GPT-5.6 Sol vs Kimi K2.7 Code
GPT-5.6 Sol leads Kimi K2.7 Code on coding, 73.5 to 53.4 on the Vibe Coding Index, and Kimi K2.7 Code costs 5.0x less per output token ($4.00 vs $20.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
GPT-5.6 Sol or Kimi K2.7 Code: which to pick
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code.
Pick Kimi K2.7 Code for output tokens 5.0x cheaper ($4.00 vs $20.00 per million) and a lower cost per solved task ($0.35 against $1.20).
- Shared results
- 50
- GPT-5.6 Sol only
- 89
- Kimi K2.7 Code only
- 1
- 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-5.6 Sol
GPT-5.6 Sol scores 73.5 against 53.4.
6 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 47.0 against 25.8.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Sol
GPT-5.6 Sol leads, 50.2 against 21.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Sol has a GPQA Diamond result, 95.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Sol has a MMLU Pro result, 89.1%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Sol
GPT-5.6 Sol leads, 1321 against 1263.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Sol
GPT-5.6 Sol takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.7 Code
GPT-5.6 Sol $0.014, 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-5.6 Sol $1.20, Kimi K2.7 Code $0.35.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Sol 73.5 from 6 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
- Widest category
- Math, 36.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: GPT-5.6 Sol 82.9% against Kimi K2.7 Code 12.2%, 70.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Sol $20.00 against Kimi K2.7 Code $4.00 per million output tokens, 5.0x apart.
- Context window
- GPT-5.6 Sol 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-5.6 Sol | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 17 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 10 | 1 | ||
| Reasoning | 5 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 1 | 0 | ||
| Web design | 8 | 0 | ||
| General | 3 | 0 | ||
| All shared boards | 49 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index170
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench coding | 89.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev | 1616 | 1474 | GPT-5.6 Sol |
| AA Coding Index | 77.4 | 60.8 | GPT-5.6 Sol |
| LiveCodeBench (Vals AI) | 82.6% | 82.0% | GPT-5.6 Sol |
| SWE-bench Verified (Vals AI) | 96.2% | 78.2% | GPT-5.6 Sol |
| Terminal-Bench 4 (Vals AI) | 37.9% | Not measured | Only GPT-5.6 Sol measured |
| Vibe Code Bench (Vals AI) | 80.5% | 47.2% | GPT-5.6 Sol |
| Code migration (Vals AI) | 52.9% | 25.4% | GPT-5.6 Sol |
| ProgramBench (Vals AI) | 1.5% | 0.0% | GPT-5.6 Sol |
| IOI (Vals AI) | 91.2% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 85.8% | 67.0% | GPT-5.6 Sol |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| SkillsBench (Vals AI) | 54.1% | 50.0% | GPT-5.6 Sol |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-5.6 Sol measured |
| SciCode | 57.8% | 47.8% | GPT-5.6 Sol |
| Terminal-Bench Hard (Artificial Analysis run) | 65.9% | 44.7% | GPT-5.6 Sol |
| FrontierCode | 47.5% | 30.1% | GPT-5.6 Sol |
| FrontierSWE | 32.2% | Not measured | Only GPT-5.6 Sol measured |
| GSO-Bench | 76.5% | Not measured | Only GPT-5.6 Sol measured |
| WeirdML | 88.8% | 54.1% | GPT-5.6 Sol |
| MirrorCode | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| ALE-Bench | 2176.9 | 886.2 | GPT-5.6 Sol |
| LMArena Text: coding | 1496 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: software and IT | 1489 | Not measured | Only GPT-5.6 Sol measured |
| LMArena WebDev: React | 1620 | 1475 | GPT-5.6 Sol |
| LMArena WebDev: HTML | 1618 | 1451 | GPT-5.6 Sol |
| LMArena WebDev: from an image | 1609 | 1513 | GPT-5.6 Sol |
| SciCode | 10.8% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.0 | 25.8 | GPT-5.6 Sol |
| Epoch Capabilities Index | 162.8 | 150.1 | GPT-5.6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index101
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Agentic Index | 50.2 | 21.0 | GPT-5.6 Sol |
| Design Arena: agents building web apps | 1245 | 1176 | GPT-5.6 Sol |
| Design Arena: agents building full-stack apps | 1261 | 1175 | GPT-5.6 Sol |
| Design Arena: agents building mobile apps | 1248 | 1143 | GPT-5.6 Sol |
