Gemini 3.5 Flash vs GPT-4.1 Nano
Updated October 21, 2026. 57 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 57 shared
Decision reading
Pick Gemini 3.5 Flash for coding: it scores 42.8 on the Vibe Coding Index against 8.9 for GPT-4.1 Nano.
Pick GPT-4.1 Nano when output tokens cost 22.5x less.
Model AGemini 3.5 FlashGoogle42.8/1001 independent benchmark$1.21 per solved taskModel BGPT-4.1 NanoOpenAI8.9/1001 independent benchmarkNo cost per solved task
- Shared results
- 57
- Gemini 3.5 Flash only
- 53
- GPT-4.1 Nano only
- 12
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Gemini 3.5 Flash
Gemini 3.5 Flash scores 42.8 against 8.9.
One benchmark behind each score
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 7.8.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 50.8%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 63.5%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1268 against 973.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
Gemini 3.5 Flash $0.0060, GPT-4.1 Nano $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.5 Flash has the number, $1.21.
Directional only
At a glance
- Coding index
- Gemini 3.5 Flash 42.8 from 1 benchmark, GPT-4.1 Nano 8.9 from 1 benchmark.
- Widest category
- Agentic work, 78.4 points apart on average over 1 shared benchmark.
- Widest single gap
- ARC-AGI: Gemini 3.5 Flash 92.5% against GPT-4.1 Nano 0.0%, 92.5 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GPT-4.1 Nano $0.40 per million output tokens, 22.5x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GPT-4.1 Nano 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 | Gemini 3.5 Flash | Bars | GPT-4.1 Nano | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 11 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 10 | 0 | ||
| Web design | 6 | 0 | ||
| General | 19 | 0 | ||
| All shared boards | 57 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 8.9% | Only GPT-4.1 Nano measured |
| LMArena WebDev | 1499 | Not measured | Only Gemini 3.5 Flash measured |
| AA Coding Index | Not measured | 11.1 | Only GPT-4.1 Nano measured |
| LiveCodeBench (Vals AI) | 87.6% | 42.7% | Gemini 3.5 Flash |
| SWE-bench Verified (Vals AI) | 78.8% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 4 (Vals AI) | 6.1% | Not measured | Only Gemini 3.5 Flash measured |
| Vibe Code Bench (Vals AI) | 48.7% | Not measured | Only Gemini 3.5 Flash measured |
| Code migration (Vals AI) | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.2% | Not measured | Only Gemini 3.5 Flash measured |
| SkillsBench (Vals AI) | 52.7% | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 32.6% | Only GPT-4.1 Nano measured |
| SciCode | 53.9% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | 3.8% | Gemini 3.5 Flash |
| WeirdML | 62.6% | 19.0% | Gemini 3.5 Flash |
| ALE-Bench | 911.0 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: coding | 1493 | 1306 | Gemini 3.5 Flash |
| LMArena Text: software and IT | 1494 | 1305 | Gemini 3.5 Flash |
| LMArena WebDev: React | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: HTML | 1522 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena WebDev: from an image | 1504 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 7.8 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | 129.5 | Gemini 3.5 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building full-stack apps | 1197 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building mobile apps | 1175 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Android apps | 1190 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Godot games | 1098 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building games | 1180 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: HTML slides | 1142 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: PowerPoint slides | 1178 | Not measured | Only Gemini 3.5 Flash measured |
