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Nova Premier 1.0 vs Qwen3.7 Max

Qwen3.7 Max leads Nova Premier 1.0 on the Artificial Analysis Intelligence Index, 29.5 to 9.2, and Qwen3.7 Max costs 2.8x less per output token ($4.425 vs $12.50 per million). Updated October 22, 2026; 9 benchmarks have a result for both.

Nova Premier 1.0Amazon
Coding
No data
Overall
No data
Output $/M
$12.50
0
Qwen3.7 MaxQwen
Coding
54.1
Overall
61.7
Output $/M
$4.425
9
vs boards won of 9 shared

Nova Premier 1.0 or Qwen3.7 Max: which to pick

Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Nova Premier 1.0 has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Shared results
9
Nova Premier 1.0 only
5
Qwen3.7 Max only
51
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
Nova Premier 1.0 not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
Widest category
Agentic work, 56.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Nova Premier 1.0 38.3% against Qwen3.7 Max 94.7%, 56.4 points apart on a 0 to 100 scale.
Output price
Nova Premier 1.0 $12.50 against Qwen3.7 Max $4.425 per million output tokens, 2.8x apart.
Context window
Both take 1M 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.

Boards won, where both have a result
CategoryNova Premier 1.0BarsQwen3.7 MaxTies
Coding02
Intelligence01
Agentic work01
Reasoning02
Web design01
General02
All shared boards09
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
LMArena WebDevNot measured1515Only Qwen3.7 Max measured
AA Coding IndexNot measured66.0Only Qwen3.7 Max measured
LiveCodeBench (Vals AI)Not measured87.1%Only Qwen3.7 Max measured
SWE-bench Verified (Vals AI)Not measured68.8%Only Qwen3.7 Max measured
Vibe Code Bench (Vals AI)Not measured47.7%Only Qwen3.7 Max measured
Code migration (Vals AI)Not measured13.1%Only Qwen3.7 Max measured
Terminal-Bench 2.1 (Vals AI)Not measured61.0%Only Qwen3.7 Max measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly Nova Premier 1.0 measured
SciCodeNot measured49.5%Only Qwen3.7 Max measured
Terminal-Bench Hard (Artificial Analysis run)6.8%50.8%Qwen3.7 Max
ALE-Bench147.41189.4Qwen3.7 Max
LMArena WebDev: ReactNot measured1514Only Qwen3.7 Max measured
LMArena WebDev: HTMLNot measured1512Only Qwen3.7 Max 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 Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
AA Intelligence Index9.229.5Qwen3.7 Max
Epoch Capabilities IndexNot measured154.0Only Qwen3.7 Max measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
AA Agentic IndexNot measured22.5Only Qwen3.7 Max measured
Design Arena: agents building web appsNot measured1208Only Qwen3.7 Max measured
Design Arena: agents building full-stack appsNot measured1191Only Qwen3.7 Max measured
Design Arena: agents building mobile appsNot measured1154Only Qwen3.7 Max measured
Design Arena: agents building Android appsNot measured1159Only Qwen3.7 Max measured
Design Arena: agents building Godot gamesNot measured1187Only Qwen3.7 Max measured
Design Arena: agents building gamesNot measured1161Only Qwen3.7 Max measured
Design Arena: HTML slidesNot measured1098Only Qwen3.7 Max measured
Design Arena: PowerPoint slidesNot measured1169Only Qwen3.7 Max measured
Finance Agent (Vals AI)Not measured47.8%Only Qwen3.7 Max measured
Tax Agent Bench (Vals AI)Not measured15.8%Only Qwen3.7 Max measured
Legal Research Bench (Vals AI)Not measured25.5%Only Qwen3.7 Max measured
Excel Modeling Benchmark (Vals AI)Not measured57.0%Only Qwen3.7 Max measured
τ²-Bench Telecom38.3%94.7%Qwen3.7 Max
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
GPQA DiamondNot measured90.2%Only Qwen3.7 Max measured
GPQA Diamond (Artificial Analysis run)56.9%92.3%Qwen3.7 Max
Humanity's Last Exam (Artificial Analysis run)4.2%40.5%Qwen3.7 Max
GPQA Diamond (Epoch AI run)Not measured90.9%Only Qwen3.7 Max measured
SimpleBenchNot measured70.4%Only Qwen3.7 Max measured
Chess puzzlesNot measured26.0%Only Qwen3.7 Max measured
Mystery game puzzlesNot measured32.0%Only Qwen3.7 Max measured
EBR-benchNot measured9.5%Only Qwen3.7 Max measured
Kagi LLM Benchmark44.8%Not measuredOnly Nova Premier 1.0 measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
AA Math Index17.3Not measuredOnly Nova Premier 1.0 measured
AIME 2025 (Artificial Analysis run)17.3%Not measuredOnly Nova Premier 1.0 measured
OTIS Mock AIMENot measured95.6%Only Qwen3.7 Max measured
FrontierMath, tiers 1 to 3Not measured64.6%Only Qwen3.7 Max measured
FrontierMath, tier 4Not measured34.1%Only Qwen3.7 Max measured
ProofBenchNot measured26.0%Only Qwen3.7 Max measured
Neither model has a result on the other 8 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Knowledge benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
MMLU ProNot measured89.3%Only Qwen3.7 Max measured
LegalBench (Vals AI)Not measured84.9%Only Qwen3.7 Max measured
CorpFin (Vals AI)Not measured63.7%Only Qwen3.7 Max measured
TaxEval (Vals AI)Not measured75.3%Only Qwen3.7 Max measured
MedCode (Vals AI)Not measured38.8%Only Qwen3.7 Max measured
MMLU Pro (Artificial Analysis run)73.3%Not measuredOnly Nova Premier 1.0 measured
SimpleQA VerifiedNot measured55.8%Only Qwen3.7 Max measured
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index01
Web design benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
Design Arena: websites8351274Qwen3.7 Max
Design Arena: UI componentsNot measured1267Only Qwen3.7 Max measured
Design Arena: game devNot measured1265Only Qwen3.7 Max measured
Design Arena: data vizNot measured1276Only Qwen3.7 Max measured
Design Arena: 3DNot measured1265Only Qwen3.7 Max measured
Design Arena: all code categoriesNot measured1275Only Qwen3.7 Max measured
Design Arena: SVGNot measured1216Only Qwen3.7 Max measured
Design Arena: ASCII artNot measured1211Only Qwen3.7 Max measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
MedScribe (Vals AI)Not measured79.4%Only Qwen3.7 Max measured
IFBench36.2%80.5%Qwen3.7 Max
Long context reasoning (AA-LCR)34.3%79.0%Qwen3.7 Max
DTBenchNot measured92.3%Only Qwen3.7 Max measured
LMCANot measured44.0%Only Qwen3.7 Max measured
EQ-Bench 4Not measured1110Only Qwen3.7 Max measured
Neither model has a result on the other 42 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Nova Premier 1.0 and Qwen3.7 Max
BenchmarkNova Premier 1.0Qwen3.7 MaxLeader
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 Premier 1.0$0.0088Fits in one request
Qwen3.7 Max$0.0037Fits in one request

