GPT-5.6 Sol Pro vs Nemotron 3 Super
Nemotron 3 Super costs 44.4x less per output token than GPT-5.6 Sol Pro ($0.45 vs $20.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-5.6 Sol Pro or Nemotron 3 Super: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 262K tokens).
- Shared results
- 1
- GPT-5.6 Sol Pro only
- 6
- Nemotron 3 Super only
- 38
- Like-for-like categories
- 1 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
No call
Only Nemotron 3 Super has a AA Intelligence Index result, 12.8.
Directional only
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-5.6 Sol Pro
GPT-5.6 Sol Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3 Super
GPT-5.6 Sol Pro $0.014, Nemotron 3 Super $0.0003 per turn.
List prices
At a glance
- Coding index
- GPT-5.6 Sol Pro not benchmarked, Nemotron 3 Super not benchmarked.
- Widest category
- Coding, 51.4 points apart on average over 1 shared benchmark.
- Widest single gap
- WeirdML: GPT-5.6 Sol Pro 89.4% against Nemotron 3 Super 38.0%, 51.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol Pro $20.00 against Nemotron 3 Super $0.45 per million output tokens, 44.4x apart.
- Context window
- GPT-5.6 Sol Pro takes 1.05M 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-5.6 Sol Pro | Bars | Nemotron 3 Super | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 37.7 | Only Nemotron 3 Super measured |
| SciCode | Not measured | 36.2% | Only Nemotron 3 Super measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 28.8% | Only Nemotron 3 Super measured |
| WeirdML | 89.4% | 38.0% | GPT-5.6 Sol Pro |
| ALE-Bench | Not measured | 213.9 | Only Nemotron 3 Super measured |
| LMArena Text: coding | Not measured | 1403 | Only Nemotron 3 Super measured |
| LMArena Text: software and IT | Not measured | 1409 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 12.8 | Only Nemotron 3 Super measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.7 | Only Nemotron 3 Super measured |
| τ²-Bench Telecom | Not measured | 67.8% | Only Nemotron 3 Super measured |
| APEX-Agents | 51.4% | Not measured | Only GPT-5.6 Sol Pro measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 80.0% | Only Nemotron 3 Super measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 20.8% | Only Nemotron 3 Super measured |
| SimpleBench | 71.7% | Not measured | Only GPT-5.6 Sol Pro measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-5.6 Sol Pro measured |
| 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 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| FrontierMath, tier 4 | 80.5% | Not measured | Only GPT-5.6 Sol Pro measured |
| LMArena Text: math | Not measured | 1380 | Only Nemotron 3 Super measured |
| LMArena Text: mathematical work | Not measured | 1405 | Only Nemotron 3 Super measured |
| MathArena | Not measured | 16.6% | Only Nemotron 3 Super measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | 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 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GPT-5.6 Sol Pro | Nemotron 3 Super | Leader |
|---|---|---|---|
| IFBench | Not measured | 71.5% | Only Nemotron 3 Super measured |
| Long context reasoning (AA-LCR) | Not measured | 65.7% | Only Nemotron 3 Super measured |
| DTBench | 96.0% | Not measured | Only GPT-5.6 Sol Pro measured |
| LMCA | 59.2% | Not measured | Only GPT-5.6 Sol Pro measured |
| 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-5.6 Sol Pro | 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-5.6 Sol Pro | NVIDIA Nemotron 3 Super |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | March 11, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $4.00 | $0.085 |
| Output $/Mtok | $20.00 | $0.45 |
| 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 GPT-5.6 Sol Pro better than Nemotron 3 Super for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.6 Sol Pro for a larger context window (1.05M against 262K tokens).
Which is cheaper, GPT-5.6 Sol Pro or Nemotron 3 Super?
GPT-5.6 Sol Pro costs $4.00 per million input tokens and $20.00 per million output tokens; Nemotron 3 Super costs $0.085 and $0.45. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.6 Sol Pro or Nemotron 3 Super?
GPT-5.6 Sol Pro, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Sol Pro vs Nemotron 3 Super comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol Pro's coding index rests on 0 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.