| Finance Agent (Vals AI) | 57.9% | Not measured | Only Gemini 3.5 Flash measured |
| Tax Agent Bench (Vals AI) | 21.6% | Not measured | Only Gemini 3.5 Flash measured |
| Legal Research Bench (Vals AI) | 30.8% | Not measured | Only Gemini 3.5 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 63.5% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only Gemini 3.5 Flash measured |
| τ²-Bench Telecom | 95.6% | 17.3% | Gemini 3.5 Flash |
| APEX-Agents | 27.5% | Not measured | Only Gemini 3.5 Flash measured |
| Vending-Bench 2 | $5,396 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | 50.8% | Gemini 3.5 Flash |
| MMMU Pro (Vals AI) | 88.3% | 55.0% | Gemini 3.5 Flash |
| GPQA Diamond (Artificial Analysis run) | 92.2% | 51.2% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 3.8% | Gemini 3.5 Flash |
| GPQA Diamond (Epoch AI run) | 92.8% | 48.9% | Gemini 3.5 Flash |
| ARC-AGI | 92.5% | 0.0% | Gemini 3.5 Flash |
| ARC-AGI-2 | 72.1% | 0.0% | Gemini 3.5 Flash |
| SimpleBench | 76.7% | Not measured | Only Gemini 3.5 Flash measured |
| Chess puzzles | 45.0% | 0.0% | Gemini 3.5 Flash |
| Mystery game puzzles | 32.0% | Not measured | Only Gemini 3.5 Flash measured |
| EBR-bench | 4.8% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: hard prompts | 1488 | 1286 | Gemini 3.5 Flash |
| LMArena Text: expert | 1495 | 1271 | Gemini 3.5 Flash |
| LMArena Text: hard prompts in English | 1483 | 1293 | Gemini 3.5 Flash |
| LMArena Vision: diagrams | 1323 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: homework | 1330 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 26.5% | Only GPT-4.1 Nano measured |
| MATH 500 (Vals AI) | Not measured | 80.2% | Only GPT-4.1 Nano measured |
| MGSM (Vals AI) | Not measured | 69.3% | Only GPT-4.1 Nano measured |
| AA Math Index | Not measured | 24.0 | Only GPT-4.1 Nano measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 24.0% | Only GPT-4.1 Nano measured |
| OTIS Mock AIME | 95.6% | 28.9% | Gemini 3.5 Flash |
| FrontierMath, tiers 1 to 3 | 62.8% | Not measured | Only Gemini 3.5 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only Gemini 3.5 Flash measured |
| ProofBench | 31.0% | Not measured | Only Gemini 3.5 Flash measured |
| MATH level 5 | Not measured | 70.0% | Only GPT-4.1 Nano measured |
| LMArena Text: math | 1504 | 1274 | Gemini 3.5 Flash |
| LMArena Text: mathematical work | 1495 | 1274 | Gemini 3.5 Flash |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | 63.5% | Gemini 3.5 Flash |
| LegalBench (Vals AI) | 83.6% | 61.1% | Gemini 3.5 Flash |
| CorpFin (Vals AI) | 64.7% | 42.1% | Gemini 3.5 Flash |
| TaxEval (Vals AI) | 74.4% | 60.8% | Gemini 3.5 Flash |
| MortgageTax (Vals AI) | 68.1% | 52.8% | Gemini 3.5 Flash |
| MedQA (Vals AI) | Not measured | 68.2% | Only GPT-4.1 Nano measured |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 65.7% | Only GPT-4.1 Nano measured |
| SimpleQA Verified | 66.2% | 6.0% | Gemini 3.5 Flash |
| LMArena Text: science | 1495 | 1297 | Gemini 3.5 Flash |
| LMArena Text: medicine and healthcare | 1483 | 1276 | Gemini 3.5 Flash |
| LMArena Text: legal and government | 1488 | 1300 | Gemini 3.5 Flash |
| LMArena Text: business and finance | 1467 | 1267 | Gemini 3.5 Flash |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 973 | Gemini 3.5 Flash |
| Design Arena: UI components | 1272 | 916 | Gemini 3.5 Flash |
| Design Arena: game dev | 1263 | 971 | Gemini 3.5 Flash |
| Design Arena: data viz | 1236 | 891 | Gemini 3.5 Flash |
| Design Arena: 3D | 1244 | 925 | Gemini 3.5 Flash |
| Design Arena: all code categories | 1266 | 963 | Gemini 3.5 Flash |
| Design Arena: SVG | 1254 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: ASCII art | 1253 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 59.5% | Not measured | Only Gemini 3.5 Flash measured |
