GPT-4o-mini (2024-07-18) vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL costs 1.2x less per output token than GPT-4o-mini (2024-07-18) ($0.50 vs $0.60 per million). Updated October 23, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
- Coding
- No data
- Overall
- 35.6
- Output $/M
- $0.50
GPT-4o-mini (2024-07-18) or Nemotron 3.5 Super VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-4o-mini (2024-07-18) has results on Aider polyglot, WeirdML and LMArena Text: coding; Nemotron 3.5 Super VL on LiveBench coding, SciCode and AA Intelligence Index. Compare them on price and context instead.
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
No call
Only Nemotron 3.5 Super VL has a AA Intelligence Index result, 26.5.
Directional only
Web design
Design Arena: websites
No call
Only Nemotron 3.5 Super VL has a Design Arena: websites result, 1172.
Directional only
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GPT-4o-mini (2024-07-18) $0.0004, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Nemotron 3.5 Super VL $0.50 per million output tokens, 1.2x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K tokens, Nemotron 3.5 Super VL 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: coding | 1289 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: software and IT | 1291 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 26.5 | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| 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 |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 30.7% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Epoch AI run) | 37.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| SimpleBench | 10.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Mystery game puzzles | 12.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: expert | 1235 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts in English | 1278 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.9% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| FrontierMath, tiers 1 to 3 | 0.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: mathematical work | 1271 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| SimpleQA Verified | 8.3% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: medicine and healthcare | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: legal and government | 1305 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: business and finance | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1172 | Only Nemotron 3.5 Super VL measured |
| Design Arena: UI components | Not measured | 1148 | Only Nemotron 3.5 Super VL measured |
| Design Arena: game dev | Not measured | 1110 | Only Nemotron 3.5 Super VL measured |
| Design Arena: 3D | Not measured | 1127 | Only Nemotron 3.5 Super VL measured |
| Design Arena: all code categories | Not measured | 1154 | Only Nemotron 3.5 Super VL measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index23 boards measured
| Benchmark | GPT-4o-mini (2024-07-18) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| Long context reasoning (AA-LCR) | Not measured | 69.0% | Only Nemotron 3.5 Super VL measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: instruction following | 1258 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: creative writing | 1268 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision | 1066 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: English | 1301 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: non-English | 1266 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Chinese | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: German | 1272 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Japanese | 1216 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Korean | 1195 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: French | 1297 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Spanish | 1276 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Russian | 1275 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Polish | 1244 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: multi-turn | 1285 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: longer queries | 1289 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: writing and language | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: entertainment and media | 1253 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: English | 1072 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: Chinese | 1033 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 25 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 | OpenAI GPT-4o-mini (2024-07-18) | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | October 15, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini (2024-07-18) better than Nemotron 3.5 Super VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-4o-mini (2024-07-18) or Nemotron 3.5 Super VL?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o-mini (2024-07-18) or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 128K.
Has any benchmark tested both GPT-4o-mini (2024-07-18) and Nemotron 3.5 Super VL?
No benchmark has tested both yet. GPT-4o-mini (2024-07-18) has results on Aider polyglot, WeirdML and LMArena Text: coding; Nemotron 3.5 Super VL on LiveBench coding, SciCode and AA Intelligence Index. Compare them on price and context instead. Data as of October 23, 2026.