Gemini 2.5 Flash vs GPT-5 Pro
Updated October 21, 2026. 4 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Gemini 2.5 Flash has the edge where output tokens cost 48.0x less, and where the context window is larger, 1.05M against 400K tokens.
- Shared results
- 4
- Gemini 2.5 Flash only
- 65
- GPT-5 Pro only
- 4
- Like-for-like categories
- 2 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Gemini 2.5 Flash has a AA Intelligence Index result, 15.5.
Directional only
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 Pro $0.075 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Flash has the number, $0.466.
Directional only
At a glance
- Coding index
- Gemini 2.5 Flash not benchmarked, GPT-5 Pro not benchmarked.
- Widest category
- Reasoning, 24.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- ARC-AGI: Gemini 2.5 Flash 33.3% against GPT-5 Pro 70.2%, 36.9 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Flash $2.50 against GPT-5 Pro $120.00 per million output tokens, 48.0x apart.
- Context window
- Gemini 2.5 Flash takes 1.05M tokens, GPT-5 Pro 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 Pro | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | 28.7% | Not measured | Only Gemini 2.5 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only Gemini 2.5 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | Not measured | Only Gemini 2.5 Flash measured |
| Terminal-Bench | 17.1% | Not measured | Only Gemini 2.5 Flash measured |
| WeirdML | Not measured | 60.4% | Only GPT-5 Pro 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 |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.5 | Not measured | Only Gemini 2.5 Flash measured |
| Epoch Capabilities Index | 142.9 | 150.6 | GPT-5 Pro |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 31.6% | Not measured | Only Gemini 2.5 Flash measured |
| 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 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.0% | Not measured | Only Gemini 2.5 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 12.1% | Not measured | Only Gemini 2.5 Flash measured |
| ARC-AGI | 33.3% | 70.2% | GPT-5 Pro |
| ARC-AGI-2 | 2.5% | 18.3% | GPT-5 Pro |
| SimpleBench | 41.2% | 61.6% | GPT-5 Pro |
| Chess puzzles | Not measured | 48.0% | Only GPT-5 Pro 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 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | 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 |
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GPT-5 Pro measured |
| FrontierMath, tier 4 | Not measured | 19.5% | Only GPT-5 Pro 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 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | 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 Pro | 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 | 1020 | Not measured | Only Gemini 2.5 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards, none shared
| Benchmark | Gemini 2.5 Flash | GPT-5 Pro | 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% | Not measured | Only Gemini 2.5 Flash measured |
| Long context reasoning (AA-LCR) | 65.3% | Not measured | Only Gemini 2.5 Flash measured |
| 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 Pro | 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 Pro |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | October 6, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.30 | $15.00 |
| Output $/Mtok | $2.50 | $120.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.466 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Flash better than GPT-5 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Gemini 2.5 Flash has the edge where output tokens cost 48.0x less, and where the context window is larger, 1.05M against 400K tokens.
Which is cheaper, Gemini 2.5 Flash or GPT-5 Pro?
Gemini 2.5 Flash costs $0.30 per million input tokens and $2.50 per million output tokens; GPT-5 Pro costs $15.00 and $120.00. Cost per solved task is known for Gemini 2.5 Flash only.
Which has the bigger context window, Gemini 2.5 Flash or GPT-5 Pro?
Gemini 2.5 Flash, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 2.5 Flash vs GPT-5 Pro comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Flash's coding index rests on 0 independent benchmarks and GPT-5 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.