| SAGE (Vals AI) | 49.9% | Not measured | Only Gemini 3.5 Flash measured |
| MedScribe (Vals AI) | 76.6% | Not measured | Only Gemini 3.5 Flash measured |
| Vals Multimodal Index | 62.9% | Not measured | Only Gemini 3.5 Flash measured |
| IFBench | 76.3% | 32.0% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 20.3% | Gemini 3.5 Flash |
| DTBench | 94.7% | 52.5% | Gemini 3.5 Flash |
| LMCA | 47.1% | 5.5% | Gemini 3.5 Flash |
| Fiction.LiveBench | Not measured | 25.0% | Only GPT-4.1 Nano measured |
| Surface Evolver Bench | 58.1% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text | 1482 | 1285 | Gemini 3.5 Flash |
| LMArena Text: instruction following | 1467 | 1266 | Gemini 3.5 Flash |
| LMArena Text: creative writing | 1470 | 1260 | Gemini 3.5 Flash |
| LMArena Vision | 1308 | 1063 | Gemini 3.5 Flash |
| LMArena Text: English | 1481 | 1303 | Gemini 3.5 Flash |
| LMArena Text: non-English | 1475 | 1260 | Gemini 3.5 Flash |
| LMArena Text: Chinese | 1527 | 1270 | Gemini 3.5 Flash |
| LMArena Text: German | 1494 | 1288 | Gemini 3.5 Flash |
| LMArena Text: Japanese | 1486 | 1198 | Gemini 3.5 Flash |
| LMArena Text: Korean | 1448 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: French | 1490 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Spanish | 1480 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Russian | 1493 | 1261 | Gemini 3.5 Flash |
| LMArena Text: Polish | 1516 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: multi-turn | 1481 | 1277 | Gemini 3.5 Flash |
| LMArena Text: longer queries | 1483 | 1283 | Gemini 3.5 Flash |
| LMArena Text: writing and language | 1478 | 1261 | Gemini 3.5 Flash |
| LMArena Text: entertainment and media | 1459 | 1243 | Gemini 3.5 Flash |
| LMArena Vision: English | 1305 | 1101 | Gemini 3.5 Flash |
| LMArena Vision: Chinese | 1377 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: reading text | 1317 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: humor | 1321 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Document | 1463 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-4.1 Nano | 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
Gemini 3.5 Flash$0.0060Fits in one request
GPT-4.1 Nano$0.0003Fits in one request
GPT-4.1 Nano costs less
Repository review
50K input + 3K output tokens
Gemini 3.5 Flash$0.102Fits in one request
GPT-4.1 Nano$0.0062Fits in one request
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
Gemini 3.5 Flash$0.390Fits in one request
GPT-4.1 Nano$0.024Fits in one request
GPT-4.1 Nano 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 | Google Gemini 3.5 Flash | OpenAI GPT-4.1 Nano |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | May 19, 2026 | April 14, 2025 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.50 | $0.10 |
| Output $/Mtok | $9.00 | $0.40 |
| Coding index | 42.8 | 8.9 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $1.21 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GPT-4.1 Nano for coding?
Pick Gemini 3.5 Flash for coding: it scores 42.8 on the Vibe Coding Index against 8.9 for GPT-4.1 Nano. Pick GPT-4.1 Nano when output tokens cost 22.5x less.
Which is cheaper, Gemini 3.5 Flash or GPT-4.1 Nano?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GPT-4.1 Nano costs $0.10 and $0.40. Cost per solved task is known for Gemini 3.5 Flash only.
Which has the bigger context window, Gemini 3.5 Flash or GPT-4.1 Nano?
Gemini 3.5 Flash, with 1.05M tokens against 1.05M.
How many benchmarks is the Gemini 3.5 Flash vs GPT-4.1 Nano comparison based on?
57 benchmarks have a result for both models as of October 21, 2026. Gemini 3.5 Flash's coding index rests on 1 independent benchmark and GPT-4.1 Nano's on 1. Where only one model has a result, the page says so instead of naming a winner.