Gemini 2.5 Pro vs GPT-5
GPT-5 leads Gemini 2.5 Pro on coding, 38.8 to 19.1 on the Vibe Coding Index, at about the same output price ($10.00 vs $10.00 per million). Updated October 21, 2026; 85 benchmarks have a result for both.
Gemini 2.5 Pro or GPT-5: which to pick
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.
Pick Gemini 2.5 Pro for a larger context window (1.05M against 400K tokens) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 85
- Gemini 2.5 Pro only
- 5
- GPT-5 only
- 28
- Like-for-like categories
- 8 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
GPT-5
GPT-5 scores 38.8 against 19.1.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5
GPT-5 leads, 23.0 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 2.5 Pro has a AA Agentic Index result, 1.6.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
GPT-5
GPT-5 leads, 1184 against 1167.
Same board, both measured
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Gemini 2.5 Pro $0.0063, GPT-5 $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5
Gemini 2.5 Pro $0.64, GPT-5 $0.39.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 2.5 Pro 19.1 from 3 benchmarks, GPT-5 38.8 from 3 benchmarks.
- Widest category
- Agentic work, 25.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- BrowseComp-Plus: Gemini 2.5 Pro 29.5% against GPT-5 80.0%, 50.5 points apart on a 0 to 100 scale.
- Output price
- Both cost $10.00 per million output tokens.
- Context window
- Gemini 2.5 Pro takes 1.05M tokens, GPT-5 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 Pro | Bars | GPT-5 | Ties |
|---|---|---|---|---|
| Coding | 2 | 11 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 4 | ||
| Reasoning | 5 | 6 | ||
| Math | 2 | 5 | ||
| Knowledge | 7 | 3 | ||
| Web design | 1 | 4 | 1 tie | |
| General | 25 | 7 | ||
| All shared boards | 42 | 42 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index211
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 88.0% | Only GPT-5 measured |
| SWE-bench Verified | 53.6% | 74.4% | GPT-5 |
| LMArena WebDev | 1226 | 1418 | GPT-5 |
| AA Coding Index | 33.3 | 37.8 | GPT-5 |
| LiveCodeBench (Vals AI) | Not measured | 85.9% | Only GPT-5 measured |
| SWE-bench Verified (Vals AI) | 54.4% | 69.0% | GPT-5 |
| Vibe Code Bench (Vals AI) | 0.4% | 20.1% | GPT-5 |
| LiveCodeBench (Artificial Analysis run) | 80.1% | 84.6% | GPT-5 |
| SciCode | 46.3% | Not measured | Only Gemini 2.5 Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 37.9% | GPT-5 |
| Terminal-Bench | 32.6% | 49.6% | GPT-5 |
| GSO-Bench | 3.9% | 6.9% | GPT-5 |
| WeirdML | 54.0% | 60.7% | GPT-5 |
| ALE-Bench | Not measured | 807.6 | Only GPT-5 measured |
| LMArena Text: coding | 1452 | 1436 | Gemini 2.5 Pro |
| LMArena Text: software and IT | 1459 | 1420 | Gemini 2.5 Pro |
| LMArena WebDev: HTML | 1235 | 1427 | GPT-5 |
| LMArena WebDev: from an image | 1285 | Not measured | Only Gemini 2.5 Pro measured |
| SciCode | Not measured | 0.0% | Only GPT-5 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 23.0 | GPT-5 |
| Epoch Capabilities Index | 145.1 | 150.0 | GPT-5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Gemini 2.5 Pro measured |
| τ²-Bench Telecom | 54.1% | 86.5% | GPT-5 |
| METR time horizons | Not measured | 69.6% | Only GPT-5 measured |
| Remote Labor Index | Not measured | 1.7% | Only GPT-5 measured |
| GDPval | 23.3% | 34.8% | GPT-5 |
| BALROG | Not measured | 32.8% | Only GPT-5 measured |
| DeepResearch Bench | 41.5% | 49.6% | GPT-5 |
