GPT-5.1 vs Kimi K2.6
Updated October 21, 2026. 81 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-5.1 for coding: it scores 53.3 on the Vibe Coding Index against 44.2 for Kimi K2.6.
Pick Kimi K2.6 when output tokens cost 2.9x less, or when a solved task costs $0.35 against $1.02.
- Shared results
- 81
- GPT-5.1 only
- 19
- Kimi K2.6 only
- 28
- 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-5.1
GPT-5.1 scores 53.3 against 44.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Kimi K2.6
Kimi K2.6 leads, 27.0 against 24.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Kimi K2.6 has a AA Agentic Index result, 20.5.
Directional only
Reasoning
GPQA Diamond
Kimi K2.6
Kimi K2.6 leads, 89.1% against 86.6%.
Same board, both measured
Math
AIME
No call
Only GPT-5.1 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
Kimi K2.6
Kimi K2.6 leads, 87.6% against 86.4%.
Same board, both measured
Web design
Design Arena: websites
Kimi K2.6
Kimi K2.6 leads, 1269 against 1187.
Same board, both measured
Long documents
Listed context window
GPT-5.1
GPT-5.1 takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.6
GPT-5.1 $0.0063, Kimi K2.6 $0.0024 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.6
GPT-5.1 $1.02, Kimi K2.6 $0.35.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.1 53.3 from 2 benchmarks, Kimi K2.6 44.2 from 1 benchmark.
- Widest category
- Agentic work, 21.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GPT-5.1 1307 against Kimi K2.6 1505, 33.1 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1 $10.00 against Kimi K2.6 $3.41 per million output tokens, 2.9x apart.
- Context window
- GPT-5.1 takes 400K tokens, Kimi K2.6 262K.
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 | GPT-5.1 | Bars | Kimi K2.6 | Ties |
|---|---|---|---|---|
| Coding | 2 | 10 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 2 | 10 | ||
| Math | 0 | 3 | ||
| Knowledge | 5 | 7 | ||
| Web design | 0 | 8 | ||
| General | 4 | 26 | ||
| All shared boards | 13 | 68 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index210
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1 measured |
| LMArena WebDev | 1396 | 1509 | Kimi K2.6 |
| AA Coding Index | 49.4 | 61.8 | Kimi K2.6 |
| LiveCodeBench (Vals AI) | 86.5% | 86.8% | Kimi K2.6 |
| SWE-bench Verified (Vals AI) | 69.8% | 76.2% | Kimi K2.6 |
| Vibe Code Bench (Vals AI) | 24.6% | 37.9% | Kimi K2.6 |
| Code migration (Vals AI) | Not measured | 27.8% | Only Kimi K2.6 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Kimi K2.6 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.6% | Only Kimi K2.6 measured |
| LiveCodeBench (Artificial Analysis run) | 86.8% | Not measured | Only GPT-5.1 measured |
| SciCode | Not measured | 51.5% | Only Kimi K2.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 45.5% | 43.9% | GPT-5.1 |
| Terminal-Bench | 47.6% | Not measured | Only GPT-5.1 measured |
| GSO-Bench | 13.7% | Not measured | Only GPT-5.1 measured |
| WeirdML | 60.8% | 55.9% | GPT-5.1 |
| ALE-Bench | Not measured | 1092.7 | Only Kimi K2.6 measured |
| LMArena Text: coding | 1453 | 1489 | Kimi K2.6 |
| LMArena Text: software and IT | 1454 | 1484 | Kimi K2.6 |
| LMArena WebDev: React | 1307 | 1505 | Kimi K2.6 |
| LMArena WebDev: HTML | 1405 | 1485 | Kimi K2.6 |
| LMArena WebDev: from an image | 1421 | 1521 | Kimi K2.6 |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 27.0 | Kimi K2.6 |
| Epoch Capabilities Index | 149.8 | 150.9 | Kimi K2.6 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 20.5 | Only Kimi K2.6 measured |
| Design Arena: agents building web apps | Not measured | 1268 | Only Kimi K2.6 measured |
| Design Arena: agents building full-stack apps | Not measured | 1155 | Only Kimi K2.6 measured |
| Design Arena: agents building mobile apps | Not measured | 1156 | Only Kimi K2.6 measured |
| Design Arena: agents building Android apps | Not measured | 1166 | Only Kimi K2.6 measured |
