GPT-4o-mini (2024-07-18) vs Kimi K2.7 Code
Updated October 21, 2026. 7 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 K2.7 Code for coding: it scores 39.1 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18).
Pick GPT-4o-mini (2024-07-18) when output tokens cost 5.6x less.
- Shared results
- 7
- GPT-4o-mini (2024-07-18) only
- 39
- Kimi K2.7 Code only
- 44
- Like-for-like categories
- 4 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 K2.7 Code
Kimi K2.7 Code scores 39.1 against 3.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only Kimi K2.7 Code has a AA Intelligence Index result, 25.8.
Directional only
Agentic work
AA Agentic Index
No call
Only Kimi K2.7 Code has a AA Agentic Index result, 21.0.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2.7 Code has a Design Arena: websites result, 1264.
Directional only
Long documents
Listed context window
Kimi K2.7 Code
Kimi K2.7 Code takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
GPT-4o-mini (2024-07-18) $0.0004, Kimi K2.7 Code $0.0023 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Kimi K2.7 Code has the number, $0.35.
Directional only
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) 3.6 from 1 benchmark, Kimi K2.7 Code 39.1 from 1 benchmark.
- Widest category
- Math, 71.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-4o-mini (2024-07-18) 6.9% against Kimi K2.7 Code 95.6%, 88.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Kimi K2.7 Code $3.35 per million output tokens, 5.6x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K 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-4o-mini (2024-07-18) | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena WebDev | Not measured | 1474 | Only Kimi K2.7 Code measured |
| AA Coding Index | Not measured | 60.8 | Only Kimi K2.7 Code measured |
| LiveCodeBench (Vals AI) | Not measured | 82.0% | Only Kimi K2.7 Code measured |
| SWE-bench Verified (Vals AI) | Not measured | 78.2% | Only Kimi K2.7 Code measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.2% | Only Kimi K2.7 Code measured |
| Code migration (Vals AI) | Not measured | 25.4% | Only Kimi K2.7 Code measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Kimi K2.7 Code measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.0% | Only Kimi K2.7 Code measured |
| SkillsBench (Vals AI) | Not measured | 50.0% | Only Kimi K2.7 Code measured |
| SciCode | Not measured | 47.8% | Only Kimi K2.7 Code measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 44.7% | Only Kimi K2.7 Code measured |
| FrontierCode | Not measured | 30.1% | Only Kimi K2.7 Code measured |
| WeirdML | 11.8% | 54.1% | Kimi K2.7 Code |
| ALE-Bench | Not measured | 886.2 | Only Kimi K2.7 Code measured |
| LMArena Text: coding | 1290 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: software and IT | 1291 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena WebDev: React | Not measured | 1475 | Only Kimi K2.7 Code measured |
| LMArena WebDev: HTML | Not measured | 1451 | Only Kimi K2.7 Code measured |
| LMArena WebDev: from an image | Not measured | 1513 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 25.8 | Only Kimi K2.7 Code measured |
| Epoch Capabilities Index | Not measured | 150.1 | Only Kimi K2.7 Code measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 21.0 | Only Kimi K2.7 Code measured |
| Design Arena: agents building web apps | Not measured | 1176 | Only Kimi K2.7 Code measured |
| Design Arena: agents building full-stack apps | Not measured | 1175 | Only Kimi K2.7 Code measured |
| Design Arena: agents building mobile apps | Not measured | 1143 | Only Kimi K2.7 Code measured |
| Design Arena: agents building Android apps | Not measured | 1143 | Only Kimi K2.7 Code measured |
| Design Arena: agents building Godot games | Not measured | 1147 | Only Kimi K2.7 Code measured |
| Design Arena: agents building games | Not measured | 1130 | Only Kimi K2.7 Code measured |
| Design Arena: HTML slides | Not measured | 1201 | Only Kimi K2.7 Code measured |
| Design Arena: PowerPoint slides | Not measured | 1154 | Only Kimi K2.7 Code measured |
| τ²-Bench Telecom | Not measured | 90.1% | Only Kimi K2.7 Code measured |
| APEX-Agents | Not measured | 37.6% | Only Kimi K2.7 Code measured |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Vending-Bench 2 | Not measured | $5,083 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.6% | Only Kimi K2.7 Code measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.0% | Only Kimi K2.7 Code measured |
| GPQA Diamond (Epoch AI run) | 37.7% | 87.9% | Kimi K2.7 Code |
| SimpleBench | 10.7% | 57.9% | Kimi K2.7 Code |
| Chess puzzles | 3.0% | 14.0% | Kimi K2.7 Code |
| Mystery game puzzles | 12.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: expert | 1234 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts in English | 1278 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.9% | 95.6% | Kimi K2.7 Code |
| FrontierMath, tiers 1 to 3 | 0.7% | 54.0% | Kimi K2.7 Code |
| FrontierMath, tier 4 | Not measured | 12.2% | Only Kimi K2.7 Code measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: mathematical work | 1271 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| SimpleQA Verified | 8.3% | 36.5% | Kimi K2.7 Code |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: medicine and healthcare | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: legal and government | 1305 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: business and finance | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1264 | Only Kimi K2.7 Code measured |
| Design Arena: UI components | Not measured | 1259 | Only Kimi K2.7 Code measured |
| Design Arena: game dev | Not measured | 1220 | Only Kimi K2.7 Code measured |
| Design Arena: data viz | Not measured | 1226 | Only Kimi K2.7 Code measured |
| Design Arena: 3D | Not measured | 1241 | Only Kimi K2.7 Code measured |
| Design Arena: all code categories | Not measured | 1253 | Only Kimi K2.7 Code measured |
| Design Arena: SVG | Not measured | 1176 | Only Kimi K2.7 Code measured |
| Design Arena: ASCII art | Not measured | 1196 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Kimi K2.7 Code | Leader |
|---|---|---|---|
| IFBench | Not measured | 63.1% | Only Kimi K2.7 Code measured |
| Long context reasoning (AA-LCR) | Not measured | 79.3% | Only Kimi K2.7 Code measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Surface Evolver Bench | Not measured | 48.8% | Only Kimi K2.7 Code measured |
| LMArena Text | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: instruction following | 1258 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: creative writing | 1268 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision | 1066 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: English | 1301 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: non-English | 1266 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Chinese | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: German | 1272 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Japanese | 1216 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Korean | 1195 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: French | 1297 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Spanish | 1276 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Russian | 1275 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Polish | 1244 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: multi-turn | 1285 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: longer queries | 1289 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: writing and language | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: entertainment and media | 1253 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: English | 1072 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: Chinese | 1033 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | 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
GPT-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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-4o-mini (2024-07-18) | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | OpenAI | Moonshot AI |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | June 12, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.15 | $0.6712 |
| Output $/Mtok | $0.60 | $3.35 |
| Coding index | 3.6 | 39.1 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini (2024-07-18) better than Kimi K2.7 Code for coding?
Pick Kimi K2.7 Code for coding: it scores 39.1 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18). Pick GPT-4o-mini (2024-07-18) when output tokens cost 5.6x less.
Which is cheaper, GPT-4o-mini (2024-07-18) or Kimi K2.7 Code?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Kimi K2.7 Code costs $0.6712 and $3.35. Cost per solved task is known for Kimi K2.7 Code only.
Which has the bigger context window, GPT-4o-mini (2024-07-18) or Kimi K2.7 Code?
Kimi K2.7 Code, with 262K tokens against 128K.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs Kimi K2.7 Code comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 1 independent benchmark and Kimi K2.7 Code's on 1. Where only one model has a result, the page says so instead of naming a winner.