GPT-5.5 vs Kimi K2.7 Code
GPT-5.5 leads Kimi K2.7 Code on coding, 71.3 to 53.4 on the Vibe Coding Index, and Kimi K2.7 Code costs 7.5x less per output token ($4.00 vs $30.00 per million). Updated October 21, 2026; 51 benchmarks have a result for both.
GPT-5.5 or Kimi K2.7 Code: which to pick
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code.
Pick Kimi K2.7 Code for output tokens 7.5x cheaper ($4.00 vs $30.00 per million) and a lower cost per solved task ($0.35 against $1.65).
- Shared results
- 51
- GPT-5.5 only
- 83
- Kimi K2.7 Code only
- 0
- 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-5.5
GPT-5.5 scores 71.3 against 53.4.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 25.8.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.5
GPT-5.5 leads, 36.4 against 21.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
Kimi K2.7 Code
Kimi K2.7 Code leads, 1264 against 1256.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.7 Code
GPT-5.5 $0.020, Kimi K2.7 Code $0.0029 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.7 Code
GPT-5.5 $1.65, Kimi K2.7 Code $0.35.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.5 71.3 from 5 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
- Widest category
- Math, 34.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: GPT-5.5 72.5% against Kimi K2.7 Code 12.2%, 60.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.5 $30.00 against Kimi K2.7 Code $4.00 per million output tokens, 7.5x apart.
- Context window
- GPT-5.5 takes 1.05M tokens, Kimi K2.7 Code 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-5.5 | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 17 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 7 | 4 | 1 tie | |
| Reasoning | 5 | 0 | ||
| Math | 2 | 1 | ||
| Knowledge | 1 | 0 | ||
| Web design | 6 | 2 | ||
| General | 3 | 0 | ||
| All shared boards | 43 | 7 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index170
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench coding | 85.1% | Not measured | Only GPT-5.5 measured |
| LMArena WebDev | 1513 | 1474 | GPT-5.5 |
| AA Coding Index | 74.9 | 60.8 | GPT-5.5 |
| LiveCodeBench (Vals AI) | 85.3% | 82.0% | GPT-5.5 |
| SWE-bench Verified (Vals AI) | 82.6% | 78.2% | GPT-5.5 |
| Vibe Code Bench (Vals AI) | 69.8% | 47.2% | GPT-5.5 |
| Code migration (Vals AI) | 45.2% | 25.4% | GPT-5.5 |
| ProgramBench (Vals AI) | 0.5% | 0.0% | GPT-5.5 |
| Terminal-Bench 2.1 (Vals AI) | 76.4% | 67.0% | GPT-5.5 |
| SkillsBench (Vals AI) | 62.5% | 50.0% | GPT-5.5 |
| SciCode | 56.1% | 47.8% | GPT-5.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 60.6% | 44.7% | GPT-5.5 |
| Terminal-Bench | 84.7% | Not measured | Only GPT-5.5 measured |
| FrontierCode | 43.0% | 30.1% | GPT-5.5 |
| GSO-Bench | 40.2% | Not measured | Only GPT-5.5 measured |
| WeirdML | 84.9% | 54.1% | GPT-5.5 |
| MirrorCode | 10.0% | Not measured | Only GPT-5.5 measured |
| ALE-Bench | 1943.0 | 886.2 | GPT-5.5 |
| LMArena Text: coding | 1494 | Not measured | Only GPT-5.5 measured |
| LMArena Text: software and IT | 1493 | Not measured | Only GPT-5.5 measured |
| LMArena WebDev: React | 1506 | 1475 | GPT-5.5 |
| LMArena WebDev: HTML | 1543 | 1451 | GPT-5.5 |
| LMArena WebDev: from an image | 1532 | 1513 | GPT-5.5 |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 25.8 | GPT-5.5 |
| Epoch Capabilities Index | 159.7 | 150.1 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index74
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Agentic Index | 36.4 | 21.0 | GPT-5.5 |
| Design Arena: agents building web apps | 1117 | 1176 | Kimi K2.7 Code |
| Design Arena: agents building full-stack apps | 1089 | 1175 | Kimi K2.7 Code |
| Design Arena: agents building mobile apps | 1143 | 1143 | Tie |
| Design Arena: agents building Android apps | 1163 | 1137 | GPT-5.5 |
| Design Arena: agents building Godot games | 1173 | 1147 | GPT-5.5 |
| Design Arena: agents building games | 1179 | 1131 | GPT-5.5 |
| Design Arena: HTML slides | 1067 | 1201 | Kimi K2.7 Code |
