GPT-5 vs Nemotron 3.5 Lightning
GPT-5 leads Nemotron 3.5 Lightning on coding, 38.8 to 22.6 on the Vibe Coding Index, and Nemotron 3.5 Lightning costs 68.0x less per output token ($0.147 vs $10.00 per million). Updated October 21, 2026; 10 benchmarks have a result for both.
GPT-5 or Nemotron 3.5 Lightning: which to pick
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 22.6 for Nemotron 3.5 Lightning.
Pick Nemotron 3.5 Lightning for output tokens 68.0x cheaper ($0.147 vs $10.00 per million) and a lower cost per solved task ($0.06 against $0.39).
- Shared results
- 10
- GPT-5 only
- 103
- Nemotron 3.5 Lightning only
- 10
- 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
GPT-5
GPT-5 scores 38.8 against 22.6.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5
GPT-5 leads, 23.0 against 12.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Nemotron 3.5 Lightning has a AA Agentic Index result, 3.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-5 has a Design Arena: websites result, 1184.
Directional only
Long documents
Listed context window
GPT-5
GPT-5 takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Lightning
GPT-5 $0.0063, Nemotron 3.5 Lightning $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Nemotron 3.5 Lightning
GPT-5 $0.39, Nemotron 3.5 Lightning $0.06.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 38.8 from 3 benchmarks, Nemotron 3.5 Lightning 22.6 from 2 benchmarks.
- Widest category
- General, 48.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- MedScribe (Vals AI): GPT-5 83.7% against Nemotron 3.5 Lightning 4.3%, 79.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5 $10.00 against Nemotron 3.5 Lightning $0.147 per million output tokens, 68.0x apart.
- Context window
- GPT-5 takes 400K tokens, Nemotron 3.5 Lightning 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 | Bars | Nemotron 3.5 Lightning | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 10 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| Aider polyglot | 88.0% | Not measured | Only GPT-5 measured |
| SWE-bench Verified | 74.4% | Not measured | Only GPT-5 measured |
| LMArena WebDev | 1418 | Not measured | Only GPT-5 measured |
| AA Coding Index | 37.8 | 26.8 | GPT-5 |
| LiveCodeBench (Vals AI) | 85.9% | Not measured | Only GPT-5 measured |
| SWE-bench Verified (Vals AI) | 69.0% | 52.8% | GPT-5 |
| Vibe Code Bench (Vals AI) | 20.1% | 0.0% | GPT-5 |
| Code migration (Vals AI) | Not measured | 3.0% | Only Nemotron 3.5 Lightning measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Nemotron 3.5 Lightning measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 10.9% | Only Nemotron 3.5 Lightning measured |
| CyberBench (Vals AI) | Not measured | 29.5% | Only Nemotron 3.5 Lightning measured |
| LiveCodeBench (Artificial Analysis run) | 84.6% | Not measured | Only GPT-5 measured |
| SciCode | Not measured | 32.1% | Only Nemotron 3.5 Lightning measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | Not measured | Only GPT-5 measured |
| Terminal-Bench | 49.6% | Not measured | Only GPT-5 measured |
| GSO-Bench | 6.9% | Not measured | Only GPT-5 measured |
| WeirdML | 60.7% | Not measured | Only GPT-5 measured |
| ALE-Bench | 807.6 | 769.2 | GPT-5 |
| LMArena Text: coding | 1436 | Not measured | Only GPT-5 measured |
| LMArena Text: software and IT | 1420 | Not measured | Only GPT-5 measured |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5 measured |
| SciCode | 0.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.0 | 12.9 | GPT-5 |
| Epoch Capabilities Index | 150.0 | Not measured | Only GPT-5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 3.5 | Only Nemotron 3.5 Lightning measured |
| Finance Agent (Vals AI) | Not measured | 18.5% | Only Nemotron 3.5 Lightning measured |
| Tax Agent Bench (Vals AI) | Not measured | 2.6% | Only Nemotron 3.5 Lightning measured |
