results
- 24
- GLM 4.6V only
- 14
- Granite 4.2 8B only
- 3
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.6V
GLM 4.6V leads, 11.2 against 11.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Granite 4.2 8B has a AA Agentic Index result, 1.3.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.2 8B
GLM 4.6V $0.0008, Granite 4.2 8B $0.0002 per turn.
List prices
At a glance
- Coding index
- GLM 4.6V not benchmarked, Granite 4.2 8B not benchmarked.
- Widest category
- General, 9.9 points apart on average over 12 shared benchmarks.
- Widest single gap
- LMArena Text: creative writing: GLM 4.6V 1347 against Granite 4.2 8B 1236, 18.6 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against Granite 4.2 8B $0.20 per million output tokens, 4.5x apart.
- Context window
- Both take 131K tokens.
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.
| Category | GLM 4.6V | Bars | Granite 4.2 8B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 2 | ||
| Knowledge | 4 | 0 | ||
| General | 12 | 0 | ||
| All shared boards | 22 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 22.4 | Only Granite 4.2 8B measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| SciCode | Not measured | 31.5% | Only Granite 4.2 8B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | Not measured | Only GLM 4.6V measured |
| LMArena Text: coding | 1389 | 1378 | GLM 4.6V |
| LMArena Text: software and IT | 1391 | 1367 | GLM 4.6V |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 11.1 | GLM 4.6V |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.3 | Only Granite 4.2 8B measured |
| τ²-Bench Telecom | 31.6% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 71.9% | 63.1% | GLM 4.6V |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 9.7% | Granite 4.2 8B |
| LMArena Text: hard prompts | 1367 | 1327 | GLM 4.6V |
| LMArena Text: expert | 1371 | 1386 | Granite 4.2 8B |
| LMArena Text: hard prompts in English | 1391 | 1345 | GLM 4.6V |
| LMArena Vision: diagrams | 1182 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: homework | 1155 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | 1336 | GLM 4.6V |
| LMArena Text: medicine and healthcare | 1389 | 1336 | GLM 4.6V |
| LMArena Text: legal and government | 1393 | 1327 | GLM 4.6V |
| LMArena Text: business and finance | 1359 | 1322 | GLM 4.6V |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| IFBench | 30.1% | Not measured | Only GLM 4.6V measured |
| Long context reasoning (AA-LCR) | 48.7% | 45.0% | GLM 4.6V |
| LMArena Text | 1376 | 1319 | GLM 4.6V |
| LMArena Text: instruction following | 1350 | 1300 | GLM 4.6V |
| LMArena Text: creative writing | 1347 | 1236 | GLM 4.6V |
| LMArena Vision | 1164 | Not measured | Only GLM 4.6V measured |
| LMArena Text: English | 1395 | 1337 | GLM 4.6V |
| LMArena Text: non-English | 1358 | 1301 | GLM 4.6V |
| LMArena Text: Chinese | 1425 | 1367 | GLM 4.6V |
| LMArena Text: Russian | 1339 | 1282 | GLM 4.6V |
| LMArena Text: multi-turn | 1359 | 1301 | GLM 4.6V |
| LMArena Text: longer queries | 1357 | 1325 | GLM 4.6V |
| LMArena Text: writing and language | 1347 | 1269 | GLM 4.6V |
| LMArena Text: entertainment and media | 1343 | 1271 | GLM 4.6V |
| LMArena Vision: English | 1179 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: Chinese | 1217 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: reading text | 1171 | Not measured | Only GLM 4.6V measured |
| LMArena Vision: creative writing | 1113 | Not measured | Only GLM 4.6V measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | Granite 4.2 8B | Leader |
|---|---|---|---|
| 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
Granite 4.2 8B costs less
Repository review
50K input + 3K output tokens
Granite 4.2 8B costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 4.6V | IBM Granite 4.2 8B |
|---|---|---|
| Maker | Z.ai | IBM Granite |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | August 31, 2026 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.30 | $0.08 |
| Output $/Mtok | $0.90 | $0.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than Granite 4.2 8B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.6V for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: expert, where it leads.
Which is cheaper, GLM 4.6V or Granite 4.2 8B?
GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or Granite 4.2 8B?
Neither: both take 131K tokens.
How many benchmarks is the GLM 4.6V vs Granite 4.2 8B comparison based on?
24 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 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.