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Nova Pro 1.0 vs Gemma 4 31B

Updated October 21, 2026. 33 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Nova Pro 1.0Amazon
Coding
No data
Overall
7.0
Output $/M
$3.20
0
Gemma 4 31BGoogle
Coding
39.0
Overall
14.7
Output $/M
$0.40
33
vs boards won of 33 shared

Decision reading

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Nova Pro 1.0 has none, so the two cannot be ranked on coding.

Nova Pro 1.0 has the edge where the context window is larger, 300K against 262K tokens.

Shared results
33
Nova Pro 1.0 only
9
Gemma 4 31B only
35
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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, Gemma 4 31B 39.0 from 2 benchmarks.
Widest category
Agentic work, 51.5 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Nova Pro 1.0 14.0% against Gemma 4 31B 65.5%, 51.5 points apart on a 0 to 100 scale.
Output price
Nova Pro 1.0 $3.20 against Gemma 4 31B $0.40 per million output tokens, 8.0x apart.
Context window
Nova Pro 1.0 takes 300K tokens, Gemma 4 31B 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 1.0BarsGemma 4 31BTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General017
All shared boards033
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
LiveBench codingNot measured49.0%Only Gemma 4 31B measured
LMArena WebDevNot measured1367Only Gemma 4 31B measured
AA Coding IndexNot measured43.4Only Gemma 4 31B measured
LiveCodeBench (Artificial Analysis run)23.3%Not measuredOnly Nova Pro 1.0 measured
SciCodeNot measured45.5%Only Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)6.1%36.4%Gemma 4 31B
WeirdMLNot measured52.3%Only Gemma 4 31B measured
ALE-BenchNot measured925.5Only Gemma 4 31B measured
LMArena Text: coding12701459Gemma 4 31B
LMArena Text: software and IT12711463Gemma 4 31B
LMArena WebDev: ReactNot measured1359Only Gemma 4 31B measured
LMArena WebDev: HTMLNot measured1380Only Gemma 4 31B measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
AA Intelligence Index7.014.7Gemma 4 31B
Epoch Capabilities IndexNot measured143.0Only Gemma 4 31B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
AA Agentic IndexNot measured4.2Only Gemma 4 31B measured
LiveBench researchNot measured44.4%Only Gemma 4 31B measured
LiveBench browserNot measured4.0%Only Gemma 4 31B measured
LiveBench long agenticNot measured13.3%Only Gemma 4 31B measured
τ²-Bench Telecom14.0%65.5%Gemma 4 31B
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
LiveBench reasoningNot measured68.3%Only Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)49.9%85.7%Gemma 4 31B
Humanity's Last Exam (Artificial Analysis run)3.2%23.6%Gemma 4 31B
GPQA Diamond (Epoch AI run)Not measured75.8%Only Gemma 4 31B measured
LMArena Text: hard prompts12461448Gemma 4 31B
LMArena Text: expert12111464Gemma 4 31B
LMArena Text: hard prompts in English12591456Gemma 4 31B
LMArena Vision: diagramsNot measured1283Only Gemma 4 31B measured
LMArena Vision: homeworkNot measured1310Only Gemma 4 31B measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
AA Math Index7.0Not measuredOnly Nova Pro 1.0 measured
AIME 2025 (Artificial Analysis run)7.0%Not measuredOnly Nova Pro 1.0 measured
OTIS Mock AIMENot measured73.3%Only Gemma 4 31B measured
LMArena Text: math12521463Gemma 4 31B
LMArena Text: mathematical work12611465Gemma 4 31B
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
CaseLaw (Vals AI)Not measured52.6%Only Gemma 4 31B measured
MortgageTax (Vals AI)Not measured61.4%Only Gemma 4 31B measured
MMLU Pro (Artificial Analysis run)69.1%Not measuredOnly Nova Pro 1.0 measured
SimpleQA VerifiedNot measured10.4%Only Gemma 4 31B measured
MMLU82.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: science12551457Gemma 4 31B
LMArena Text: medicine and healthcare12601451Gemma 4 31B
LMArena Text: legal and government12811438Gemma 4 31B
LMArena Text: business and finance12291430Gemma 4 31B
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Web design benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
Design Arena: websites796Not measuredOnly Nova Pro 1.0 measured
LiveBench designNot measured58.4%Only Gemma 4 31B measured
Neither model has a result on the other 7 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for Nova Pro 1.0 and Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
LiveBench data analysisNot measured53.3%Only Gemma 4 31B measured
SAGE (Vals AI)Not measured55.0%Only Gemma 4 31B measured
IFBench38.1%75.6%Gemma 4 31B
Long context reasoning (AA-LCR)21.0%69.7%Gemma 4 31B
DTBenchNot measured82.7%Only Gemma 4 31B measured
LMCANot measured39.3%Only Gemma 4 31B measured
Surface Evolver BenchNot measured30.6%Only Gemma 4 31B measured
LMArena Text12591443Gemma 4 31B
LMArena Text: instruction following12351433Gemma 4 31B
LMArena Text: creative writing12121417Gemma 4 31B
LMArena Vision9801276Gemma 4 31B
LMArena Text: English12761451Gemma 4 31B
LMArena Text: non-English12331430Gemma 4 31B
LMArena Text: Chinese12441475Gemma 4 31B
LMArena Text: German1243Not measuredOnly Nova Pro 1.0 measured
LMArena Text: Japanese1200Not measuredOnly Nova Pro 1.0 measured
LMArena Text: Korean1203Not measuredOnly Nova Pro 1.0 measured
LMArena Text: French12711436Gemma 4 31B
LMArena Text: Spanish11821442Gemma 4 31B
LMArena Text: Russian12401460Gemma 4 31B
LMArena Text: PolishNot measured1433Only Gemma 4 31B measured
LMArena Text: multi-turn12461452Gemma 4 31B
LMArena Text: longer queries12551446Gemma 4 31B
LMArena Text: writing and language12471432Gemma 4 31B
LMArena Text: entertainment and media12101405Gemma 4 31B
LMArena Vision: English9871271Gemma 4 31B
LMArena Vision: ChineseNot measured1326Only Gemma 4 31B measured
LMArena Vision: reading textNot measured1282Only Gemma 4 31B measured
LMArena Vision: humorNot measured1280Only Gemma 4 31B measured
LMArena Vision: captioningNot measured1218Only Gemma 4 31B measured
LMArena Vision: recognizing thingsNot measured1226Only Gemma 4 31B measured
LMArena Vision: creative writingNot measured1285Only Gemma 4 31B measured
LMArena DocumentNot measured1425Only Gemma 4 31B measured
Neither model has a result on the other 13 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 Gemma 4 31B
BenchmarkNova Pro 1.0Gemma 4 31BLeader
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
Gemma 4 31B$0.0003Fits in one request