Qwen3.7 Max costs less

Repository review

50K input + 3K output tokens

Nova Premier 1.0$0.163Fits in one request
Qwen3.7 Max$0.087Fits in one request

Qwen3.7 Max costs less

Agent loop

200K input + 10K output tokens

Nova Premier 1.0$0.625Fits in one request
Qwen3.7 Max$0.339Fits in one request

Qwen3.7 Max 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 Premier 1.0 and Qwen3.7 Max
SpecificationAmazon Nova Premier 1.0Qwen3.7 Max
MakerAmazonQwen
WeightsNo public weights listedNo public weights listed
ReleasedOctober 31, 2025May 21, 2026
Context window1M1M
Input $/Mtok$2.50$1.475
Output $/Mtok$12.50$4.425
Coding indexNot benchmarked54.1
Independent measurements behind it03
Cost per solved taskNot computable$4.69
SourceOpenRouter listingOpenRouter listing

Questions

Is Nova Premier 1.0 better than Qwen3.7 Max for coding?

Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Nova Premier 1.0 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Nova Premier 1.0 or Qwen3.7 Max?

Nova Premier 1.0 costs $2.50 per million input tokens and $12.50 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Cost per solved task is known for Qwen3.7 Max only.

Which has the bigger context window, Nova Premier 1.0 or Qwen3.7 Max?

Neither: both take 1M tokens.

How many benchmarks is the Nova Premier 1.0 vs Qwen3.7 Max comparison based on?

9 benchmarks have a result for both models as of October 22, 2026. Nova Premier 1.0's coding index rests on 0 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.

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