| Vending-Bench 2 | $574 | Not measured | Only Gemini 2.5 Pro measured |
| BrowseComp-Plus | 29.5% | 80.0% | GPT-5 |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index56
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 85.6% | Only GPT-5 measured |
| MMMU Pro (Vals AI) | Not measured | 81.5% | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 84.4% | 85.4% | GPT-5 |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 28.5% | GPT-5 |
| GPQA Diamond (Epoch AI run) | 85.3% | 86.2% | GPT-5 |
| ARC-AGI | 41.0% | 65.7% | GPT-5 |
| ARC-AGI-2 | 4.9% | 9.9% | GPT-5 |
| SimpleBench | Not measured | 56.7% | Only GPT-5 measured |
| Chess puzzles | Not measured | 36.0% | Only GPT-5 measured |
| Mystery game puzzles | Not measured | 23.0% | Only GPT-5 measured |
| EBR-bench | Not measured | 12.7% | Only GPT-5 measured |
| VPCT | Not measured | 66.0% | Only GPT-5 measured |
| LMArena Text: hard prompts | 1455 | 1415 | Gemini 2.5 Pro |
| LMArena Text: expert | 1452 | 1419 | Gemini 2.5 Pro |
| LMArena Text: hard prompts in English | 1453 | 1419 | Gemini 2.5 Pro |
| LMArena Vision: diagrams | 1274 | 1252 | Gemini 2.5 Pro |
| LMArena Vision: homework | 1281 | 1278 | Gemini 2.5 Pro |
| Kagi LLM Benchmark | 70.3% | 72.7% | GPT-5 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| AIME | Not measured | 93.4% | Only GPT-5 measured |
| MATH 500 (Vals AI) | Not measured | 96.0% | Only GPT-5 measured |
| MGSM (Vals AI) | Not measured | 92.8% | Only GPT-5 measured |
| AA Math Index | 87.7 | 94.3 | GPT-5 |
| AIME 2025 (Artificial Analysis run) | 87.7% | 94.3% | GPT-5 |
| OTIS Mock AIME | 84.7% | 91.4% | GPT-5 |
| FrontierMath, tiers 1 to 3 | 24.6% | 55.4% | GPT-5 |
| FrontierMath, tier 4 | 0.0% | 22.0% | GPT-5 |
| ProofBench | Not measured | 18.0% | Only GPT-5 measured |
| MATH level 5 | Not measured | 98.1% | Only GPT-5 measured |
| LMArena Text: math | 1450 | 1407 | Gemini 2.5 Pro |
| LMArena Text: mathematical work | 1454 | 1405 | Gemini 2.5 Pro |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index73
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.5% | Only GPT-5 measured |
| LegalBench (Vals AI) | Not measured | 86.0% | Only GPT-5 measured |
| CaseLaw (Vals AI) | 63.9% | 66.5% | GPT-5 |
| CorpFin (Vals AI) | 60.8% | 61.1% | GPT-5 |
| TaxEval (Vals AI) | Not measured | 73.4% | Only GPT-5 measured |
| MortgageTax (Vals AI) | 68.9% | 65.5% | Gemini 2.5 Pro |
| MedQA (Vals AI) | Not measured | 96.3% | Only GPT-5 measured |
| MedCode (Vals AI) | 50.6% | 49.6% | Gemini 2.5 Pro |
| MMLU Pro (Artificial Analysis run) | 86.2% | 87.1% | GPT-5 |
| SimpleQA Verified | Not measured | 50.1% | Only GPT-5 measured |
| GeoBench | Not measured | 81.0% | Only GPT-5 measured |
| LMArena Text: science | 1480 | 1416 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1476 | 1413 | Gemini 2.5 Pro |
| LMArena Text: legal and government | 1480 | 1431 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1439 | 1400 | Gemini 2.5 Pro |
| FACTS Benchmark Suite | 61.5% | 56.5% | Gemini 2.5 Pro |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | 1184 | GPT-5 |
| Design Arena: UI components | 1140 | 1180 | GPT-5 |
| Design Arena: game dev | 1111 | 1185 | GPT-5 |
| Design Arena: data viz | 1221 | 1221 | Tie |
| Design Arena: 3D | 1081 | 1056 | Gemini 2.5 Pro |
| Design Arena: all code categories | 1152 | 1168 | GPT-5 |
| Design Arena: SVG | Not measured | 1189 | Only GPT-5 measured |