Gemma 4 31B costs less

Repository review

50K input + 3K output tokens

Nova Pro 1.0$0.050Fits in one request
Gemma 4 31B$0.0082Fits in one request

Gemma 4 31B costs less

Agent loop

200K input + 10K output tokens

Nova Pro 1.0$0.192Fits in one request
Gemma 4 31B$0.032Fits in one request

Gemma 4 31B 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 Gemma 4 31B
SpecificationAmazon Nova Pro 1.0Google Gemma 4 31B
MakerAmazonGoogle
WeightsNo public weights listedOpen weights
ReleasedDecember 5, 2024April 2, 2026
Context window300K262K
Input $/Mtok$0.80$0.14
Output $/Mtok$3.20$0.40
Coding indexNot benchmarked39.0
Independent measurements behind it02
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Nova Pro 1.0 better than Gemma 4 31B for coding?

Only Gemma 4 31B has a coding score, 39.0 on the Vibe Coding Index; Nova Pro 1.0 has none, so the two cannot be ranked on coding. Nova Pro 1.0 has the edge where the context window is larger, 300K against 262K tokens.

Which is cheaper, Nova Pro 1.0 or Gemma 4 31B?

Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; Gemma 4 31B costs $0.14 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, Nova Pro 1.0 or Gemma 4 31B?

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

How many benchmarks is the Nova Pro 1.0 vs Gemma 4 31B comparison based on?

33 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 Gemma 4 31B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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