GPT-4 vs Nemotron 3.5 Lightning
Nemotron 3.5 Lightning leads GPT-4 on the Artificial Analysis Intelligence Index, 12.9 to 6.7, and Nemotron 3.5 Lightning costs 408.2x less per output token ($0.147 vs $60.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-4 or Nemotron 3.5 Lightning: which to pick
Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; GPT-4 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 3
- GPT-4 only
- 42
- Nemotron 3.5 Lightning only
- 17
- 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 Nemotron 3.5 Lightning has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Nemotron 3.5 Lightning
Nemotron 3.5 Lightning leads, 12.9 against 6.7.
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
Long documents
Listed context window
Nemotron 3.5 Lightning
Nemotron 3.5 Lightning takes 262K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Lightning
GPT-4 $0.060, Nemotron 3.5 Lightning $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Nemotron 3.5 Lightning has the number, $0.06.
Directional only
At a glance
- Coding index
- GPT-4 not benchmarked, Nemotron 3.5 Lightning 22.6 from 2 benchmarks.
- Widest category
- Reasoning, 39.4 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4 34.9% against Nemotron 3.5 Lightning 74.3%, 39.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4 $60.00 against Nemotron 3.5 Lightning $0.147 per million output tokens, 408.2x apart.
- Context window
- GPT-4 takes 8K 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-4 | Bars | Nemotron 3.5 Lightning | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| SWE-bench Verified | 22.4% | Not measured | Only GPT-4 measured |
| SWE-bench Lite | 39.7% | Not measured | Only GPT-4 measured |
| SWE-bench (full test set) | 12.5% | Not measured | Only GPT-4 measured |
| AA Coding Index | 13.1 | 26.8 | Nemotron 3.5 Lightning |
| SWE-bench Verified (Vals AI) | Not measured | 52.8% | Only Nemotron 3.5 Lightning measured |
| Vibe Code Bench (Vals AI) | Not measured | 0.0% | Only Nemotron 3.5 Lightning measured |
| 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 |
| SciCode | Not measured | 32.1% | Only Nemotron 3.5 Lightning measured |
| ALE-Bench | Not measured | 769.2 | Only Nemotron 3.5 Lightning measured |
| LMArena Text: coding | 1209 | Not measured | Only GPT-4 measured |
| LMArena Text: software and IT | 1195 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 12.9 | Nemotron 3.5 Lightning |
| Epoch Capabilities Index | 125.7 | Not measured | Only GPT-4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4 | 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 |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 34.9% | 74.3% | Nemotron 3.5 Lightning |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 10.6% | Only Nemotron 3.5 Lightning measured |
| BIG-Bench Hard | 75.1% | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts | 1200 | Not measured | Only GPT-4 measured |
| LMArena Text: expert | 1149 | Not measured | Only GPT-4 measured |
| LMArena Text: hard prompts in English | 1209 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| GSM8K | 92.0% | Not measured | Only GPT-4 measured |
| LMArena Text: math | 1230 | Not measured | Only GPT-4 measured |
| LMArena Text: mathematical work | 1228 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| CorpFin (Vals AI) | Not measured | 54.2% | Only Nemotron 3.5 Lightning measured |
| MMLU Pro (Artificial Analysis run) | 56.2% | Not measured | Only GPT-4 measured |
| MMLU | 86.4% | Not measured | Only GPT-4 measured |
| AI2 Reasoning Challenge | 96.3% | Not measured | Only GPT-4 measured |
| LMArena Text: science | 1177 | Not measured | Only GPT-4 measured |
| LMArena Text: medicine and healthcare | 1145 | Not measured | Only GPT-4 measured |
| LMArena Text: legal and government | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: business and finance | 1145 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | GPT-4 | Nemotron 3.5 Lightning | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 4.3% | Only Nemotron 3.5 Lightning measured |
| IFBench | 33.2% | Not measured | Only GPT-4 measured |
| Long context reasoning (AA-LCR) | Not measured | 60.3% | Only Nemotron 3.5 Lightning measured |
| DTBench | 62.7% | Not measured | Only GPT-4 measured |
| LMCA | 17.1% | Not measured | Only GPT-4 measured |
| HellaSwag | 95.3% | Not measured | Only GPT-4 measured |
| WinoGrande | 87.5% | Not measured | Only GPT-4 measured |
| LMArena Text | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: instruction following | 1201 | Not measured | Only GPT-4 measured |
| LMArena Text: creative writing | 1191 | Not measured | Only GPT-4 measured |
| LMArena Text: English | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: non-English | 1194 | Not measured | Only GPT-4 measured |
| LMArena Text: Chinese | 1184 | Not measured | Only GPT-4 measured |
| LMArena Text: German | 1198 | Not measured | Only GPT-4 measured |
| LMArena Text: Japanese | 1137 | Not measured | Only GPT-4 measured |
| LMArena Text: Korean | 1087 | Not measured | Only GPT-4 measured |
| LMArena Text: French | 1220 | Not measured | Only GPT-4 measured |
| LMArena Text: Spanish | 1197 | Not measured | Only GPT-4 measured |
| LMArena Text: Russian | 1195 | Not measured | Only GPT-4 measured |
| LMArena Text: Polish | 1265 | Not measured | Only GPT-4 measured |
| LMArena Text: multi-turn | 1206 | Not measured | Only GPT-4 measured |
| LMArena Text: longer queries | 1190 | Not measured | Only GPT-4 measured |
| LMArena Text: writing and language | 1219 | Not measured | Only GPT-4 measured |
| LMArena Text: entertainment and media | 1184 | Not measured | Only GPT-4 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 | 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-4 | NVIDIA Nemotron 3.5 Lightning |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | August 11, 2026 |
| Context window | 8K | 262K |
| Input $/Mtok | $30.00 | $0.0534 |
| Output $/Mtok | $60.00 | $0.147 |
| Coding index | Not benchmarked | 22.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.06 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4 better than Nemotron 3.5 Lightning for coding?
Only Nemotron 3.5 Lightning has a coding score, 22.6 on the Vibe Coding Index; GPT-4 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-4 or Nemotron 3.5 Lightning?
GPT-4 costs $30.00 per million input tokens and $60.00 per million output tokens; Nemotron 3.5 Lightning costs $0.0534 and $0.147. Cost per solved task is known for Nemotron 3.5 Lightning only.
Which has the bigger context window, GPT-4 or Nemotron 3.5 Lightning?
Nemotron 3.5 Lightning, with 262K tokens against 8K.
How many benchmarks is the GPT-4 vs Nemotron 3.5 Lightning comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GPT-4's coding index rests on 0 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.