| Design Arena: ASCII art | Not measured | 1141 | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index257
| Benchmark | Gemini 2.5 Pro | GPT-5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.9% | 43.7% | GPT-5 |
| MedScribe (Vals AI) | 73.5% | 83.7% | GPT-5 |
| IFBench | 48.7% | 73.1% | GPT-5 |
| Long context reasoning (AA-LCR) | 69.0% | 78.2% | GPT-5 |
| DTBench | 82.4% | 90.7% | GPT-5 |
| LMCA | 34.8% | 40.0% | GPT-5 |
| Fiction.LiveBench | Not measured | 97.2% | Only GPT-5 measured |
| Creative writing (Lech Mazur) | 83.8% | 86.0% | GPT-5 |
| LMArena Text | 1458 | 1406 | Gemini 2.5 Pro |
| LMArena Text: instruction following | 1437 | 1388 | Gemini 2.5 Pro |
| LMArena Text: creative writing | 1454 | 1365 | Gemini 2.5 Pro |
| LMArena Vision | 1263 | 1232 | Gemini 2.5 Pro |
| LMArena Text: English | 1458 | 1409 | Gemini 2.5 Pro |
| LMArena Text: non-English | 1452 | 1397 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1507 | 1422 | Gemini 2.5 Pro |
| LMArena Text: German | 1487 | 1416 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1461 | 1409 | Gemini 2.5 Pro |
| LMArena Text: Korean | 1434 | 1360 | Gemini 2.5 Pro |
| LMArena Text: French | 1473 | 1409 | Gemini 2.5 Pro |
| LMArena Text: Spanish | 1474 | 1399 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1461 | 1407 | Gemini 2.5 Pro |
| LMArena Text: Polish | 1479 | 1431 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1453 | 1426 | Gemini 2.5 Pro |
| LMArena Text: longer queries | 1449 | 1399 | Gemini 2.5 Pro |
| LMArena Text: writing and language | 1454 | 1384 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1441 | 1379 | Gemini 2.5 Pro |
| LMArena Vision: English | 1261 | 1231 | Gemini 2.5 Pro |
| LMArena Vision: Chinese | 1300 | 1267 | Gemini 2.5 Pro |
| LMArena Vision: reading text | 1270 | 1246 | Gemini 2.5 Pro |
| LMArena Vision: humor | 1262 | 1219 | Gemini 2.5 Pro |
| LMArena Vision: captioning | 1304 | 1212 | Gemini 2.5 Pro |
| LMArena Vision: recognizing things | 1283 | 1263 | Gemini 2.5 Pro |
| LMArena Vision: creative writing | 1265 | 1210 | Gemini 2.5 Pro |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro 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 2.5 Pro | GPT-5 | 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
Same cost
Repository review
50K input + 3K output tokens
Same cost
Agent loop
200K input + 10K output tokens
Same cost
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 Pro | OpenAI GPT-5 |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | August 7, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $1.25 | $1.25 |
| Output $/Mtok | $10.00 | $10.00 |
| Coding index | 19.1 | 38.8 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $0.64 | $0.39 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than GPT-5 for coding?
Pick GPT-5 for coding: it scores 38.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for a larger context window (1.05M against 400K tokens) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Gemini 2.5 Pro or GPT-5?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; GPT-5 costs $1.25 and $10.00. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and GPT-5 $0.39.
Which has the bigger context window, Gemini 2.5 Pro or GPT-5?
Gemini 2.5 Pro, with 1.05M tokens against 400K.
How many benchmarks is the Gemini 2.5 Pro vs GPT-5 comparison based on?
85 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and GPT-5's on 3. Where only one model has a result, the page says so instead of naming a winner.