Gemini 2.5 Flash vs GPT-6 Sol
Updated October 21, 2026. 53 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-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash.
Pick Gemini 2.5 Flash when output tokens cost 4.0x less, or when what matters is LMArena Text: hard prompts or LMArena Text: hard prompts in English, where it leads.
- Shared results
- 53
- Gemini 2.5 Flash only
- 16
- GPT-6 Sol only
- 47
- Like-for-like categories
- 8 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-6 Sol
GPT-6 Sol scores 82.8 against 28.7.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6 Sol
GPT-6 Sol leads, 47.6 against 15.5.
Same board, both measured
Web design
Design Arena: websites
GPT-6 Sol
GPT-6 Sol leads, 1292 against 1074.
Same board, both measured
Long documents
Listed context window
GPT-6 Sol
GPT-6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 2.5 Flash
Gemini 2.5 Flash $0.0015, GPT-6 Sol $0.0070 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 28.7 from 1 benchmark, GPT-6 Sol 82.8 from 2 benchmarks.
- Widest category
- Agentic work, 86.7 points apart on average over 1 shared benchmark.
- Widest single gap
- ARC-AGI-2: Gemini 2.5 Flash 2.5% against GPT-6 Sol 89.6%, 87.0 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Flash $2.50 against GPT-6 Sol $10.00 per million output tokens, 4.0x apart.
- Context window
- Gemini 2.5 Flash takes 1.05M tokens, GPT-6 Sol 1.05M.
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-6 Sol | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 6 | ||
| Math | 1 | 1 | ||
| Knowledge | 4 | 1 | ||
| Web design | 0 | 7 | ||
| General | 18 | 8 | ||
| All shared boards | 25 | 28 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| SWE-bench Verified | 28.7% | Not measured | Only Gemini 2.5 Flash measured |
| LiveBench coding | Not measured | 93.1% | Only GPT-6 Sol measured |
| LMArena WebDev | Not measured | 1683 | Only GPT-6 Sol measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 44.4% | Only GPT-6 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 87.8% | Only GPT-6 Sol measured |
| Code migration (Vals AI) | Not measured | 57.2% | Only GPT-6 Sol measured |
| ProgramBench (Vals AI) | Not measured | 2.0% | Only GPT-6 Sol measured |
| IOI (Vals AI) | Not measured | 82.6% | Only GPT-6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 83.2% | Only GPT-6 Sol measured |
| CyberBench (Vals AI) | Not measured | 78.0% | Only GPT-6 Sol measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only Gemini 2.5 Flash measured |
| SciCode | Not measured | 57.6% | Only GPT-6 Sol 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 |
| FrontierCode | Not measured | 49.3% | Only GPT-6 Sol measured |
| ALE-Bench | Not measured | 2462.0 | Only GPT-6 Sol measured |
| LMArena Text: coding | 1423 | 1448 | GPT-6 Sol |
| LMArena Text: software and IT | 1425 | 1433 | GPT-6 Sol |
| LMArena WebDev: React | Not measured | 1685 | Only GPT-6 Sol measured |
| LMArena WebDev: HTML | Not measured | 1685 | Only GPT-6 Sol measured |
| LMArena WebDev: from an image | Not measured | 1660 | Only GPT-6 Sol measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.5 | 47.6 | GPT-6 Sol |
| Epoch Capabilities Index | 142.9 | 163.7 | GPT-6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1232 | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | Not measured | 1238 | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | Not measured | 1216 | Only GPT-6 Sol measured |
| LiveBench research | Not measured | 69.3% | Only GPT-6 Sol measured |
