Nova Pro 1.0 vs GLM 4.5V
GLM 4.5V leads Nova Pro 1.0 on the Artificial Analysis Intelligence Index, 7.6 to 7.0, and GLM 4.5V costs 1.8x less per output token ($1.80 vs $3.20 per million). Updated October 21, 2026; 36 benchmarks have a result for both.
Nova Pro 1.0 or GLM 4.5V: 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 66K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 36
- Nova Pro 1.0 only
- 6
- GLM 4.5V only
- 5
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 4.5V
GLM 4.5V leads, 7.6 against 7.0.
Same board, both measured
Web design
Design Arena: websites
No call
Only Nova Pro 1.0 has a Design Arena: websites result, 796.
Directional only
Long documents
Listed context window
Nova Pro 1.0
Nova Pro 1.0 takes 300K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
Nova Pro 1.0 $0.0024, GLM 4.5V $0.0015 per turn.
List prices
At a glance
- Coding index
- Nova Pro 1.0 not benchmarked, GLM 4.5V not benchmarked.
- Widest category
- Math, 40.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- AA Math Index: Nova Pro 1.0 7.0 against GLM 4.5V 73.0, 66.0 points apart on a 0 to 100 scale.
- Output price
- Nova Pro 1.0 $3.20 against GLM 4.5V $1.80 per million output tokens, 1.8x apart.
- Context window
- Nova Pro 1.0 takes 300K tokens, GLM 4.5V 66K.
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.
| Category | Nova Pro 1.0 | Bars | GLM 4.5V | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 2 | 14 | ||
| All shared boards | 2 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 23.3% | 60.4% | GLM 4.5V |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 6.8% | GLM 4.5V |
| LMArena Text: coding | 1270 | 1347 | GLM 4.5V |
| LMArena Text: software and IT | 1271 | 1353 | GLM 4.5V |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 7.6 | GLM 4.5V |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 14.0% | 22.5% | GLM 4.5V |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.9% | 68.4% | GLM 4.5V |
| Humanity's Last Exam (Artificial Analysis run) | 3.2% | 6.3% | GLM 4.5V |
| LMArena Text: hard prompts | 1246 | 1335 | GLM 4.5V |
| LMArena Text: expert | 1211 | 1353 | GLM 4.5V |
| LMArena Text: hard prompts in English | 1259 | 1357 | GLM 4.5V |
| LMArena Vision: diagrams | Not measured | 1151 | Only GLM 4.5V measured |
| LMArena Vision: homework | Not measured | 1163 | Only GLM 4.5V measured |
| Kagi LLM Benchmark | Not measured | 59.8% | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Math Index | 7.0 | 73.0 | GLM 4.5V |
| AIME 2025 (Artificial Analysis run) | 7.0% | 73.0% | GLM 4.5V |
| LMArena Text: math | 1252 | 1354 | GLM 4.5V |
| LMArena Text: mathematical work | 1261 | 1336 | GLM 4.5V |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 69.1% | 78.8% | GLM 4.5V |
| MMLU | 82.0% | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: science | 1255 | 1330 | GLM 4.5V |
| LMArena Text: medicine and healthcare | 1260 | 1349 | GLM 4.5V |
| LMArena Text: legal and government | 1281 | 1338 | GLM 4.5V |
| LMArena Text: business and finance | 1229 | 1309 | GLM 4.5V |
| 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
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| Design Arena: websites | 796 | Not measured | Only 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 index214
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| IFBench | 38.1% | 34.2% | Nova Pro 1.0 |
| Long context reasoning (AA-LCR) | 21.0% | 0.0% | Nova Pro 1.0 |
| LMArena Text | 1259 | 1333 | GLM 4.5V |
| LMArena Text: instruction following | 1235 | 1311 | GLM 4.5V |
| LMArena Text: creative writing | 1212 | 1294 | GLM 4.5V |
| LMArena Vision | 980 | 1154 | GLM 4.5V |
| LMArena Text: English | 1276 | 1362 | GLM 4.5V |
| LMArena Text: non-English | 1233 | 1303 | GLM 4.5V |
| LMArena Text: Chinese | 1244 | 1337 | GLM 4.5V |
| LMArena Text: German | 1243 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Japanese | 1200 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Korean | 1203 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: French | 1271 | Not measured | Only Nova Pro 1.0 measured |
| LMArena Text: Spanish | 1182 | 1336 | GLM 4.5V |
| LMArena Text: Russian | 1240 | 1298 | GLM 4.5V |
| LMArena Text: Polish | Not measured | 1302 | Only GLM 4.5V measured |
| LMArena Text: multi-turn | 1246 | 1333 | GLM 4.5V |
| LMArena Text: longer queries | 1255 | 1305 | GLM 4.5V |
| LMArena Text: writing and language | 1247 | 1302 | GLM 4.5V |
| LMArena Text: entertainment and media | 1210 | 1303 | GLM 4.5V |
| LMArena Vision: English | 987 | 1183 | GLM 4.5V |
| LMArena Vision: reading text | Not measured | 1151 | Only GLM 4.5V measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Pro 1.0 | GLM 4.5V | Leader |
|---|---|---|---|
| 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
GLM 4.5V costs less
Repository review
50K input + 3K output tokens
GLM 4.5V costs less
Agent loop
200K input + 10K output tokens
GLM 4.5V 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.
| Specification | Amazon Nova Pro 1.0 | Z.ai GLM 4.5V |
|---|---|---|
| Maker | Amazon | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | August 11, 2025 |
| Context window | 300K | 66K |
| Input $/Mtok | $0.80 | $0.60 |
| Output $/Mtok | $3.20 | $1.80 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Pro 1.0 better than GLM 4.5V 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 66K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Nova Pro 1.0 or GLM 4.5V?
Nova Pro 1.0 costs $0.80 per million input tokens and $3.20 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for neither of them.
Which has the bigger context window, Nova Pro 1.0 or GLM 4.5V?
Nova Pro 1.0, with 300K tokens against 66K.
How many benchmarks is the Nova Pro 1.0 vs GLM 4.5V 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 GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.