| Design Arena: agents building Godot games | Not measured | 1120 | Only Kimi K2.6 measured |
| Design Arena: agents building games | Not measured | 1141 | Only Kimi K2.6 measured |
| Design Arena: HTML slides | Not measured | 1184 | Only Kimi K2.6 measured |
| Design Arena: PowerPoint slides | Not measured | 1128 | Only Kimi K2.6 measured |
| Design Arena: spreadsheets | Not measured | 1276 | Only Kimi K2.6 measured |
| Finance Agent (Vals AI) | Not measured | 44.9% | Only Kimi K2.6 measured |
| Tax Agent Bench (Vals AI) | Not measured | 12.4% | Only Kimi K2.6 measured |
| Legal Research Bench (Vals AI) | Not measured | 15.9% | Only Kimi K2.6 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.9% | Only Kimi K2.6 measured |
| τ²-Bench Telecom | 81.9% | 95.9% | Kimi K2.6 |
| OSWorld 2.0 | Not measured | 4.6% | Only Kimi K2.6 measured |
| DeepResearch Bench | 42.8% | Not measured | Only GPT-5.1 measured |
| Vending-Bench 2 | $1,473 | $6,205 | Kimi K2.6 |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index210
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| GPQA Diamond | 86.6% | 89.1% | Kimi K2.6 |
| MMMU Pro (Vals AI) | 83.2% | 86.3% | Kimi K2.6 |
| GPQA Diamond (Artificial Analysis run) | 87.3% | 91.1% | Kimi K2.6 |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 37.5% | Kimi K2.6 |
| GPQA Diamond (Epoch AI run) | 87.6% | 90.8% | Kimi K2.6 |
| ARC-AGI | 72.8% | Not measured | Only GPT-5.1 measured |
| ARC-AGI-2 | 17.6% | Not measured | Only GPT-5.1 measured |
| SimpleBench | 53.2% | Not measured | Only GPT-5.1 measured |
| Chess puzzles | 42.0% | 20.0% | GPT-5.1 |
| Mystery game puzzles | 19.0% | 18.0% | GPT-5.1 |
| EBR-bench | Not measured | 2.4% | Only Kimi K2.6 measured |
| VPCT | 58.7% | Not measured | Only GPT-5.1 measured |
| LMArena Text: hard prompts | 1457 | 1470 | Kimi K2.6 |
| LMArena Text: expert | 1470 | 1491 | Kimi K2.6 |
| LMArena Text: hard prompts in English | 1462 | 1471 | Kimi K2.6 |
| LMArena Vision: diagrams | 1264 | 1298 | Kimi K2.6 |
| LMArena Vision: homework | 1272 | 1310 | Kimi K2.6 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GPT-5.1 measured |
| MGSM (Vals AI) | 93.0% | Not measured | Only GPT-5.1 measured |
| AA Math Index | 94.0 | Not measured | Only GPT-5.1 measured |
| AIME 2025 (Artificial Analysis run) | 94.0% | Not measured | Only GPT-5.1 measured |
| OTIS Mock AIME | 88.6% | 96.1% | Kimi K2.6 |
| FrontierMath, tiers 1 to 3 | Not measured | 57.2% | Only Kimi K2.6 measured |
| FrontierMath, tier 4 | Not measured | 25.6% | Only Kimi K2.6 measured |
| ProofBench | Not measured | 16.0% | Only Kimi K2.6 measured |
| LMArena Text: math | 1446 | 1474 | Kimi K2.6 |
| LMArena Text: mathematical work | 1449 | 1480 | Kimi K2.6 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index57
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| MMLU Pro | 86.4% | 87.6% | Kimi K2.6 |
| LegalBench (Vals AI) | 85.7% | 84.7% | GPT-5.1 |
| CaseLaw (Vals AI) | 73.4% | 61.2% | GPT-5.1 |
| CorpFin (Vals AI) | 63.8% | 66.7% | Kimi K2.6 |
| TaxEval (Vals AI) | 74.9% | 74.7% | GPT-5.1 |
| MortgageTax (Vals AI) | 61.4% | 65.8% | Kimi K2.6 |
| MedQA (Vals AI) | 96.4% | Not measured | Only GPT-5.1 measured |
| MedCode (Vals AI) | 52.7% | 40.1% | GPT-5.1 |
| MMLU Pro (Artificial Analysis run) | 87.0% | Not measured | Only GPT-5.1 measured |
| SimpleQA Verified | 48.0% | 34.9% | GPT-5.1 |
| LMArena Text: science | 1459 | 1468 | Kimi K2.6 |
| LMArena Text: medicine and healthcare | 1456 | 1457 | Kimi K2.6 |
| LMArena Text: legal and government | 1459 | 1462 | Kimi K2.6 |
| LMArena Text: business and finance | 1431 | 1445 | Kimi K2.6 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | 1269 | Kimi K2.6 |
| Design Arena: UI components | 1166 | 1266 | Kimi K2.6 |
| Design Arena: game dev | 1179 | 1251 | Kimi K2.6 |
| Design Arena: data viz | 1204 | 1249 | Kimi K2.6 |
| Design Arena: 3D | 1061 | 1268 | Kimi K2.6 |
