Gemini 2.5 Pro vs Kimi K3
Kimi K3 leads Gemini 2.5 Pro on coding, 62.3 to 19.1 on the Vibe Coding Index, and Gemini 2.5 Pro costs 1.5x less per output token ($10.00 vs $15.00 per million). Updated October 22, 2026; 62 benchmarks have a result for both.
Gemini 2.5 Pro or Kimi K3: which to pick
Pick Kimi K3 for coding: it scores 62.3 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro.
Pick Gemini 2.5 Pro for output tokens 1.5x cheaper ($10.00 vs $15.00 per million) and a lower cost per solved task ($0.64 against $0.81).
- Shared results
- 62
- Gemini 2.5 Pro only
- 28
- Kimi K3 only
- 55
- 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
Kimi K3
Kimi K3 scores 62.3 against 19.1.
3 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
Kimi K3
Kimi K3 leads, 43.6 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
Kimi K3
Kimi K3 leads, 50.0 against 1.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K3 has a GPQA Diamond result, 92.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K3 has a MMLU Pro result, 88.0%.
Directional only
Web design
Design Arena: websites
Kimi K3
Kimi K3 leads, 1334 against 1167.
Same board, both measured
General
Vals Index
No call
Only Kimi K3 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Gemini 2.5 Pro
Gemini 2.5 Pro $0.0063, Kimi K3 $0.011 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Gemini 2.5 Pro
Gemini 2.5 Pro $0.64, Kimi K3 $0.81.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 2.5 Pro 19.1 from 3 benchmarks, Kimi K3 62.3 from 6 benchmarks.
- Widest category
- Coding, 39.5 points apart on average over 10 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Gemini 2.5 Pro 0.4% against Kimi K3 85.0%, 84.6 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Pro $10.00 against Kimi K3 $15.00 per million output tokens, 1.5x apart.
- Context window
- Both take 1.05M tokens.
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 | Kimi K3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 10 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 0 | 8 | ||
| Math | 0 | 5 | ||
| Knowledge | 2 | 5 | ||
| Web design | 0 | 6 | ||
| General | 2 | 20 | ||
| All shared boards | 4 | 58 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index010
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| SWE-bench Verified | 53.6% | Not measured | Only Gemini 2.5 Pro measured |
| LiveBench coding | Not measured | 59.3% | Only Kimi K3 measured |
| LMArena WebDev | 1226 | 1655 | Kimi K3 |
| AA Coding Index | 33.3 | 76.2 | Kimi K3 |
| LiveCodeBench (Vals AI) | Not measured | 87.2% | Only Kimi K3 measured |
| SWE-bench Verified (Vals AI) | 54.4% | 93.4% | Kimi K3 |
| Terminal-Bench 4 (Vals AI) | Not measured | 17.2% | Only Kimi K3 measured |
| Vibe Code Bench (Vals AI) | 0.4% | 85.0% | Kimi K3 |
| Code migration (Vals AI) | Not measured | 16.1% | Only Kimi K3 measured |
| ProgramBench (Vals AI) | Not measured | 2.0% | Only Kimi K3 measured |
| IOI (Vals AI) | Not measured | 48.9% | Only Kimi K3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 80.9% | Only Kimi K3 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 44.2% | Only Kimi K3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 18.2% | Only Kimi K3 measured |
| CyberBench (Vals AI) | Not measured | 75.2% | Only Kimi K3 measured |
| LiveCodeBench (Artificial Analysis run) | 80.1% | Not measured | Only Gemini 2.5 Pro measured |
| SciCode | 46.3% | 59.5% | Kimi K3 |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | Not measured | Only Gemini 2.5 Pro measured |
| Terminal-Bench | 32.6% | Not measured | Only Gemini 2.5 Pro measured |
| FrontierCode | Not measured | 44.2% | Only Kimi K3 measured |
| FrontierSWE | Not measured | 25.9% | Only Kimi K3 measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro measured |
| WeirdML | 54.0% | 82.6% | Kimi K3 |
| ALE-Bench | Not measured | 1524.5 | Only Kimi K3 measured |
| LMArena Text: coding | 1452 | 1508 | Kimi K3 |
| LMArena Text: software and IT | 1459 | 1499 | Kimi K3 |
| LMArena WebDev: React | Not measured | 1670 | Only Kimi K3 measured |
| LMArena WebDev: HTML | 1235 | 1644 | Kimi K3 |
| LMArena WebDev: from an image | 1285 | 1579 | Kimi K3 |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 43.6 | Kimi K3 |
| Epoch Capabilities Index | 145.1 | 157.9 | Kimi K3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | 50.0 | Kimi K3 |
