Gemini 2.5 Flash vs GPT-5.2-Codex
Updated October 21, 2026. 9 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash.
Pick Gemini 2.5 Flash when output tokens cost 5.6x less, or when the context window is larger, 1.05M against 400K tokens.
- Shared results
- 9
- Gemini 2.5 Flash only
- 60
- GPT-5.2-Codex only
- 13
- Like-for-like categories
- 5 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
GPT-5.2-Codex
GPT-5.2-Codex scores 55.1 against 28.7.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 15.5.
Same board, both measured
Web design
Design Arena: websites
No call
Only Gemini 2.5 Flash has a Design Arena: websites result, 1074.
Directional only
Long documents
Listed context window
Gemini 2.5 Flash
Gemini 2.5 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 2.5 Flash
Gemini 2.5 Flash $0.0015, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2-Codex
Gemini 2.5 Flash $0.466, GPT-5.2-Codex $0.43.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 2.5 Flash 28.7 from 1 benchmark, GPT-5.2-Codex 55.1 from 2 benchmarks.
- Widest category
- Agentic work, 60.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Gemini 2.5 Flash 31.6% against GPT-5.2-Codex 92.1%, 60.5 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Flash $2.50 against GPT-5.2-Codex $14.00 per million output tokens, 5.6x apart.
- Context window
- Gemini 2.5 Flash takes 1.05M tokens, GPT-5.2-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 2.5 Flash | Bars | GPT-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | 28.7% | 72.8% | GPT-5.2-Codex |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | Not measured | 1339 | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 88.0% | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only Gemini 2.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | 37.1% | GPT-5.2-Codex |
| Terminal-Bench | 17.1% | 66.5% | GPT-5.2-Codex |
| ALE-Bench | Not measured | 1299.9 | Only GPT-5.2-Codex measured |
| LMArena Text: coding | 1423 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: software and IT | 1425 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena WebDev: React | Not measured | 1329 | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | Not measured | 1348 | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.5 | 28.5 | GPT-5.2-Codex |
| Epoch Capabilities Index | 142.9 | Not measured | Only Gemini 2.5 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 31.6% | 92.1% | GPT-5.2-Codex |
| BALROG | 33.5% | Not measured | Only Gemini 2.5 Flash measured |
| Vending-Bench 2 | $549 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.0% | 89.9% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 12.1% | 35.7% | GPT-5.2-Codex |
| ARC-AGI | 33.3% | Not measured | Only Gemini 2.5 Flash measured |
| ARC-AGI-2 | 2.5% | Not measured | Only Gemini 2.5 Flash measured |
| SimpleBench | 41.2% | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: hard prompts | 1422 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: expert | 1426 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: hard prompts in English | 1420 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: diagrams | 1245 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: homework | 1244 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Math Index | 73.3 | Not measured | Only Gemini 2.5 Flash measured |
| AIME 2025 (Artificial Analysis run) | 73.3% | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: math | 1411 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: mathematical work | 1422 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 40.4% | Not measured | Only Gemini 2.5 Flash measured |
| MMLU Pro (Artificial Analysis run) | 83.2% | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: science | 1433 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: medicine and healthcare | 1428 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: legal and government | 1430 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: business and finance | 1404 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1074 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: UI components | 1005 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: game dev | 1005 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: data viz | 1060 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: 3D | 1021 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: all code categories | 1059 | Not measured | Only Gemini 2.5 Flash measured |
| Design Arena: SVG | 1018 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Flash | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 44.8% | Not measured | Only Gemini 2.5 Flash measured |
| MedScribe (Vals AI) | 83.0% | Not measured | Only Gemini 2.5 Flash measured |
| IFBench | 50.3% | 77.6% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 65.3% | 82.3% | GPT-5.2-Codex |
| DTBench | 76.5% | Not measured | Only Gemini 2.5 Flash measured |
| LMCA | 27.5% | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text | 1417 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: instruction following | 1405 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: creative writing | 1400 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision | 1235 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: English | 1418 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: non-English | 1409 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Chinese | 1449 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: German | 1417 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Japanese | 1396 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Korean | 1385 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: French | 1432 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Spanish | 1422 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Russian | 1415 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: Polish | 1413 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: multi-turn | 1408 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: longer queries | 1419 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: writing and language | 1405 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Text: entertainment and media | 1393 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: English | 1239 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: Chinese | 1284 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: reading text | 1237 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: humor | 1225 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: captioning | 1277 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: recognizing things | 1252 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: creative writing | 1241 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5.2-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 2.5 Flash costs less
Repository review
50K input + 3K output tokens
Gemini 2.5 Flash costs less
Agent loop
200K input + 10K output tokens
Gemini 2.5 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 2.5 Flash | OpenAI GPT-5.2-Codex |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | January 14, 2026 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.30 | $1.75 |
| Output $/Mtok | $2.50 | $14.00 |
| Coding index | 28.7 | 55.1 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.466 | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Flash better than GPT-5.2-Codex for coding?
Pick GPT-5.2-Codex for coding: it scores 55.1 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash. Pick Gemini 2.5 Flash when output tokens cost 5.6x less, or when the context window is larger, 1.05M against 400K tokens.
Which is cheaper, Gemini 2.5 Flash or GPT-5.2-Codex?
Gemini 2.5 Flash costs $0.30 per million input tokens and $2.50 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, Gemini 2.5 Flash costs $0.466 and GPT-5.2-Codex $0.43.
Which has the bigger context window, Gemini 2.5 Flash or GPT-5.2-Codex?
Gemini 2.5 Flash, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 2.5 Flash vs GPT-5.2-Codex comparison based on?
9 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Flash's coding index rests on 1 independent benchmark and GPT-5.2-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.