| Legal Research Bench (Vals AI) | Not measured | 2.9% | Only Nemotron 3.5 Lightning measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 17.0% | Only Nemotron 3.5 Lightning measured |
| τ²-Bench Telecom | 86.5% | Not measured | Only GPT-5 measured |
| METR time horizons | 69.6% | Not measured | Only GPT-5 measured |
| Remote Labor Index | 1.7% | Not measured | Only GPT-5 measured |
| GDPval | 34.8% | Not measured | Only GPT-5 measured |
| BALROG | 32.8% | Not measured | Only GPT-5 measured |
| DeepResearch Bench | 49.6% | Not measured | Only GPT-5 measured |
| BrowseComp-Plus | 80.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GPT-5 measured |
| MMMU Pro (Vals AI) | 81.5% | Not measured | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 85.4% | 74.3% | GPT-5 |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 10.6% | GPT-5 |
| GPQA Diamond (Epoch AI run) | 86.2% | Not measured | Only GPT-5 measured |
| ARC-AGI | 65.7% | Not measured | Only GPT-5 measured |
| ARC-AGI-2 | 9.9% | Not measured | Only GPT-5 measured |
| SimpleBench | 56.7% | Not measured | Only GPT-5 measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-5 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5 measured |
| EBR-bench | 12.7% | Not measured | Only GPT-5 measured |
| VPCT | 66.0% | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only GPT-5 measured |
| LMArena Text: expert | 1419 | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts in English | 1419 | Not measured | Only GPT-5 measured |
| LMArena Vision: diagrams | 1252 | Not measured | Only GPT-5 measured |
| LMArena Vision: homework | 1278 | Not measured | Only GPT-5 measured |
| Kagi LLM Benchmark | 72.7% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| AIME | 93.4% | Not measured | Only GPT-5 measured |
| MATH 500 (Vals AI) | 96.0% | Not measured | Only GPT-5 measured |
| MGSM (Vals AI) | 92.8% | Not measured | Only GPT-5 measured |
| AA Math Index | 94.3 | Not measured | Only GPT-5 measured |
| AIME 2025 (Artificial Analysis run) | 94.3% | Not measured | Only GPT-5 measured |
| OTIS Mock AIME | 91.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tiers 1 to 3 | 55.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tier 4 | 22.0% | Not measured | Only GPT-5 measured |
| ProofBench | 18.0% | Not measured | Only GPT-5 measured |
| MATH level 5 | 98.1% | Not measured | Only GPT-5 measured |
| LMArena Text: math | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: mathematical work | 1405 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| MMLU Pro | 86.5% | Not measured | Only GPT-5 measured |
| LegalBench (Vals AI) | 86.0% | Not measured | Only GPT-5 measured |
| CaseLaw (Vals AI) | 66.5% | Not measured | Only GPT-5 measured |
| CorpFin (Vals AI) | 61.1% | 54.2% | GPT-5 |
| TaxEval (Vals AI) | 73.4% | Not measured | Only GPT-5 measured |
| MortgageTax (Vals AI) | 65.5% | Not measured | Only GPT-5 measured |
| MedQA (Vals AI) | 96.3% | Not measured | Only GPT-5 measured |
| MedCode (Vals AI) | 49.6% | Not measured | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | 87.1% | Not measured | Only GPT-5 measured |
| SimpleQA Verified | 50.1% | Not measured | Only GPT-5 measured |
| GeoBench | 81.0% | Not measured | Only GPT-5 measured |
| LMArena Text: science | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: medicine and healthcare | 1413 | Not measured | Only GPT-5 measured |
| LMArena Text: legal and government | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: business and finance | 1400 | Not measured | Only GPT-5 measured |
| FACTS Benchmark Suite | 56.5% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| Design Arena: websites | 1184 | Not measured | Only GPT-5 measured |
| Design Arena: UI components | 1180 | Not measured | Only GPT-5 measured |
| Design Arena: game dev | 1185 | Not measured | Only GPT-5 measured |
| Design Arena: data viz | 1221 | Not measured | Only GPT-5 measured |
| Design Arena: 3D | 1056 | Not measured | Only GPT-5 measured |
