GLM 4.7 vs Nemotron 3.5 Super VL
Updated October 21, 2026. 8 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
GLM 4.7 scores 33.6 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.
Pick GLM 4.7 when what matters is Design Arena: websites or Design Arena: UI components, where it leads; pick Nemotron 3.5 Super VL when output tokens cost 4.0x less, or when the context window is larger, 1.05M against 205K tokens.
- Shared results
- 8
- GLM 4.7 only
- 72
- Nemotron 3.5 Super VL only
- 8
- Like-for-like categories
- 4 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.
Coding
Vibe Coding Index, four public coding benchmarks
No call
33.6 against 65.5, from different benchmarks with none in common.
Directional only
Intelligence
AA Intelligence Index
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 26.5 against 22.2.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 1171.
Same board, both measured
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GLM 4.7 $0.0015, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, Nemotron 3.5 Super VL 65.5 from 1 benchmark.
- Widest category
- Web design, 7.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: game dev: GLM 4.7 1189 against Nemotron 3.5 Super VL 1108, 10.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against Nemotron 3.5 Super VL $0.50 per million output tokens, 4.0x apart.
- Context window
- GLM 4.7 takes 205K tokens, Nemotron 3.5 Super VL 1.05M.
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 | GLM 4.7 | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 5 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 6 | 2 |
CodingDirectional onlyFour of these boards feed the Vibe Coding Index14 boards, none shared
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 26.5 | Nemotron 3.5 Super VL |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1118 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| LiveBench research | Not measured | 70.9% | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | Not measured | 4.0% | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | Not measured | 15.8% | Only Nemotron 3.5 Super VL measured |
| τ²-Bench Telecom | 95.9% | Not measured | Only GLM 4.7 measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | Not measured | Only GLM 4.7 measured |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 30.7% | Nemotron 3.5 Super VL |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1411 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1469 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1429 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1171 | GLM 4.7 |
| Design Arena: UI components | 1199 | 1148 | GLM 4.7 |
| Design Arena: game dev | 1189 | 1108 | GLM 4.7 |
| Design Arena: data viz | 1195 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1191 | 1132 | GLM 4.7 |
| Design Arena: all code categories | 1217 | 1155 | GLM 4.7 |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | Not measured | Only GLM 4.7 measured |
| Long context reasoning (AA-LCR) | 71.0% | 69.0% | GLM 4.7 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1401 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1495 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | Nemotron 3.5 Super VL | 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
Nemotron 3.5 Super VL costs less
Repository review
50K input + 3K output tokens
Nemotron 3.5 Super VL costs less
Agent loop
200K input + 10K output tokens
Nemotron 3.5 Super VL 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 | Z.ai GLM 4.7 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | Z.ai | NVIDIA |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | October 15, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.54 | $0.15 |
| Output $/Mtok | $1.98 | $0.50 |
| Coding index | 33.6 | 65.5 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Nemotron 3.5 Super VL for coding?
GLM 4.7 scores 33.6 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick GLM 4.7 when what matters is Design Arena: websites or Design Arena: UI components, where it leads; pick Nemotron 3.5 Super VL when output tokens cost 4.0x less, or when the context window is larger, 1.05M against 205K tokens.
Which is cheaper, GLM 4.7 or Nemotron 3.5 Super VL?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Nemotron 3.5 Super VL comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.