| Design Arena: agents building Android apps | 1240 | 1138 | GPT-5.6 Sol |
| Design Arena: agents building Godot games | 1260 | 1147 | GPT-5.6 Sol |
| Design Arena: agents building games | 1266 | 1130 | GPT-5.6 Sol |
| Design Arena: HTML slides | 1236 | 1151 | GPT-5.6 Sol |
| Design Arena: PowerPoint slides | 1215 | 1129 | GPT-5.6 Sol |
| LiveBench research | 54.5% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench browser | 88.0% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench long agentic | 48.5% | Not measured | Only GPT-5.6 Sol measured |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-5.6 Sol measured |
| Finance Agent (Vals AI) | 53.8% | Not measured | Only GPT-5.6 Sol measured |
| Tax Agent Bench (Vals AI) | 31.1% | Not measured | Only GPT-5.6 Sol measured |
| Legal Research Bench (Vals AI) | 48.1% | Not measured | Only GPT-5.6 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 72.3% | Not measured | Only GPT-5.6 Sol measured |
| BioMysteryBench (Vals AI) | 71.1% | Not measured | Only GPT-5.6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 20.0% | Not measured | Only GPT-5.6 Sol measured |
| Vals Science Index | 41.5% | Not measured | Only GPT-5.6 Sol measured |
| Time Horizon Index (Vals AI) | 23.8% | Not measured | Only GPT-5.6 Sol measured |
| CUA-bench (Vals AI) | 8.3% | Not measured | Only GPT-5.6 Sol measured |
| τ²-Bench Telecom | 85.1% | 90.1% | Kimi K2.7 Code |
| APEX-Agents | Not measured | 37.6% | Only Kimi K2.7 Code measured |
| OSWorld 2.0 | 27.3% | Not measured | Only GPT-5.6 Sol measured |
| BALROG | 60.0% | Not measured | Only GPT-5.6 Sol measured |
| PostTrainBench | 36.2% | Not measured | Only GPT-5.6 Sol measured |
| Vending-Bench 2 | $9,619 | $5,083 | GPT-5.6 Sol |
| LMArena Search | 1261 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 12 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| GPQA Diamond | 95.2% | Not measured | Only GPT-5.6 Sol measured |
| LiveBench reasoning | 91.7% | Not measured | Only GPT-5.6 Sol measured |
| MysteryMechanism (Vals AI) | 33.3% | Not measured | Only GPT-5.6 Sol measured |
| MMMU Pro (Vals AI) | 88.8% | Not measured | Only GPT-5.6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | 94.1% | 89.6% | GPT-5.6 Sol |
| Humanity's Last Exam (Artificial Analysis run) | 49.5% | 35.0% | GPT-5.6 Sol |
| GPQA Diamond (Epoch AI run) | 93.5% | 87.9% | GPT-5.6 Sol |
| ARC-AGI | 97.5% | Not measured | Only GPT-5.6 Sol measured |
| ARC-AGI-2 | 92.5% | Not measured | Only GPT-5.6 Sol measured |
| SimpleBench | 64.8% | 57.9% | GPT-5.6 Sol |
| Chess puzzles | 59.0% | 14.0% | GPT-5.6 Sol |
| Mystery game puzzles | 58.0% | Not measured | Only GPT-5.6 Sol measured |
| EBR-bench | 44.8% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: expert | 1517 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: hard prompts in English | 1477 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: diagrams | 1305 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: homework | 1305 | Not measured | Only GPT-5.6 Sol measured |
| Kagi LLM Benchmark | 67.0% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 3 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | 95.6% | GPT-5.6 Sol |
| FrontierMath, tiers 1 to 3 | 89.1% | 54.0% | GPT-5.6 Sol |
| FrontierMath, tier 4 | 82.9% | 12.2% | GPT-5.6 Sol |
| ProofBench | 83.0% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: math | 1473 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: mathematical work | 1496 | Not measured | Only GPT-5.6 Sol 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-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| MMLU Pro | 89.1% | Not measured | Only GPT-5.6 Sol measured |
| LegalBench (Vals AI) | 87.0% | Not measured | Only GPT-5.6 Sol measured |
| CorpFin (Vals AI) | 64.4% | Not measured | Only GPT-5.6 Sol measured |
| TaxEval (Vals AI) | 74.8% | Not measured | Only GPT-5.6 Sol measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Sol measured |
| MedCode (Vals AI) | 44.0% | Not measured | Only GPT-5.6 Sol measured |
| SimpleQA Verified | 69.7% | 36.5% | GPT-5.6 Sol |
| LMArena Text: science | 1455 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: medicine and healthcare | 1431 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: legal and government | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: business and finance | 1457 | Not measured | Only GPT-5.6 Sol measured |
