Gemini 3.5 Flash vs GPT-4o (2024-11-20)
Gemini 3.5 Flash leads GPT-4o (2024-11-20) on the Artificial Analysis Intelligence Index, 33.6 to 8.4, at about the same output price ($9.00 vs $10.00 per million). Updated October 22, 2026; 12 benchmarks have a result for both.
vs boards won of 12 shared
Gemini 3.5 Flash or GPT-4o (2024-11-20): which to pick
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; GPT-4o (2024-11-20) has none, so the two cannot be ranked on coding.
Pick GPT-4o (2024-11-20) for work measured by TaxEval (Vals AI), where it leads.
Model AGemini 3.5 FlashGoogle39.8/1004 independent benchmarks$1.21 per solved taskModel BGPT-4o (2024-11-20)OpenAINot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 12
- Gemini 3.5 Flash only
- 100
- GPT-4o (2024-11-20) only
- 12
- 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 Gemini 3.5 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 8.4.
Same board, both measured
Reasoning
GPQA Diamond
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 92.7% against 53.8%.
Same board, both measured
Math
AIME
No call
Only GPT-4o (2024-11-20) has a AIME result, 11.9%.
Directional only
Knowledge
MMLU Pro
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 89.5% against 72.6%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Gemini 3.5 Flash has a Design Arena: websites result, 1267.
Directional only
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash $0.0060, GPT-4o (2024-11-20) $0.0075 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 39.8 from 4 benchmarks, GPT-4o (2024-11-20) not benchmarked.
- Widest category
- Math, 89.3 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: Gemini 3.5 Flash 95.6% against GPT-4o (2024-11-20) 6.3%, 89.3 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GPT-4o (2024-11-20) $10.00 per million output tokens, 1.1x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GPT-4o (2024-11-20) 128K.
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-4o (2024-11-20) | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 4 | 1 | ||
| All shared boards | 11 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 18.2% | Only GPT-4o (2024-11-20) measured |
| LMArena WebDev | 1499 | Not measured | Only Gemini 3.5 Flash measured |
| LiveCodeBench (Vals AI) | 87.6% | 43.4% | 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 |
| SciCode | 53.9% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | Not measured | Only Gemini 3.5 Flash measured |
| GSO-Bench | Not measured | 0.0% | Only GPT-4o (2024-11-20) measured |
| WeirdML | 62.6% | 25.1% | Gemini 3.5 Flash |
| Cybench | Not measured | 12.5% | Only GPT-4o (2024-11-20) measured |
| ALE-Bench | 911.0 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: coding | 1493 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: software and IT | 1494 | Not measured | Only Gemini 3.5 Flash measured |
| 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 |
| SciCode | 12.3% | 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 index10
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 8.4 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | Not measured | Only Gemini 3.5 Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | 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 | 1174 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: agents building Android apps | 1185 | 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 | 1093 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: PowerPoint slides | 1156 | 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% | Not measured | Only Gemini 3.5 Flash measured |
| APEX-Agents | 27.5% | Not measured | Only Gemini 3.5 Flash measured |
| TheAgentCompany | Not measured | 8.6% | Only GPT-4o (2024-11-20) measured |
| Vending-Bench 2 | $5,396 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | 53.8% | Gemini 3.5 Flash |
| MMMU Pro (Vals AI) | 88.3% | 62.2% | Gemini 3.5 Flash |
| GPQA Diamond (Artificial Analysis run) | 92.2% | Not measured | Only Gemini 3.5 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | Not measured | Only Gemini 3.5 Flash measured |
| GPQA Diamond (Epoch AI run) | 92.8% | 47.9% | Gemini 3.5 Flash |
| ARC-AGI | 92.5% | Not measured | Only Gemini 3.5 Flash measured |
| ARC-AGI-2 | 72.1% | Not measured | Only Gemini 3.5 Flash measured |
| SimpleBench | 76.7% | Not measured | Only Gemini 3.5 Flash measured |
| Chess puzzles | 45.0% | Not measured | Only Gemini 3.5 Flash measured |
| 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 |
| VPCT | Not measured | 40.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: hard prompts | 1488 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: expert | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: hard prompts in English | 1483 | Not measured | Only Gemini 3.5 Flash measured |
| 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 index10
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| AIME | Not measured | 11.9% | Only GPT-4o (2024-11-20) measured |
| MATH 500 (Vals AI) | Not measured | 74.0% | Only GPT-4o (2024-11-20) measured |
| MGSM (Vals AI) | Not measured | 90.4% | Only GPT-4o (2024-11-20) measured |
| OTIS Mock AIME | 95.6% | 6.3% | 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 | 49.8% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: math | 1504 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: mathematical work | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | 72.6% | Gemini 3.5 Flash |
