Gemini 3.5 Flash vs GPT-5.1-Codex
Gemini 3.5 Flash leads GPT-5.1-Codex on coding, 39.8 to 15.3 on the Vibe Coding Index, at about the same output price ($9.00 vs $10.00 per million). Updated October 22, 2026; 20 benchmarks have a result for both.
Gemini 3.5 Flash or GPT-5.1-Codex: which to pick
Pick Gemini 3.5 Flash for coding: it scores 39.8 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex.
Pick GPT-5.1-Codex for a lower cost per solved task ($0.892 against $1.21) and work measured by ALE-Bench or Design Arena: agents building mobile apps, where it leads.
- Shared results
- 20
- Gemini 3.5 Flash only
- 92
- GPT-5.1-Codex only
- 6
- Like-for-like categories
- 6 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
Gemini 3.5 Flash
Gemini 3.5 Flash scores 39.8 against 15.3.
4 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 23.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Gemini 3.5 Flash has a GPQA Diamond result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.5 Flash has a MMLU Pro result, 89.5%.
Directional only
Web design
Design Arena: websites
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 1267 against 1161.
Same board, both measured
Long documents
Listed context window
Gemini 3.5 Flash
Gemini 3.5 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 3.5 Flash
Gemini 3.5 Flash $0.0060, GPT-5.1-Codex $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.1-Codex
Gemini 3.5 Flash $1.21, GPT-5.1-Codex $0.892.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 3.5 Flash 39.8 from 4 benchmarks, GPT-5.1-Codex 15.3 from 2 benchmarks.
- Widest category
- Coding, 18.8 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Gemini 3.5 Flash 48.7% against GPT-5.1-Codex 13.1%, 35.6 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GPT-5.1-Codex $10.00 per million output tokens, 1.1x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GPT-5.1-Codex 400K.
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-5.1-Codex | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 1 | ||
| Reasoning | 2 | 0 | ||
| Web design | 5 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 18 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 66.0% | Only GPT-5.1-Codex measured |
| LMArena WebDev | 1499 | 1337 | Gemini 3.5 Flash |
| LiveCodeBench (Vals AI) | 87.6% | 85.5% | 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% | 13.1% | Gemini 3.5 Flash |
| 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 | 84.9% | Only GPT-5.1-Codex measured |
| SciCode | 53.9% | Not measured | Only Gemini 3.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 46.2% | 34.8% | Gemini 3.5 Flash |
| Terminal-Bench | Not measured | 57.8% | Only GPT-5.1-Codex measured |
| WeirdML | 62.6% | Not measured | Only Gemini 3.5 Flash measured |
| ALE-Bench | 911.0 | 1244.9 | GPT-5.1-Codex |
| 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 | 1346 | Gemini 3.5 Flash |
| 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-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 23.7 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | Not measured | Only Gemini 3.5 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | 1018 | Gemini 3.5 Flash |
| Design Arena: agents building full-stack apps | 1197 | 1086 | Gemini 3.5 Flash |
| Design Arena: agents building mobile apps | 1174 | 1191 | GPT-5.1-Codex |
| 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 | 1154 | 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% | 83.0% | 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 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 92.7% | Not measured | Only Gemini 3.5 Flash measured |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only Gemini 3.5 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 92.2% | 86.0% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 25.7% | Gemini 3.5 Flash |
| GPQA Diamond (Epoch AI run) | 92.8% | Not measured | Only Gemini 3.5 Flash measured |
| 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 |
| 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 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 95.7 | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.7% | Only GPT-5.1-Codex measured |
| OTIS Mock AIME | 95.6% | Not measured | Only Gemini 3.5 Flash measured |
| 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 |
| 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 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 89.5% | Not measured | Only Gemini 3.5 Flash measured |
| LegalBench (Vals AI) | 83.6% | Not measured | Only Gemini 3.5 Flash measured |
| CorpFin (Vals AI) | 64.7% | Not measured | Only Gemini 3.5 Flash measured |
| TaxEval (Vals AI) | 74.4% | Not measured | Only Gemini 3.5 Flash measured |
| MortgageTax (Vals AI) | 68.1% | Not measured | Only Gemini 3.5 Flash measured |
| MedCode (Vals AI) | 55.8% | Not measured | Only Gemini 3.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| SimpleQA Verified | 66.2% | Not measured | Only Gemini 3.5 Flash 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 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1267 | 1161 | Gemini 3.5 Flash |
| Design Arena: UI components | 1269 | 1234 | Gemini 3.5 Flash |
| Design Arena: game dev | 1263 | 1137 | Gemini 3.5 Flash |
| Design Arena: data viz | 1236 | 1213 | Gemini 3.5 Flash |
| Design Arena: 3D | 1243 | Not measured | Only Gemini 3.5 Flash measured |
| Design Arena: all code categories | 1266 | 1152 | Gemini 3.5 Flash |
| 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 | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | 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% | 70.0% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 69.3% | Gemini 3.5 Flash |
| DTBench | 94.7% | Not measured | Only Gemini 3.5 Flash measured |
| LMCA | 47.1% | Not measured | Only Gemini 3.5 Flash 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-5.1-Codex | 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 costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex 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-5.1-Codex |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | May 19, 2026 | November 13, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.50 | $1.25 |
| Output $/Mtok | $9.00 | $10.00 |
| Coding index | 39.8 | 15.3 |
| Independent measurements behind it | 4 | 2 |
| Cost per solved task | $1.21 | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GPT-5.1-Codex for coding?
Pick Gemini 3.5 Flash for coding: it scores 39.8 on the Vibe Coding Index against 15.3 for GPT-5.1-Codex. Pick GPT-5.1-Codex for a lower cost per solved task ($0.892 against $1.21) and work measured by ALE-Bench or Design Arena: agents building mobile apps, where it leads.
Which is cheaper, Gemini 3.5 Flash or GPT-5.1-Codex?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Per solved SWE-bench Verified task, Gemini 3.5 Flash costs $1.21 and GPT-5.1-Codex $0.892.
Which has the bigger context window, Gemini 3.5 Flash or GPT-5.1-Codex?
Gemini 3.5 Flash, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 3.5 Flash vs GPT-5.1-Codex comparison based on?
20 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-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.