| Design Arena: all code categories | 1171 | 1272 | Kimi K2.6 |
| Design Arena: SVG | 1149 | 1181 | Kimi K2.6 |
| Design Arena: ASCII art | 1118 | 1164 | Kimi K2.6 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index426
| Benchmark | GPT-5.1 | Kimi K2.6 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 56.6% | Only Kimi K2.6 measured |
| SAGE (Vals AI) | 43.2% | 50.2% | Kimi K2.6 |
| MedScribe (Vals AI) | 88.1% | 78.2% | GPT-5.1 |
| Vals Multimodal Index | Not measured | 56.4% | Only Kimi K2.6 measured |
| IFBench | 72.9% | 76.0% | Kimi K2.6 |
| Long context reasoning (AA-LCR) | 80.0% | 81.0% | Kimi K2.6 |
| DTBench | 90.1% | 90.9% | Kimi K2.6 |
| LMCA | 43.9% | 37.3% | GPT-5.1 |
| CL-bench | 23.7% | Not measured | Only GPT-5.1 measured |
| Furniture assembly | Not measured | 21.7% | Only Kimi K2.6 measured |
| ExploitBench | Not measured | 18.4% | Only Kimi K2.6 measured |
| CL-bench Life | 17.3% | Not measured | Only GPT-5.1 measured |
| LMArena Text | 1443 | 1455 | Kimi K2.6 |
| LMArena Text: instruction following | 1443 | 1451 | Kimi K2.6 |
| LMArena Text: creative writing | 1427 | 1434 | Kimi K2.6 |
| LMArena Vision | 1250 | 1282 | Kimi K2.6 |
| LMArena Text: English | 1451 | 1460 | Kimi K2.6 |
| LMArena Text: non-English | 1431 | 1446 | Kimi K2.6 |
| LMArena Text: Chinese | 1495 | 1520 | Kimi K2.6 |
| LMArena Text: German | 1438 | 1451 | Kimi K2.6 |
| LMArena Text: Japanese | 1453 | 1443 | GPT-5.1 |
| LMArena Text: Korean | 1401 | 1427 | Kimi K2.6 |
| LMArena Text: French | 1450 | 1471 | Kimi K2.6 |
| LMArena Text: Spanish | 1433 | 1464 | Kimi K2.6 |
| LMArena Text: Russian | 1435 | 1446 | Kimi K2.6 |
| LMArena Text: Polish | 1440 | 1438 | GPT-5.1 |
| LMArena Text: multi-turn | 1450 | 1453 | Kimi K2.6 |
| LMArena Text: longer queries | 1448 | 1468 | Kimi K2.6 |
| LMArena Text: writing and language | 1428 | 1439 | Kimi K2.6 |
| LMArena Text: entertainment and media | 1415 | 1428 | Kimi K2.6 |
| LMArena Vision: English | 1252 | 1287 | Kimi K2.6 |
| LMArena Vision: Chinese | 1270 | 1346 | Kimi K2.6 |
| LMArena Vision: reading text | 1255 | 1291 | Kimi K2.6 |
| LMArena Vision: humor | 1263 | 1280 | Kimi K2.6 |
| LMArena Vision: captioning | 1252 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.1 measured |
| LMArena Vision: creative writing | 1266 | 1273 | Kimi K2.6 |
| LMArena Document | 1403 | 1451 | Kimi K2.6 |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1 | Kimi K2.6 | 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
Kimi K2.6 costs less
Repository review
50K input + 3K output tokens
Kimi K2.6 costs less
Agent loop
200K input + 10K output tokens
Kimi K2.6 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 | OpenAI GPT-5.1 | MoonshotAI Kimi K2.6 |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | November 13, 2025 | April 20, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $1.25 | $0.65 |
| Output $/Mtok | $10.00 | $3.41 |
| Coding index | 53.3 | 44.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.02 | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1 better than Kimi K2.6 for coding?
Pick GPT-5.1 for coding: it scores 53.3 on the Vibe Coding Index against 44.2 for Kimi K2.6. Pick Kimi K2.6 when output tokens cost 2.9x less, or when a solved task costs $0.35 against $1.02.
Which is cheaper, GPT-5.1 or Kimi K2.6?
GPT-5.1 costs $1.25 per million input tokens and $10.00 per million output tokens; Kimi K2.6 costs $0.65 and $3.41. Per solved SWE-bench Verified task, GPT-5.1 costs $1.02 and Kimi K2.6 $0.35.
Which has the bigger context window, GPT-5.1 or Kimi K2.6?
GPT-5.1, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1 vs Kimi K2.6 comparison based on?
81 benchmarks have a result for both models as of October 21, 2026. GPT-5.1's coding index rests on 2 independent benchmarks and Kimi K2.6's on 1. Where only one model has a result, the page says so instead of naming a winner.