| Design Arena: agents building web apps | Not measured | 1295 | Only Kimi K3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1315 | Only Kimi K3 measured |
| Design Arena: agents building mobile apps | Not measured | 1269 | Only Kimi K3 measured |
| Design Arena: agents building Android apps | Not measured | 1253 | Only Kimi K3 measured |
| Design Arena: agents building Godot games | Not measured | 1223 | Only Kimi K3 measured |
| Design Arena: agents building games | Not measured | 1252 | Only Kimi K3 measured |
| Design Arena: HTML slides | Not measured | 1206 | Only Kimi K3 measured |
| Design Arena: PowerPoint slides | Not measured | 1247 | Only Kimi K3 measured |
| Design Arena: spreadsheets | Not measured | 1070 | Only Kimi K3 measured |
| LiveBench research | Not measured | 79.0% | Only Kimi K3 measured |
| LiveBench browser | Not measured | 16.0% | Only Kimi K3 measured |
| LiveBench long agentic | Not measured | 42.8% | Only Kimi K3 measured |
| Finance Agent (Vals AI) | Not measured | 53.1% | Only Kimi K3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 33.4% | Only Kimi K3 measured |
| Legal Research Bench (Vals AI) | Not measured | 46.1% | Only Kimi K3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 12.9% | Only Kimi K3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 66.7% | Only Kimi K3 measured |
| BioMysteryBench (Vals AI) | Not measured | 72.6% | Only Kimi K3 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 1.4% | Only Kimi K3 measured |
| Time Horizon Index (Vals AI) | Not measured | 10.5% | Only Kimi K3 measured |
| τ²-Bench Telecom | 54.1% | Not measured | Only Gemini 2.5 Pro measured |
| APEX-Agents | Not measured | 47.8% | Only Kimi K3 measured |
| GDPval | 23.3% | Not measured | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | 41.5% | Not measured | Only Gemini 2.5 Pro measured |
| PostTrainBench | Not measured | 32.0% | Only Kimi K3 measured |
| Vending-Bench 2 | $574 | $5,165 | Kimi K3 |
| BrowseComp-Plus | 29.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 92.9% | Only Kimi K3 measured |
| LiveBench reasoning | Not measured | 90.7% | Only Kimi K3 measured |
| MMMU Pro (Vals AI) | Not measured | 88.2% | Only Kimi K3 measured |
| GPQA Diamond (Artificial Analysis run) | 84.4% | 93.5% | Kimi K3 |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 46.9% | Kimi K3 |
| GPQA Diamond (Epoch AI run) | 85.3% | 93.1% | Kimi K3 |
| ARC-AGI | 41.0% | 94.5% | Kimi K3 |
| ARC-AGI-2 | 4.9% | 60.4% | Kimi K3 |
| SimpleBench | Not measured | 60.7% | Only Kimi K3 measured |
| Chess puzzles | Not measured | 37.0% | Only Kimi K3 measured |
| Mystery game puzzles | Not measured | 26.0% | Only Kimi K3 measured |
| LMArena Text: hard prompts | 1455 | 1495 | Kimi K3 |
| LMArena Text: expert | 1452 | 1520 | Kimi K3 |
| LMArena Text: hard prompts in English | 1453 | 1496 | Kimi K3 |
| LMArena Vision: diagrams | 1274 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | 1281 | Not measured | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | 70.3% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | 84.7% | 97.2% | Kimi K3 |
| FrontierMath, tiers 1 to 3 | 24.6% | 72.2% | Kimi K3 |
| FrontierMath, tier 4 | 0.0% | 39.0% | Kimi K3 |
| ProofBench | Not measured | 87.0% | Only Kimi K3 measured |
| LMArena Text: math | 1450 | 1494 | Kimi K3 |
| LMArena Text: mathematical work | 1454 | 1505 | Kimi K3 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.0% | Only Kimi K3 measured |
| LegalBench (Vals AI) | Not measured | 86.2% | Only Kimi K3 measured |
| CaseLaw (Vals AI) | 63.9% | Not measured | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | 60.8% | 71.6% | Kimi K3 |
| TaxEval (Vals AI) | Not measured | 75.7% | Only Kimi K3 measured |
| MortgageTax (Vals AI) | 68.9% | 66.3% | Gemini 2.5 Pro |
| MedCode (Vals AI) | 50.6% | 49.4% | Gemini 2.5 Pro |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only Gemini 2.5 Pro measured |
| SimpleQA Verified | Not measured | 50.6% | Only Kimi K3 measured |
| LMArena Text: science | 1480 | 1508 | Kimi K3 |
| LMArena Text: medicine and healthcare | 1476 | 1489 | Kimi K3 |
| LMArena Text: legal and government | 1480 | 1503 | Kimi K3 |
| LMArena Text: business and finance | 1439 | 1470 | Kimi K3 |
| FACTS Benchmark Suite | 61.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | 1334 | Kimi K3 |