| LegalBench (Vals AI) | 83.6% | 82.2% | Gemini 3.5 Flash |
| CaseLaw (Vals AI) | Not measured | 59.7% | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 64.7% | 45.9% | Gemini 3.5 Flash |
| TaxEval (Vals AI) | 74.4% | 74.5% | GPT-4o (2024-11-20) |
| MortgageTax (Vals AI) | 68.1% | 57.4% | Gemini 3.5 Flash |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| SimpleQA Verified | 66.2% | Not measured | Only Gemini 3.5 Flash measured |
| GeoBench | Not measured | 71.0% | Only GPT-4o (2024-11-20) measured |
| LMArena Text: science | 1495 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: medicine and healthcare | 1483 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: legal and government | 1488 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: business and finance | 1467 | Not measured | Only Gemini 3.5 Flash measured |
| FACTS Benchmark Suite | 63.1% | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | Leader |
|---|---|---|---|
| Design Arena: websites | 1267 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: UI components | 1270 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: game dev | 1263 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: data viz | 1237 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: 3D | 1243 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: all code categories | 1266 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: SVG | 1253 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: ASCII art | 1254 | Not measured | Only Gemini 3.5 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index34 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-4o (2024-11-20) | 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% | Not measured | Only Gemini 3.5 Flash measured |
| Long context reasoning (AA-LCR) | 74.3% | Not measured | Only Gemini 3.5 Flash measured |
| DTBench | 94.7% | Not measured | Only Gemini 3.5 Flash measured |
| LMCA | 47.1% | Not measured | Only Gemini 3.5 Flash measured |
| Creative writing (Lech Mazur) | Not measured | 81.8% | Only GPT-4o (2024-11-20) measured |
| Surface Evolver Bench | 58.1% | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text | 1482 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: instruction following | 1467 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: creative writing | 1470 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision | 1308 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: English | 1481 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: non-English | 1475 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Chinese | 1527 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: German | 1494 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Japanese | 1486 | Not measured | Only Gemini 3.5 Flash measured |
| 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 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: Polish | 1516 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: multi-turn | 1481 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: longer queries | 1483 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: writing and language | 1478 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Text: entertainment and media | 1459 | Not measured | Only Gemini 3.5 Flash measured |
| LMArena Vision: English | 1305 | Not measured | Only Gemini 3.5 Flash measured |
| 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-4o (2024-11-20) | 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-4o (2024-11-20)$0.0075Fits in one request
Gemini 3.5 Flash costs less
Repository review
50K input + 3K output tokens
Gemini 3.5 Flash$0.102Fits in one request
GPT-4o (2024-11-20)$0.155Fits in one request
Gemini 3.5 Flash costs less
Agent loop
200K input + 10K output tokens
Gemini 3.5 Flash$0.390Fits in one request
GPT-4o (2024-11-20)$0.600Exceeds the context window
Gemini 3.5 Flash 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-4o (2024-11-20) |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | May 19, 2026 | November 20, 2024 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $1.50 | $2.50 |
| Output $/Mtok | $9.00 | $10.00 |
| Coding index | 39.8 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $1.21 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GPT-4o (2024-11-20) for coding?
Only Gemini 3.5 Flash has a coding score, 39.8 on the Vibe Coding Index; GPT-4o (2024-11-20) has none, so the two cannot be ranked on coding. Pick GPT-4o (2024-11-20) for work measured by TaxEval (Vals AI), where it leads.
Which is cheaper, Gemini 3.5 Flash or GPT-4o (2024-11-20)?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GPT-4o (2024-11-20) costs $2.50 and $10.00. Cost per solved task is known for Gemini 3.5 Flash only.
Which has the bigger context window, Gemini 3.5 Flash or GPT-4o (2024-11-20)?
Gemini 3.5 Flash, with 1.05M tokens against 128K.
How many benchmarks is the Gemini 3.5 Flash vs GPT-4o (2024-11-20) comparison based on?
12 benchmarks have a result for both models as of October 22, 2026. Gemini 3.5 Flash's coding index rests on 4 independent benchmarks and GPT-4o (2024-11-20)'s on 0. Where only one model has a result, the page says so instead of naming a winner.