GPT-5.2 vs Kimi K2 0711
Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Kimi K2 0711 for coding: it scores 64.7 on the Vibe Coding Index against 58.9 for GPT-5.2.
Pick GPT-5.2 when the context window is larger, 400K against 131K tokens, or when what matters is SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 32
- GPT-5.2 only
- 79
- Kimi K2 0711 only
- 4
- 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
Kimi K2 0711
Kimi K2 0711 scores 64.7 against 58.9.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.2
GPT-5.2 leads, 30.4 against 12.7.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.2
GPT-5.2 leads, 91.7% against 71.5%.
Same board, both measured
Math
AIME
GPT-5.2
GPT-5.2 leads, 96.9% against 62.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 79.4%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.2
GPT-5.2 leads, 1196 against 1050.
Same board, both measured
Long documents
Listed context window
GPT-5.2
GPT-5.2 takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2 0711
GPT-5.2 $0.0088, Kimi K2 0711 $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 58.9 from 2 benchmarks, Kimi K2 0711 64.7 from 2 benchmarks.
- Widest category
- Math, 30.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GPT-5.2 99.0% against Kimi K2 0711 57.0%, 42.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Kimi K2 0711 $2.30 per million output tokens, 6.1x apart.
- Context window
- GPT-5.2 takes 400K tokens, Kimi K2 0711 131K.
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-5.2 | Bars | Kimi K2 0711 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 6 | 0 | ||
| Web design | 5 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 32 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index70
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 59.1% | Only Kimi K2 0711 measured |
| SWE-bench Verified | 72.8% | 71.2% | GPT-5.2 |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LMArena WebDev | 1417 | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Vals AI) | 85.4% | 70.5% | GPT-5.2 |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Vibe Code Bench (Vals AI) | 53.5% | Not measured | Only GPT-5.2 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 55.6% | GPT-5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | 15.9% | GPT-5.2 |
| Terminal-Bench | 64.9% | 27.8% | GPT-5.2 |
| GSO-Bench | 27.5% | 4.9% | GPT-5.2 |
| WeirdML | 72.2% | 39.4% | GPT-5.2 |
| ALE-Bench | 1293.5 | Not measured | Only GPT-5.2 measured |
| LMArena Text: coding | 1442 | Not measured | Only GPT-5.2 measured |
| LMArena Text: software and IT | 1434 | Not measured | Only GPT-5.2 measured |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 12.7 | GPT-5.2 |
| Epoch Capabilities Index | 153.7 | 140.0 | GPT-5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1094 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1064 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| τ²-Bench Telecom | 84.8% | 61.1% | GPT-5.2 |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 71.5% | GPT-5.2 |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 76.6% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 7.4% | GPT-5.2 |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | 26.3% | GPT-5.2 |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | Not measured | Only GPT-5.2 measured |
| LMArena Text: expert | 1445 | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | 1431 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| AIME | 96.9% | 62.7% | GPT-5.2 |
| MATH 500 (Vals AI) | Not measured | 94.2% | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | 94.0% | 91.0% | GPT-5.2 |
| AA Math Index | 99.0 | 57.0 | GPT-5.2 |
| AIME 2025 (Artificial Analysis run) | 99.0% | 57.0% | GPT-5.2 |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | Not measured | Only GPT-5.2 measured |
| LMArena Text: mathematical work | 1442 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 79.4% | GPT-5.2 |
| LegalBench (Vals AI) | 82.8% | 81.5% | GPT-5.2 |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | 50.4% | GPT-5.2 |
| TaxEval (Vals AI) | 75.8% | 70.2% | GPT-5.2 |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | 84.0% | GPT-5.2 |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | 82.4% | GPT-5.2 |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: legal and government | 1427 | Not measured | Only GPT-5.2 measured |
| LMArena Text: business and finance | 1414 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| Design Arena: websites | 1196 | 1050 | GPT-5.2 |
| Design Arena: UI components | 1190 | 1030 | GPT-5.2 |
| Design Arena: game dev | 1196 | 974 | GPT-5.2 |
| Design Arena: data viz | 1202 | 1018 | GPT-5.2 |
| Design Arena: 3D | 1079 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1171 | 1034 | GPT-5.2 |
| Design Arena: SVG | 1137 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1153 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.2 | Kimi K2 0711 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | 41.5% | GPT-5.2 |
| Long context reasoning (AA-LCR) | 82.7% | 53.0% | GPT-5.2 |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| Fiction.LiveBench | Not measured | 61.1% | Only Kimi K2 0711 measured |
| Creative writing (Lech Mazur) | Not measured | 85.6% | Only Kimi K2 0711 measured |
| CL-bench | 18.2% | 11.9% | GPT-5.2 |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | Not measured | Only GPT-5.2 measured |
| LMArena Text: instruction following | 1409 | Not measured | Only GPT-5.2 measured |
| LMArena Text: creative writing | 1376 | Not measured | Only GPT-5.2 measured |
| LMArena Vision | 1246 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | Not measured | Only GPT-5.2 measured |
| LMArena Text: non-English | 1405 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Chinese | 1447 | Not measured | Only GPT-5.2 measured |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Spanish | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1415 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | Not measured | Only GPT-5.2 measured |
| LMArena Text: longer queries | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: writing and language | 1392 | Not measured | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | 1378 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | Kimi K2 0711 | 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 0711 costs less
Repository review
50K input + 3K output tokens
Kimi K2 0711 costs less
Agent loop
200K input + 10K output tokens
Kimi K2 0711 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-5.2 | MoonshotAI Kimi K2 0711 |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | July 11, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.75 | $0.57 |
| Output $/Mtok | $14.00 | $2.30 |
| Coding index | 58.9 | 64.7 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Kimi K2 0711 for coding?
Pick Kimi K2 0711 for coding: it scores 64.7 on the Vibe Coding Index against 58.9 for GPT-5.2. Pick GPT-5.2 when the context window is larger, 400K against 131K tokens, or when what matters is SWE-bench Verified or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, GPT-5.2 or Kimi K2 0711?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Kimi K2 0711 costs $0.57 and $2.30. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Kimi K2 0711?
GPT-5.2, with 400K tokens against 131K.
How many benchmarks is the GPT-5.2 vs Kimi K2 0711 comparison based on?
32 benchmarks have a result for both models as of October 21, 2026. GPT-5.2's coding index rests on 2 independent benchmarks and Kimi K2 0711's on 2. Where only one model has a result, the page says so instead of naming a winner.