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Nova Pro 1.0 vs GLM 4.7

Updated October 21, 2026. 39 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Nova Pro 1.0 Replace GLM 4.7
Nova Pro 1.0Amazon
Coding
No data
Overall
7.0
Output $/M
$3.20
0
GLM 4.7Z.ai
Coding
55.1
Overall
22.2
Output $/M
$2.20
39
vs boards won of 39 shared

Decision reading

Only GLM 4.7 has a coding score, 55.1 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 205K tokens.

Model ANova Pro 1.0AmazonNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
39
Nova Pro 1.0 only
3
GLM 4.7 only
41
Like-for-like categories
8 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, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Agentic work, 81.9 points apart on average over 1 shared benchmark.
Widest single gap
AA Math Index: Nova Pro 1.0 7.0 against GLM 4.7 95.0, 88.0 points apart on a 0 to 100 scale.
Output price
Nova Pro 1.0 $3.20 against GLM 4.7 $2.20 per million output tokens, 1.5x apart.
Context window
Nova Pro 1.0 takes 300K tokens, GLM 4.7 205K.

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 1.0BarsGLM 4.7Ties
Coding04
Intelligence01
Agentic work01
Reasoning05
Math04
Knowledge05
Web design01
General018
All shared boards039
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)23.3%89.4%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)6.1%31.8%GLM 4.7
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding12701454GLM 4.7
LMArena Text: software and IT12711453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 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 GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
AA Intelligence Index7.022.2GLM 4.7
Epoch Capabilities IndexNot measured143.6Only GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1119Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom14.0%95.9%GLM 4.7
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)49.9%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)3.2%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)Not measured83.3%Only GLM 4.7 measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts12461443GLM 4.7
LMArena Text: expert12111424GLM 4.7
LMArena Text: hard prompts in English12591452GLM 4.7
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index7.095.0GLM 4.7
AIME 2025 (Artificial Analysis run)7.0%95.0%GLM 4.7
OTIS Mock AIMENot measured83.3%Only GLM 4.7 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: math12521424GLM 4.7
LMArena Text: mathematical work12611411GLM 4.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)69.1%85.6%GLM 4.7
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
MMLU82.0%Not measuredOnly Nova Pro 1.0 measured
LMArena Text: science12551457GLM 4.7
LMArena Text: medicine and healthcare12601469GLM 4.7
LMArena Text: legal and government12811444GLM 4.7
LMArena Text: business and finance12291429GLM 4.7
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index01
Web design benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
Design Arena: websites7961226GLM 4.7
Design Arena: UI componentsNot measured1199Only GLM 4.7 measured
Design Arena: game devNot measured1188Only GLM 4.7 measured
Design Arena: data vizNot measured1195Only GLM 4.7 measured
Design Arena: 3DNot measured1191Only GLM 4.7 measured
Design Arena: all code categoriesNot measured1217Only GLM 4.7 measured
Design Arena: SVGNot measured1143Only GLM 4.7 measured
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for Nova Pro 1.0 and GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBench38.1%67.9%GLM 4.7
Long context reasoning (AA-LCR)21.0%71.0%GLM 4.7
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text12591435GLM 4.7
LMArena Text: instruction following12351412GLM 4.7
LMArena Text: creative writing12121401GLM 4.7
LMArena Vision980Not measuredOnly Nova Pro 1.0 measured
LMArena Text: English12761453GLM 4.7
LMArena Text: non-English12331417GLM 4.7
LMArena Text: Chinese12441495GLM 4.7
LMArena Text: German12431426GLM 4.7
LMArena Text: Japanese12001439GLM 4.7
LMArena Text: Korean12031399GLM 4.7
LMArena Text: French12711432GLM 4.7
LMArena Text: Spanish11821434GLM 4.7
LMArena Text: Russian12401423GLM 4.7
LMArena Text: PolishNot measured1435Only GLM 4.7 measured
LMArena Text: multi-turn12461446GLM 4.7
LMArena Text: longer queries12551432GLM 4.7
LMArena Text: writing and language12471411GLM 4.7
LMArena Text: entertainment and media12101400GLM 4.7
LMArena Vision: English987Not measuredOnly Nova Pro 1.0 measured
Neither model has a result on the other 22 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 GLM 4.7
BenchmarkNova Pro 1.0GLM 4.7Leader
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
GLM 4.7$0.0017Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

Nova Pro 1.0$0.050Fits in one request
GLM 4.7$0.037Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

Nova Pro 1.0$0.192Fits in one request
GLM 4.7$0.142Exceeds the context window

GLM 4.7 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 GLM 4.7
SpecificationAmazon Nova Pro 1.0Z.ai GLM 4.7
MakerAmazonZ.ai
WeightsNo public weights listedOpen weights
ReleasedDecember 5, 2024December 22, 2025
Context window300K205K
Input $/Mtok$0.80$0.60
Output $/Mtok$3.20$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it02
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is Nova Pro 1.0 better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 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 205K tokens.

Which is cheaper, Nova Pro 1.0 or GLM 4.7?

Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, Nova Pro 1.0 or GLM 4.7?

Nova Pro 1.0, with 300K tokens against 205K.

How many benchmarks is the Nova Pro 1.0 vs GLM 4.7 comparison based on?

39 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 GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

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