| Design Arena: PowerPoint slides | 1109 | 1154 | Kimi K2.7 Code |
| LiveBench research | 61.6% | Not measured | Only GPT-5.5 measured |
| LiveBench browser | 48.0% | Not measured | Only GPT-5.5 measured |
| LiveBench long agentic | 44.6% | Not measured | Only GPT-5.5 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | 51.8% | Not measured | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | 40.4% | Not measured | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | 64.5% | Not measured | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | 8.3% | Not measured | Only GPT-5.5 measured |
| τ²-Bench Telecom | 93.9% | 90.1% | GPT-5.5 |
| APEX-Agents | 55.1% | 37.6% | GPT-5.5 |
| Remote Labor Index | 6.3% | Not measured | Only GPT-5.5 measured |
| OSWorld 2.0 | 13.0% | Not measured | Only GPT-5.5 measured |
| DeepResearch Bench | 54.0% | Not measured | Only GPT-5.5 measured |
| PostTrainBench | 27.2% | Not measured | Only GPT-5.5 measured |
| Vending-Bench 2 | $7,524 | $5,083 | GPT-5.5 |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| GPQA Diamond | 93.2% | Not measured | Only GPT-5.5 measured |
| LiveBench reasoning | 89.7% | Not measured | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | 88.3% | Not measured | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | 93.5% | 89.6% | GPT-5.5 |
| Humanity's Last Exam (Artificial Analysis run) | 45.8% | 35.0% | GPT-5.5 |
| GPQA Diamond (Epoch AI run) | 90.7% | 87.9% | GPT-5.5 |
| ARC-AGI | 95.0% | Not measured | Only GPT-5.5 measured |
| ARC-AGI-2 | 85.0% | Not measured | Only GPT-5.5 measured |
| SimpleBench | 69.0% | 57.9% | GPT-5.5 |
| Chess puzzles | 63.0% | 14.0% | GPT-5.5 |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-5.5 measured |
| EBR-bench | 34.3% | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1489 | Not measured | Only GPT-5.5 measured |
| LMArena Text: expert | 1507 | Not measured | Only GPT-5.5 measured |
| LMArena Text: hard prompts in English | 1483 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: diagrams | 1325 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: homework | 1332 | Not measured | Only GPT-5.5 measured |
| Kagi LLM Benchmark | 88.8% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 84.4% | 95.6% | Kimi K2.7 Code |
| FrontierMath, tiers 1 to 3 | 85.3% | 54.0% | GPT-5.5 |
| FrontierMath, tier 4 | 72.5% | 12.2% | GPT-5.5 |
| ProofBench | 50.0% | Not measured | Only GPT-5.5 measured |
| LMArena Text: math | 1487 | Not measured | Only GPT-5.5 measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| MMLU Pro | 88.1% | Not measured | Only GPT-5.5 measured |
| LegalBench (Vals AI) | 86.5% | Not measured | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | 66.2% | Not measured | Only GPT-5.5 measured |
| CorpFin (Vals AI) | 68.4% | Not measured | Only GPT-5.5 measured |
| TaxEval (Vals AI) | 75.0% | Not measured | Only GPT-5.5 measured |
| MortgageTax (Vals AI) | 68.8% | Not measured | Only GPT-5.5 measured |
| MedCode (Vals AI) | 49.1% | Not measured | Only GPT-5.5 measured |
| SimpleQA Verified | 63.0% | 36.5% | GPT-5.5 |
| LMArena Text: science | 1485 | Not measured | Only GPT-5.5 measured |
| LMArena Text: medicine and healthcare | 1465 | Not measured | Only GPT-5.5 measured |
| LMArena Text: legal and government | 1491 | Not measured | Only GPT-5.5 measured |
| LMArena Text: business and finance | 1478 | Not measured | Only GPT-5.5 measured |
| FACTS Benchmark Suite | 66.7% | Not measured | Only GPT-5.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | 1256 | 1264 | Kimi K2.7 Code |
| Design Arena: UI components | 1260 | 1259 | GPT-5.5 |
| Design Arena: game dev | 1294 | 1220 | GPT-5.5 |
| Design Arena: data viz | 1265 | 1227 | GPT-5.5 |
| Design Arena: 3D | 1198 | 1241 | Kimi K2.7 Code |
| Design Arena: all code categories | 1260 | 1253 | GPT-5.5 |
| Design Arena: SVG | 1234 | 1176 | GPT-5.5 |
| Design Arena: ASCII art | 1255 | 1196 | GPT-5.5 |
