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Nova Pro 1.0 vs Qwen3.5-27B

Qwen3.5-27B leads Nova Pro 1.0 on the Artificial Analysis Intelligence Index, 22.9 to 7.0, and Qwen3.5-27B costs 2.1x less per output token ($1.56 vs $3.20 per million). Updated October 21, 2026; 36 benchmarks have a result for both.

Replace Nova Pro 1.0 Replace Qwen3.5-27B
Nova Pro 1.0Amazon
Coding
No data
Overall
No data
Output $/M
$3.20
0
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
36
vs boards won of 36 shared

Nova Pro 1.0 or Qwen3.5-27B: which to pick

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

Pick Nova Pro 1.0 for a larger context window (300K against 262K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model ANova Pro 1.0AmazonNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
36
Nova Pro 1.0 only
6
Qwen3.5-27B only
17
Like-for-like categories
7 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
Nova Pro 1.0 not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 79.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Nova Pro 1.0 14.0% against Qwen3.5-27B 93.9%, 79.8 points apart on a 0 to 100 scale.
Output price
Nova Pro 1.0 $3.20 against Qwen3.5-27B $1.56 per million output tokens, 2.1x apart.
Context window
Nova Pro 1.0 takes 300K 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
CategoryNova Pro 1.0BarsQwen3.5-27BTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General020
All shared boards036
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
LiveCodeBench (Artificial Analysis run)23.3%Not measuredOnly Nova Pro 1.0 measured
Terminal-Bench Hard (Artificial Analysis run)6.1%32.6%Qwen3.5-27B
WeirdMLNot measured39.5%Only Qwen3.5-27B measured
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding12701427Qwen3.5-27B
LMArena Text: software and IT12711431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
AA Intelligence Index7.022.9Qwen3.5-27B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
τ²-Bench Telecom14.0%93.9%Qwen3.5-27B
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 Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
GPQA Diamond (Artificial Analysis run)49.9%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)3.2%23.9%Qwen3.5-27B
LMArena Text: hard prompts12461415Qwen3.5-27B
LMArena Text: expert12111427Qwen3.5-27B
LMArena Text: hard prompts in English12591429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
AA Math Index7.0Not measuredOnly Nova Pro 1.0 measured
AIME 2025 (Artificial Analysis run)7.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: math12521429Qwen3.5-27B
LMArena Text: mathematical work12611429Qwen3.5-27B
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
MMLU Pro (Artificial Analysis run)69.1%Not measuredOnly Nova Pro 1.0 measured
MMLU82.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: science12551437Qwen3.5-27B
LMArena Text: medicine and healthcare12601425Qwen3.5-27B
LMArena Text: legal and government12811405Qwen3.5-27B
LMArena Text: business and finance12291399Qwen3.5-27B
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
Design Arena: websites796Not measuredOnly Nova Pro 1.0 measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
IFBench38.1%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)21.0%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
LMArena Text12591409Qwen3.5-27B
LMArena Text: instruction following12351393Qwen3.5-27B
LMArena Text: creative writing12121362Qwen3.5-27B
LMArena Vision9801241Qwen3.5-27B
LMArena Text: English12761425Qwen3.5-27B
LMArena Text: non-English12331390Qwen3.5-27B
LMArena Text: Chinese12441478Qwen3.5-27B
LMArena Text: German12431392Qwen3.5-27B
LMArena Text: Japanese12001346Qwen3.5-27B
LMArena Text: Korean12031359Qwen3.5-27B
LMArena Text: French12711410Qwen3.5-27B
LMArena Text: Spanish11821407Qwen3.5-27B
LMArena Text: Russian12401390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn12461410Qwen3.5-27B
LMArena Text: longer queries12551413Qwen3.5-27B
LMArena Text: writing and language12471380Qwen3.5-27B
LMArena Text: entertainment and media12101363Qwen3.5-27B
LMArena Vision: English9871246Qwen3.5-27B
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 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Nova Pro 1.0 and Qwen3.5-27B
BenchmarkNova Pro 1.0Qwen3.5-27BLeader
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

Nova Pro 1.0$0.0024Fits in one request
Qwen3.5-27B$0.0010Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

Nova Pro 1.0$0.050Fits in one request
Qwen3.5-27B$0.014Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

Nova Pro 1.0$0.192Fits in one request
Qwen3.5-27B$0.055Fits in one request

Qwen3.5-27B 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 Nova Pro 1.0 and Qwen3.5-27B
SpecificationAmazon Nova Pro 1.0Qwen3.5-27B
MakerAmazonQwen
WeightsNo public weights listedOpen weights
ReleasedDecember 5, 2024February 25, 2026
Context window300K262K
Input $/Mtok$0.80$0.195
Output $/Mtok$3.20$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Nova Pro 1.0 better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Pro 1.0 for a larger context window (300K against 262K tokens) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Nova Pro 1.0 or Qwen3.5-27B?

Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 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, Nova Pro 1.0 or Qwen3.5-27B?

Nova Pro 1.0, with 300K tokens against 262K.

How many benchmarks is the Nova Pro 1.0 vs Qwen3.5-27B comparison based on?

36 benchmarks have a result for both models as of October 21, 2026. Nova Pro 1.0'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.

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