| Design Arena: UI components | 1137 | 1341 | Kimi K3 |
| Design Arena: game dev | 1111 | 1358 | Kimi K3 |
| Design Arena: data viz | 1222 | 1351 | Kimi K3 |
| Design Arena: 3D | 1080 | 1387 | Kimi K3 |
| Design Arena: all code categories | 1152 | 1360 | Kimi K3 |
| Design Arena: SVG | Not measured | 1302 | Only Kimi K3 measured |
| LiveBench design | Not measured | 77.2% | Only Kimi K3 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
| Benchmark | Gemini 2.5 Pro | Kimi K3 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.0% | Only Kimi K3 measured |
| LiveBench data analysis | Not measured | 78.7% | Only Kimi K3 measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.2% | Only Kimi K3 measured |
| SAGE (Vals AI) | 41.9% | 52.8% | Kimi K3 |
| MedScribe (Vals AI) | 73.5% | 88.0% | Kimi K3 |
| Vals Multimodal Index | Not measured | 73.4% | Only Kimi K3 measured |
| IFBench | 48.7% | Not measured | Only Gemini 2.5 Pro measured |
| Long context reasoning (AA-LCR) | 69.0% | 88.7% | Kimi K3 |
| DTBench | 82.4% | 91.2% | Kimi K3 |
| LMCA | 34.8% | 52.7% | Kimi K3 |
| Creative writing (Lech Mazur) | 83.8% | Not measured | Only Gemini 2.5 Pro measured |
| Surface Evolver Bench | Not measured | 95.0% | Only Kimi K3 measured |
| Furniture assembly | Not measured | 34.2% | Only Kimi K3 measured |
| LMArena Text | 1458 | 1476 | Kimi K3 |
| LMArena Text: instruction following | 1437 | 1485 | Kimi K3 |
| LMArena Text: creative writing | 1454 | 1457 | Kimi K3 |
| LMArena Vision | 1263 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1458 | 1481 | Kimi K3 |
| LMArena Text: non-English | 1452 | 1466 | Kimi K3 |
| LMArena Text: Chinese | 1507 | 1529 | Kimi K3 |
| LMArena Text: German | 1487 | 1490 | Kimi K3 |
| LMArena Text: Japanese | 1461 | 1494 | Kimi K3 |
| LMArena Text: Korean | 1434 | 1460 | Kimi K3 |
| LMArena Text: French | 1473 | 1492 | Kimi K3 |
| LMArena Text: Spanish | 1474 | 1472 | Gemini 2.5 Pro |
| LMArena Text: Russian | 1461 | 1485 | Kimi K3 |
| LMArena Text: Polish | 1479 | 1458 | Gemini 2.5 Pro |
| LMArena Text: multi-turn | 1453 | 1489 | Kimi K3 |
| LMArena Text: longer queries | 1449 | 1494 | Kimi K3 |
| LMArena Text: writing and language | 1454 | 1471 | Kimi K3 |
| LMArena Text: entertainment and media | 1441 | 1453 | Kimi K3 |
| LMArena Vision: English | 1261 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | 1300 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | 1270 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | 1262 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | 1304 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | 1283 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | 1265 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 7 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro | Kimi K3 | 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 Pro costs less
Repository review
50K input + 3K output tokens
Gemini 2.5 Pro costs less
Agent loop
200K input + 10K output tokens
Gemini 2.5 Pro 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 Pro | MoonshotAI Kimi K3 |
|---|---|---|
| Maker | Moonshot AI | |
| Weights | No public weights listed | Open weights |
| Released | June 17, 2025 | July 16, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.25 | $3.00 |
| Output $/Mtok | $10.00 | $15.00 |
| Coding index | 19.1 | 62.3 |
| Independent measurements behind it | 3 | 6 |
| Cost per solved task | $0.64 | $0.81 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than Kimi K3 for coding?
Pick Kimi K3 for coding: it scores 62.3 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro. Pick Gemini 2.5 Pro for output tokens 1.5x cheaper ($10.00 vs $15.00 per million) and a lower cost per solved task ($0.64 against $0.81).
Which is cheaper, Gemini 2.5 Pro or Kimi K3?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Kimi K3 costs $3.00 and $15.00. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and Kimi K3 $0.81.
Which has the bigger context window, Gemini 2.5 Pro or Kimi K3?
Neither: both take 1.05M tokens.
How many benchmarks is the Gemini 2.5 Pro vs Kimi K3 comparison based on?
62 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro's coding index rests on 3 independent benchmarks and Kimi K3's on 6. Where only one model has a result, the page says so instead of naming a winner.