| LiveBench browser | Not measured | 92.0% | Only GPT-6 Sol measured |
| LiveBench long agentic | Not measured | 66.9% | Only GPT-6 Sol measured |
| SREBench (Vals AI) | Not measured | 30.5% | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | Not measured | 49.0% | Only GPT-6 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.0% | Only GPT-6 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 28.9% | Only GPT-6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 71.5% | Only GPT-6 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 74.8% | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 30.0% | Only GPT-6 Sol measured |
| Vals Science Index | Not measured | 45.0% | Only GPT-6 Sol measured |
| τ²-Bench Telecom | 31.6% | Not measured | Only Gemini 2.5 Flash measured |
| APEX-Agents | Not measured | 54.3% | Only GPT-6 Sol measured |
| BALROG | 33.5% | Not measured | Only Gemini 2.5 Flash measured |
| SpireClimb | Not measured | 7.5% | Only GPT-6 Sol measured |
| Vending-Bench 2 | $549 | $14,428 | GPT-6 Sol |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index26
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 88.7% | Only GPT-6 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 30.2% | Only GPT-6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 79.0% | Not measured | Only Gemini 2.5 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 12.1% | 47.9% | GPT-6 Sol |
| GPQA Diamond (Epoch AI run) | Not measured | 94.3% | Only GPT-6 Sol measured |
| ARC-AGI | 33.3% | 95.5% | GPT-6 Sol |
| ARC-AGI-2 | 2.5% | 89.6% | GPT-6 Sol |
| SimpleBench | 41.2% | Not measured | Only Gemini 2.5 Flash measured |
| Chess puzzles | Not measured | 56.0% | Only GPT-6 Sol measured |
| Mystery game puzzles | Not measured | 56.0% | Only GPT-6 Sol measured |
| EBR-bench | Not measured | 53.3% | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | 1422 | 1422 | Gemini 2.5 Flash |
| LMArena Text: expert | 1426 | 1437 | GPT-6 Sol |
| LMArena Text: hard prompts in English | 1420 | 1420 | Gemini 2.5 Flash |
| LMArena Vision: diagrams | 1245 | 1270 | GPT-6 Sol |
| LMArena Vision: homework | 1244 | 1286 | GPT-6 Sol |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | 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 |
| OTIS Mock AIME | Not measured | 100.0% | Only GPT-6 Sol measured |
| FrontierMath, tiers 1 to 3 | Not measured | 89.8% | Only GPT-6 Sol measured |
| FrontierMath, tier 4 | Not measured | 90.0% | Only GPT-6 Sol measured |
| ProofBench | Not measured | 83.0% | Only GPT-6 Sol measured |
| LMArena Text: math | 1411 | 1407 | Gemini 2.5 Flash |
| LMArena Text: mathematical work | 1422 | 1424 | GPT-6 Sol |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 40.4% | 47.1% | GPT-6 Sol |
| MMLU Pro (Artificial Analysis run) | 83.2% | Not measured | Only Gemini 2.5 Flash measured |
| SimpleQA Verified | Not measured | 60.7% | Only GPT-6 Sol measured |
| LMArena Text: science | 1433 | 1389 | Gemini 2.5 Flash |
| LMArena Text: medicine and healthcare | 1428 | 1366 | Gemini 2.5 Flash |
| LMArena Text: legal and government | 1430 | 1396 | Gemini 2.5 Flash |
| LMArena Text: business and finance | 1404 | 1380 | Gemini 2.5 Flash |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1074 | 1292 | GPT-6 Sol |
| Design Arena: UI components | 1005 | 1292 | GPT-6 Sol |
| Design Arena: game dev | 1005 | 1313 | GPT-6 Sol |
| Design Arena: data viz | 1060 | 1269 | GPT-6 Sol |
| Design Arena: 3D | 1021 | 1322 | GPT-6 Sol |
| Design Arena: all code categories | 1059 | 1301 | GPT-6 Sol |
