Gemini 3.5 Flash vs GPT-5.3-Codex
Updated October 21, 2026. 31 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Gemini 3.5 Flash for coding: it scores 42.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when a solved task costs $0.59 against $1.21, or when what matters is Vibe Code Bench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 31
- Gemini 3.5 Flash only
- 79
- GPT-5.3-Codex only
- 3
- Like-for-like categories
- 6 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 29.5.
One benchmark behind each score
Intelligence
AA Intelligence Index
Gemini 3.5 Flash
Gemini 3.5 Flash leads, 33.6 against 32.5.
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, 1268 against 1163.
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-5.3-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.3-Codex
Gemini 3.5 Flash $1.21, GPT-5.3-Codex $0.59.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 3.5 Flash 42.8 from 1 benchmark, GPT-5.3-Codex 29.5 from 1 benchmark.
- Widest category
- Web design, 15.6 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: 3D: Gemini 3.5 Flash 1244 against GPT-5.3-Codex 1008, 29.5 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.5 Flash $9.00 against GPT-5.3-Codex $14.00 per million output tokens, 1.6x apart.
- Context window
- Gemini 3.5 Flash takes 1.05M tokens, GPT-5.3-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-5.3-Codex | Ties |
|---|---|---|---|---|
| Coding | 6 | 4 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 5 | 2 | ||
| Reasoning | 2 | 0 | ||
| Web design | 8 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 23 | 8 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index64
| Benchmark | Gemini 3.5 Flash | GPT-5.3-Codex | Leader |
|---|---|---|---|
| LMArena WebDev | 1499 | 1407 | Gemini 3.5 Flash |
| LiveCodeBench (Vals AI) | 87.6% | 87.3% | Gemini 3.5 Flash |
| SWE-bench Verified (Vals AI) | 78.8% | 78.0% | Gemini 3.5 Flash |
| Terminal-Bench 4 (Vals AI) | 6.1% | Not measured | Only Gemini 3.5 Flash measured |
| Vibe Code Bench (Vals AI) | 48.7% | 61.8% | GPT-5.3-Codex |
| 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 |
| IOI (Vals AI) | Not measured | 53.8% | Only GPT-5.3-Codex 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% | 53.0% | GPT-5.3-Codex |
| Terminal-Bench | Not measured | 78.4% | Only GPT-5.3-Codex measured |
| WeirdML | 62.6% | 79.3% | GPT-5.3-Codex |
| ALE-Bench | 911.0 | 1655.2 | GPT-5.3-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 | 1395 | Gemini 3.5 Flash |
| LMArena WebDev: HTML | 1522 | 1422 | Gemini 3.5 Flash |
| LMArena WebDev: from an image | 1504 | 1443 | Gemini 3.5 Flash |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemini 3.5 Flash | GPT-5.3-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.6 | 32.5 | Gemini 3.5 Flash |
| Epoch Capabilities Index | 154.9 | 157.1 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | Gemini 3.5 Flash | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1202 | 1033 | Gemini 3.5 Flash |
| Design Arena: agents building full-stack apps | 1197 | 983 | Gemini 3.5 Flash |
| Design Arena: agents building mobile apps | 1175 | 1054 | Gemini 3.5 Flash |
| Design Arena: agents building Android apps | 1190 | 1084 | Gemini 3.5 Flash |
| Design Arena: agents building Godot games | 1098 | 1124 | GPT-5.3-Codex |
| 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% | 86.0% | Gemini 3.5 Flash |
| APEX-Agents | 27.5% | Not measured | Only Gemini 3.5 Flash measured |
| METR time horizons | Not measured | 74.5% | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,396 | $5,940 | GPT-5.3-Codex |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.5 Flash | GPT-5.3-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% | 91.5% | Gemini 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 42.7% | 42.5% | 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 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-5.3-Codex | Leader |
|---|---|---|---|
| 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 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Gemini 3.5 Flash | GPT-5.3-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 |
| 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 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | Gemini 3.5 Flash | GPT-5.3-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1268 | 1163 | Gemini 3.5 Flash |
| Design Arena: UI components | 1272 | 1137 | Gemini 3.5 Flash |
| Design Arena: game dev | 1263 | 1162 | Gemini 3.5 Flash |
| Design Arena: data viz | 1236 | 1164 | Gemini 3.5 Flash |
| Design Arena: 3D | 1244 | 1008 | Gemini 3.5 Flash |
| Design Arena: all code categories | 1266 | 1145 | Gemini 3.5 Flash |
| Design Arena: SVG | 1254 | 1129 | Gemini 3.5 Flash |
| Design Arena: ASCII art | 1253 | 1151 | Gemini 3.5 Flash |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemini 3.5 Flash | GPT-5.3-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% | 75.4% | Gemini 3.5 Flash |
| Long context reasoning (AA-LCR) | 74.3% | 83.3% | GPT-5.3-Codex |
| 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-5.3-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
Gemini 3.5 Flash costs less
Agent loop
200K input + 10K output tokens
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-5.3-Codex |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | May 19, 2026 | February 24, 2026 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.50 | $1.75 |
| Output $/Mtok | $9.00 | $14.00 |
| Coding index | 42.8 | 29.5 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $1.21 | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.5 Flash better than GPT-5.3-Codex for coding?
Pick Gemini 3.5 Flash for coding: it scores 42.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when a solved task costs $0.59 against $1.21, or when what matters is Vibe Code Bench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Gemini 3.5 Flash or GPT-5.3-Codex?
Gemini 3.5 Flash costs $1.50 per million input tokens and $9.00 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, Gemini 3.5 Flash costs $1.21 and GPT-5.3-Codex $0.59.
Which has the bigger context window, Gemini 3.5 Flash or GPT-5.3-Codex?
Gemini 3.5 Flash, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 3.5 Flash vs GPT-5.3-Codex comparison based on?
31 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-5.3-Codex's on 1. Where only one model has a result, the page says so instead of naming a winner.