Enter opens the highlighted result. Esc closes.

Granite 4.2 8B vs Qwen3.5-27B

Qwen3.5-27B leads Granite 4.2 8B on the Artificial Analysis Intelligence Index, 22.9 to 11.1, and Granite 4.2 8B costs 7.8x less per output token ($0.20 vs $1.56 per million). Updated October 23, 2026; 24 benchmarks have a result for both.

Replace Granite 4.2 8B Replace Qwen3.5-27B
Granite 4.2 8BIBM Granite
Coding
No data
Overall
No data
Output $/M
$0.20
0
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
24
vs boards won of 24 shared

Granite 4.2 8B or Qwen3.5-27B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Granite 4.2 8B for output tokens 7.8x cheaper ($0.20 vs $1.56 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AGranite 4.2 8BIBM GraniteNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
24
Granite 4.2 8B only
3
Qwen3.5-27B only
30
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
Granite 4.2 8B not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
General, 18.2 points apart on average over 12 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): Granite 4.2 8B 45.0% against Qwen3.5-27B 77.7%, 32.7 points apart on a 0 to 100 scale.
Output price
Granite 4.2 8B $0.20 against Qwen3.5-27B $1.56 per million output tokens, 7.8x apart.
Context window
Granite 4.2 8B takes 131K tokens, Qwen3.5-27B 262K.

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
CategoryGranite 4.2 8BBarsQwen3.5-27BTies
Coding02
Intelligence01
Reasoning05
Knowledge04
General012
All shared boards024
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
LMArena WebDevNot measured1359Only Qwen3.5-27B measured
AA Coding Index22.4Not measuredOnly Granite 4.2 8B measured
SciCode31.5%Not measuredOnly Granite 4.2 8B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured32.6%Only Qwen3.5-27B measured
WeirdMLNot measured39.5%Only Qwen3.5-27B measured
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding13781427Qwen3.5-27B
LMArena Text: software and IT13661431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1387Only Qwen3.5-27B measured
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
AA Intelligence Index11.122.9Qwen3.5-27B
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
AA Agentic Index1.3Not measuredOnly Granite 4.2 8B measured
τ²-Bench TelecomNot measured93.9%Only Qwen3.5-27B measured
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
GPQA Diamond (Artificial Analysis run)63.1%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)9.7%23.9%Qwen3.5-27B
LMArena Text: hard prompts13271414Qwen3.5-27B
LMArena Text: expert13861428Qwen3.5-27B
LMArena Text: hard prompts in English13441428Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
LMArena Text: mathNot measured1429Only Qwen3.5-27B measured
LMArena Text: mathematical workNot measured1429Only Qwen3.5-27B measured
MathArenaNot measured15.8%Only Qwen3.5-27B measured
Neither model has a result on the other 11 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
LMArena Text: science13351437Qwen3.5-27B
LMArena Text: medicine and healthcare13321425Qwen3.5-27B
LMArena Text: legal and government13271405Qwen3.5-27B
LMArena Text: business and finance13221399Qwen3.5-27B
Neither model has a result on the other 16 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index012
General benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
IFBenchNot measured75.6%Only Qwen3.5-27B measured
Long context reasoning (AA-LCR)45.0%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
LMArena Text13191409Qwen3.5-27B
LMArena Text: instruction following13011393Qwen3.5-27B
LMArena Text: creative writing12351363Qwen3.5-27B
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English13371425Qwen3.5-27B
LMArena Text: non-English13001390Qwen3.5-27B
LMArena Text: Chinese13651478Qwen3.5-27B
LMArena Text: GermanNot measured1393Only Qwen3.5-27B measured
LMArena Text: JapaneseNot measured1346Only Qwen3.5-27B measured
LMArena Text: KoreanNot measured1359Only Qwen3.5-27B measured
LMArena Text: FrenchNot measured1410Only Qwen3.5-27B measured
LMArena Text: SpanishNot measured1407Only Qwen3.5-27B measured
LMArena Text: Russian12801390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn13001410Qwen3.5-27B
LMArena Text: longer queries13241413Qwen3.5-27B
LMArena Text: writing and language12691381Qwen3.5-27B
LMArena Text: entertainment and media12711363Qwen3.5-27B
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Granite 4.2 8B and Qwen3.5-27B
BenchmarkGranite 4.2 8BQwen3.5-27BLeader
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$0.0002Fits in one request
Qwen3.5-27B$0.0010Fits in one request

Granite 4.2 8B costs less

Repository review

50K input + 3K output tokens

Granite 4.2 8B$0.0046Fits in one request
Qwen3.5-27B$0.014Fits in one request

Granite 4.2 8B costs less

Agent loop

200K input + 10K output tokens

Granite 4.2 8B$0.018Exceeds the context window
Qwen3.5-27B$0.055Fits in one request

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 Granite 4.2 8B and Qwen3.5-27B
SpecificationIBM Granite 4.2 8BQwen3.5-27B
MakerIBM GraniteQwen
WeightsOpen weightsOpen weights
ReleasedAugust 31, 2026February 25, 2026
Context window131K262K
Input $/Mtok$0.08$0.195
Output $/Mtok$0.20$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Granite 4.2 8B better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Granite 4.2 8B for output tokens 7.8x cheaper ($0.20 vs $1.56 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, Granite 4.2 8B or Qwen3.5-27B?

Granite 4.2 8B costs $0.08 per million input tokens and $0.20 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.

Which has the bigger context window, Granite 4.2 8B or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 131K.

How many benchmarks is the Granite 4.2 8B vs Qwen3.5-27B comparison based on?

24 benchmarks have a result for both models as of October 23, 2026. Granite 4.2 8B's coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next