GPT-5.2-Codex vs Nemotron 3.5 Super VL
GPT-5.2-Codex leads Nemotron 3.5 Super VL on the Artificial Analysis Intelligence Index, 28.5 to 26.5, and Nemotron 3.5 Super VL costs 28.0x less per output token ($0.50 vs $14.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5.2-Codex or Nemotron 3.5 Super VL: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nemotron 3.5 Super VL has none, so the two cannot be ranked on coding.
Pick Nemotron 3.5 Super VL for output tokens 28.0x cheaper ($0.50 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 3
- GPT-5.2-Codex only
- 19
- Nemotron 3.5 Super VL only
- 13
- Like-for-like categories
- 3 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
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 26.5.
Same board, both measured
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 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GPT-5.2-Codex $0.0088, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- General, 13.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-5.2-Codex 82.3% against Nemotron 3.5 Super VL 69.0%, 13.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 28.0x apart.
- Context window
- GPT-5.2-Codex takes 400K 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 | GPT-5.2-Codex | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 3 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 26.5 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex 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 | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 30.7% | GPT-5.2-Codex |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | 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 | 1153 | 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 | 1155 | 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | 69.0% | GPT-5.2-Codex |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | 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 | OpenAI GPT-5.2-Codex | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | October 15, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.15 |
| Output $/Mtok | $14.00 | $0.50 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Nemotron 3.5 Super VL for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nemotron 3.5 Super VL has none, so the two cannot be ranked on coding. Pick Nemotron 3.5 Super VL for output tokens 28.0x cheaper ($0.50 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.2-Codex or Nemotron 3.5 Super VL?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Nemotron 3.5 Super VL comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks 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.