| FACTS Benchmark Suite | 52.4% | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1263 | GPT-5.6 Sol |
| Design Arena: UI components | 1335 | 1255 | GPT-5.6 Sol |
| Design Arena: game dev | 1367 | 1220 | GPT-5.6 Sol |
| Design Arena: data viz | 1322 | 1227 | GPT-5.6 Sol |
| Design Arena: 3D | 1384 | 1240 | GPT-5.6 Sol |
| Design Arena: all code categories | 1341 | 1252 | GPT-5.6 Sol |
| Design Arena: SVG | 1314 | 1176 | GPT-5.6 Sol |
| Design Arena: ASCII art | 1304 | 1196 | GPT-5.6 Sol |
| LiveBench design | 81.6% | Not measured | Only GPT-5.6 Sol measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-5.6 Sol | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.8% | Not measured | Only GPT-5.6 Sol measured |
| Public Benefits Bench (Vals AI) | 66.5% | Not measured | Only GPT-5.6 Sol measured |
| SAGE (Vals AI) | 52.6% | Not measured | Only GPT-5.6 Sol measured |
| MedScribe (Vals AI) | 85.2% | Not measured | Only GPT-5.6 Sol measured |
| Vals Multimodal Index | 72.6% | Not measured | Only GPT-5.6 Sol measured |
| IFBench | 72.7% | 63.1% | GPT-5.6 Sol |
| Long context reasoning (AA-LCR) | 84.0% | 79.3% | GPT-5.6 Sol |
| DTBench | 95.7% | Not measured | Only GPT-5.6 Sol measured |
| LMCA | 58.5% | Not measured | Only GPT-5.6 Sol measured |
| Surface Evolver Bench | 93.1% | 48.8% | GPT-5.6 Sol |
| Furniture assembly | 56.7% | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: instruction following | 1483 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: creative writing | 1449 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision | 1281 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: English | 1457 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: non-English | 1451 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Chinese | 1525 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: German | 1476 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Japanese | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Korean | 1442 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: French | 1472 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Spanish | 1443 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Russian | 1471 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: Polish | 1459 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: multi-turn | 1461 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: longer queries | 1481 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: writing and language | 1462 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: English | 1280 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: Chinese | 1313 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: reading text | 1290 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: humor | 1246 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only GPT-5.6 Sol measured |
| LMArena Document | 1483 | Not measured | Only GPT-5.6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.6 Sol | 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-5.6 Sol | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | June 12, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $4.00 | $0.95 |
| Output $/Mtok | $20.00 | $4.00 |
| Coding index | 73.5 | 53.4 |
| Independent measurements behind it | 6 | 4 |
| Cost per solved task | $1.20 | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Sol better than Kimi K2.7 Code for coding?
Pick GPT-5.6 Sol for coding: it scores 73.5 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code. Pick Kimi K2.7 Code for output tokens 5.0x cheaper ($4.00 vs $20.00 per million) and a lower cost per solved task ($0.35 against $1.20).
Which is cheaper, GPT-5.6 Sol or Kimi K2.7 Code?
GPT-5.6 Sol costs $4.00 per million input tokens and $20.00 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Per solved SWE-bench Verified task, GPT-5.6 Sol costs $1.20 and Kimi K2.7 Code $0.35.
Which has the bigger context window, GPT-5.6 Sol or Kimi K2.7 Code?
GPT-5.6 Sol, with 1.05M tokens against 262K.
How many benchmarks is the GPT-5.6 Sol vs Kimi K2.7 Code comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Sol's coding index rests on 6 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.