GPT-5.2-Codex vs Nemotron 3 Super
GPT-5.2-Codex leads Nemotron 3 Super on the Artificial Analysis Intelligence Index, 28.5 to 12.8, and Nemotron 3 Super costs 31.1x less per output token ($0.45 vs $14.00 per million). Updated October 22, 2026; 8 benchmarks have a result for both.
GPT-5.2-Codex or Nemotron 3 Super: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nemotron 3 Super has none, so the two cannot be ranked on coding.
Pick Nemotron 3 Super for output tokens 31.1x cheaper ($0.45 vs $14.00 per million).
- Shared results
- 8
- GPT-5.2-Codex only
- 14
- Nemotron 3 Super only
- 30
- Like-for-like categories
- 5 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 12.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Nemotron 3 Super has a AA Agentic Index result, 1.7.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3 Super
GPT-5.2-Codex $0.0088, Nemotron 3 Super $0.0003 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 Super not benchmarked.
- Widest category
- Agentic work, 24.3 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-5.2-Codex 1299.9 against Nemotron 3 Super 213.9, 36.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Nemotron 3 Super $0.45 per million output tokens, 31.1x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Nemotron 3 Super 262K.
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 Super | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 8 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | 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 |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| AA Coding Index | Not measured | 37.7 | Only Nemotron 3 Super 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 | 36.2% | Only Nemotron 3 Super measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | 28.8% | GPT-5.2-Codex |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| WeirdML | Not measured | 38.0% | Only Nemotron 3 Super measured |
| ALE-Bench | 1299.9 | 213.9 | GPT-5.2-Codex |
| LMArena Text: coding | Not measured | 1403 | Only Nemotron 3 Super measured |
| LMArena Text: software and IT | Not measured | 1409 | Only Nemotron 3 Super 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 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 12.8 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.7 | Only Nemotron 3 Super measured |
| 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 |
| τ²-Bench Telecom | 92.1% | 67.8% | GPT-5.2-Codex |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | 80.0% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 20.8% | GPT-5.2-Codex |
| LMArena Text: hard prompts | Not measured | 1386 | Only Nemotron 3 Super measured |
| LMArena Text: expert | Not measured | 1398 | Only Nemotron 3 Super measured |
| LMArena Text: hard prompts in English | Not measured | 1397 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1380 | Only Nemotron 3 Super measured |
| LMArena Text: mathematical work | Not measured | 1405 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1387 | Only Nemotron 3 Super measured |
| LMArena Text: medicine and healthcare | Not measured | 1383 | Only Nemotron 3 Super measured |
| LMArena Text: legal and government | Not measured | 1392 | Only Nemotron 3 Super measured |
| LMArena Text: business and finance | Not measured | 1364 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | Leader |
|---|---|---|---|
| IFBench | 77.6% | 71.5% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 82.3% | 65.7% | GPT-5.2-Codex |
| LMArena Text | Not measured | 1378 | Only Nemotron 3 Super measured |
| LMArena Text: instruction following | Not measured | 1347 | Only Nemotron 3 Super measured |
| LMArena Text: creative writing | Not measured | 1317 | Only Nemotron 3 Super measured |
| LMArena Text: English | Not measured | 1400 | Only Nemotron 3 Super measured |
| LMArena Text: non-English | Not measured | 1356 | Only Nemotron 3 Super measured |
| LMArena Text: Chinese | Not measured | 1431 | Only Nemotron 3 Super measured |
| LMArena Text: German | Not measured | 1341 | Only Nemotron 3 Super measured |
| LMArena Text: French | Not measured | 1403 | Only Nemotron 3 Super measured |
| LMArena Text: Spanish | Not measured | 1418 | Only Nemotron 3 Super measured |
| LMArena Text: Russian | Not measured | 1336 | Only Nemotron 3 Super measured |
| LMArena Text: Polish | Not measured | 1346 | Only Nemotron 3 Super measured |
| LMArena Text: multi-turn | Not measured | 1369 | Only Nemotron 3 Super measured |
| LMArena Text: longer queries | Not measured | 1362 | Only Nemotron 3 Super measured |
| LMArena Text: writing and language | Not measured | 1332 | Only Nemotron 3 Super measured |
| LMArena Text: entertainment and media | Not measured | 1336 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Nemotron 3 Super | 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 Super costs less
Repository review
50K input + 3K output tokens
Nemotron 3 Super costs less
Agent loop
200K input + 10K output tokens
Nemotron 3 Super 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 Super |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | January 14, 2026 | March 11, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.75 | $0.085 |
| Output $/Mtok | $14.00 | $0.45 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Nemotron 3 Super for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; Nemotron 3 Super has none, so the two cannot be ranked on coding. Pick Nemotron 3 Super for output tokens 31.1x cheaper ($0.45 vs $14.00 per million).
Which is cheaper, GPT-5.2-Codex or Nemotron 3 Super?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Nemotron 3 Super costs $0.085 and $0.45. 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 Super?
GPT-5.2-Codex, with 400K tokens against 262K.
How many benchmarks is the GPT-5.2-Codex vs Nemotron 3 Super comparison based on?
8 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 Super's on 0. Where only one model has a result, the page says so instead of naming a winner.