| LiveBench design | 70.3% | Not measured | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.5 | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.6% | Not measured | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 60.9% | Not measured | Only GPT-5.5 measured |
| SAGE (Vals AI) | 51.5% | Not measured | Only GPT-5.5 measured |
| MedScribe (Vals AI) | 86.9% | Not measured | Only GPT-5.5 measured |
| Vals Multimodal Index | 68.1% | Not measured | Only GPT-5.5 measured |
| IFBench | 75.9% | 63.1% | GPT-5.5 |
| Long context reasoning (AA-LCR) | 84.3% | 79.3% | GPT-5.5 |
| DTBench | 96.0% | Not measured | Only GPT-5.5 measured |
| LMCA | 54.3% | Not measured | Only GPT-5.5 measured |
| Surface Evolver Bench | 88.1% | 48.8% | GPT-5.5 |
| Furniture assembly | 44.2% | Not measured | Only GPT-5.5 measured |
| ExploitBench | 47.4% | Not measured | Only GPT-5.5 measured |
| CL-bench Life | 22.2% | Not measured | Only GPT-5.5 measured |
| LMArena Text | 1471 | Not measured | Only GPT-5.5 measured |
| LMArena Text: instruction following | 1478 | Not measured | Only GPT-5.5 measured |
| LMArena Text: creative writing | 1455 | Not measured | Only GPT-5.5 measured |
| LMArena Vision | 1297 | Not measured | Only GPT-5.5 measured |
| LMArena Text: English | 1470 | Not measured | Only GPT-5.5 measured |
| LMArena Text: non-English | 1467 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Chinese | 1534 | Not measured | Only GPT-5.5 measured |
| LMArena Text: German | 1480 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Japanese | 1499 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GPT-5.5 measured |
| LMArena Text: French | 1485 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Spanish | 1468 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Russian | 1473 | Not measured | Only GPT-5.5 measured |
| LMArena Text: Polish | 1465 | Not measured | Only GPT-5.5 measured |
| LMArena Text: multi-turn | 1476 | Not measured | Only GPT-5.5 measured |
| LMArena Text: longer queries | 1484 | Not measured | Only GPT-5.5 measured |
| LMArena Text: writing and language | 1466 | Not measured | Only GPT-5.5 measured |
| LMArena Text: entertainment and media | 1449 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: English | 1292 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: Chinese | 1360 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: reading text | 1306 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: humor | 1293 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | 1231 | Not measured | Only GPT-5.5 measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-5.5 measured |
| LMArena Document | 1486 | Not measured | Only GPT-5.5 measured |
| 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-5.5 | Kimi K2.7 Code | 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 K2.7 Code costs less
Repository review
50K input + 3K output tokens
Kimi K2.7 Code costs less
Agent loop
200K input + 10K output tokens
Kimi K2.7 Code 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-5.5 | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | April 24, 2026 | June 12, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $5.00 | $0.95 |
| Output $/Mtok | $30.00 | $4.00 |
| Coding index | 71.3 | 53.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $1.65 | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.5 better than Kimi K2.7 Code for coding?
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code. Pick Kimi K2.7 Code for output tokens 7.5x cheaper ($4.00 vs $30.00 per million) and a lower cost per solved task ($0.35 against $1.65).
Which is cheaper, GPT-5.5 or Kimi K2.7 Code?
GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, GPT-5.5 costs $1.65 and Kimi K2.7 Code $0.35.
Which has the bigger context window, GPT-5.5 or Kimi K2.7 Code?
GPT-5.5, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.5 vs Kimi K2.7 Code comparison based on?
51 benchmarks have a result for both models as of October 21, 2026. GPT-5.5's coding index rests on 5 independent benchmarks and Kimi K2.7 Code's on 4. Where only one model has a result, the page says so instead of naming a winner.