| Design Arena: all code categories | 1168 | Not measured | Only GPT-5 measured |
| Design Arena: SVG | 1189 | Not measured | Only GPT-5 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 43.7% | Not measured | Only GPT-5 measured |
| MedScribe (Vals AI) | 83.7% | 4.3% | GPT-5 |
| IFBench | 73.1% | Not measured | Only GPT-5 measured |
| Long context reasoning (AA-LCR) | 78.2% | 60.3% | GPT-5 |
| DTBench | 90.7% | Not measured | Only GPT-5 measured |
| LMCA | 40.0% | Not measured | Only GPT-5 measured |
| Fiction.LiveBench | 97.2% | Not measured | Only GPT-5 measured |
| Creative writing (Lech Mazur) | 86.0% | Not measured | Only GPT-5 measured |
| LMArena Text | 1406 | Not measured | Only GPT-5 measured |
| LMArena Text: instruction following | 1388 | Not measured | Only GPT-5 measured |
| LMArena Text: creative writing | 1365 | Not measured | Only GPT-5 measured |
| LMArena Vision | 1232 | Not measured | Only GPT-5 measured |
| LMArena Text: English | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: non-English | 1397 | Not measured | Only GPT-5 measured |
| LMArena Text: Chinese | 1422 | Not measured | Only GPT-5 measured |
| LMArena Text: German | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: Japanese | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Korean | 1360 | Not measured | Only GPT-5 measured |
| LMArena Text: French | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Spanish | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: Russian | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: Polish | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: multi-turn | 1426 | Not measured | Only GPT-5 measured |
| LMArena Text: longer queries | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only GPT-5 measured |
| LMArena Text: entertainment and media | 1379 | Not measured | Only GPT-5 measured |
| LMArena Vision: English | 1231 | Not measured | Only GPT-5 measured |
| LMArena Vision: Chinese | 1267 | Not measured | Only GPT-5 measured |
| LMArena Vision: reading text | 1246 | Not measured | Only GPT-5 measured |
| LMArena Vision: humor | 1219 | Not measured | Only GPT-5 measured |
| LMArena Vision: captioning | 1212 | Not measured | Only GPT-5 measured |
| LMArena Vision: recognizing things | 1263 | Not measured | Only GPT-5 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Lightning | 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 Lightning costs less
Repository review
50K input + 3K output tokens
Nemotron 3.5 Lightning costs less
Agent loop
200K input + 10K output tokens
Nemotron 3.5 Lightning 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 | NVIDIA Nemotron 3.5 Lightning |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | August 11, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.0534 |
| Output $/Mtok | $10.00 | $0.147 |
| Coding index | 38.8 | 22.6 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.39 | $0.06 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 better than Nemotron 3.5 Lightning for coding?
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 22.6 for Nemotron 3.5 Lightning. Pick Nemotron 3.5 Lightning for output tokens 68.0x cheaper ($0.147 vs $10.00 per million) and a lower cost per solved task ($0.06 against $0.39).
Which is cheaper, GPT-5 or Nemotron 3.5 Lightning?
GPT-5 costs $1.25 per million input tokens and $10.00 per million output tokens; Nemotron 3.5 Lightning costs $0.0534 and $0.147. Per solved SWE-bench Verified task, GPT-5 costs $0.39 and Nemotron 3.5 Lightning $0.06.
Which has the bigger context window, GPT-5 or Nemotron 3.5 Lightning?
GPT-5, with 400K tokens against 262K.
How many benchmarks is the GPT-5 vs Nemotron 3.5 Lightning comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. GPT-5's coding index rests on 3 independent benchmarks and Nemotron 3.5 Lightning's on 2. Where only one model has a result, the page says so instead of naming a winner.