| Design Arena: SVG | 1018 | 1318 | GPT-6 Sol |
| Design Arena: ASCII art | Not measured | 1226 | Only GPT-6 Sol measured |
| LiveBench design | Not measured | 82.2% | Only GPT-6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index188
| Benchmark | Gemini 2.5 Flash | GPT-6 Sol | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.2% | Only GPT-6 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 56.6% | Only GPT-6 Sol measured |
| SAGE (Vals AI) | 44.8% | 44.8% | GPT-6 Sol |
| MedScribe (Vals AI) | 83.0% | 82.0% | Gemini 2.5 Flash |
| IFBench | 50.3% | Not measured | Only Gemini 2.5 Flash measured |
| Long context reasoning (AA-LCR) | 65.3% | 83.7% | GPT-6 Sol |
| DTBench | 76.5% | 97.3% | GPT-6 Sol |
| LMCA | 27.5% | 59.1% | GPT-6 Sol |
| Furniture assembly | Not measured | 58.3% | Only GPT-6 Sol measured |
| LMArena Text | 1417 | 1398 | Gemini 2.5 Flash |
| LMArena Text: instruction following | 1405 | 1413 | GPT-6 Sol |
| LMArena Text: creative writing | 1400 | 1378 | Gemini 2.5 Flash |
| LMArena Vision | 1235 | 1240 | GPT-6 Sol |
| LMArena Text: English | 1418 | 1396 | Gemini 2.5 Flash |
| LMArena Text: non-English | 1409 | 1388 | Gemini 2.5 Flash |
| LMArena Text: Chinese | 1449 | 1412 | Gemini 2.5 Flash |
| LMArena Text: German | 1417 | 1379 | Gemini 2.5 Flash |
| LMArena Text: Japanese | 1396 | 1377 | Gemini 2.5 Flash |
| LMArena Text: Korean | 1385 | 1336 | Gemini 2.5 Flash |
| LMArena Text: French | 1432 | 1399 | Gemini 2.5 Flash |
| LMArena Text: Spanish | 1422 | 1387 | Gemini 2.5 Flash |
| LMArena Text: Russian | 1415 | 1402 | Gemini 2.5 Flash |
| LMArena Text: Polish | 1413 | 1372 | Gemini 2.5 Flash |
| LMArena Text: multi-turn | 1408 | 1414 | GPT-6 Sol |
| LMArena Text: longer queries | 1419 | 1416 | Gemini 2.5 Flash |
| LMArena Text: writing and language | 1405 | 1389 | Gemini 2.5 Flash |
| LMArena Text: entertainment and media | 1393 | 1373 | Gemini 2.5 Flash |
| LMArena Vision: English | 1239 | 1229 | Gemini 2.5 Flash |
| LMArena Vision: Chinese | 1284 | Not measured | Only Gemini 2.5 Flash measured |
| LMArena Vision: reading text | 1237 | 1258 | GPT-6 Sol |
| 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 | 1219 | Gemini 2.5 Flash |
| 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 Flash | GPT-6 Sol | 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-6 Sol |
|---|---|---|
| Maker | OpenAI | |
| Weights | No public weights listed | No public weights listed |
| Released | June 17, 2025 | September 22, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.30 | $2.00 |
| Output $/Mtok | $2.50 | $10.00 |
| Coding index | 28.7 | 82.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.466 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Flash better than GPT-6 Sol for coding?
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash. Pick Gemini 2.5 Flash when output tokens cost 4.0x less, or when what matters is LMArena Text: hard prompts or LMArena Text: hard prompts in English, where it leads.
Which is cheaper, Gemini 2.5 Flash or GPT-6 Sol?
Gemini 2.5 Flash costs $0.30 per million input tokens and $2.50 per million output tokens; GPT-6 Sol costs $2.00 and $10.00. Cost per solved task is known for Gemini 2.5 Flash only.
Which has the bigger context window, Gemini 2.5 Flash or GPT-6 Sol?
GPT-6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the Gemini 2.5 Flash vs GPT-6 Sol comparison based on?
53 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-6 Sol's on 2. Where only one model has a result, the page says so instead of naming a winner.