Kimi K2.7 Code vs Qwen2.5 Coder 32B Instruct
Kimi K2.7 Code leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 25.8 to 6.7, and Qwen2.5 Coder 32B Instruct costs 4.0x less per output token ($1.00 vs $4.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Kimi K2.7 Code or Qwen2.5 Coder 32B Instruct: which to pick
Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 Coder 32B Instruct for output tokens 4.0x cheaper ($1.00 vs $4.00 per million).
- Shared results
- 4
- Kimi K2.7 Code only
- 47
- Qwen2.5 Coder 32B Instruct only
- 31
- Like-for-like categories
- 2 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
No call
Only Kimi K2.7 Code has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Kimi K2.7 Code
Kimi K2.7 Code leads, 25.8 against 6.7.
Same board, both measured
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, 1263.
Directional only
Long documents
Listed context window
Kimi K2.7 Code
Kimi K2.7 Code takes 262K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Kimi K2.7 Code $0.0029, Qwen2.5 Coder 32B Instruct $0.0012 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
- Kimi K2.7 Code 53.4 from 4 benchmarks, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Reasoning, 39.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Kimi K2.7 Code 89.6% against Qwen2.5 Coder 32B Instruct 41.7%, 47.9 points apart on a 0 to 100 scale.
- Output price
- Kimi K2.7 Code $4.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 4.0x apart.
- Context window
- Kimi K2.7 Code takes 262K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 | Kimi K2.7 Code | Bars | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 2 | 0 | ||
| Reasoning | 2 | 0 | ||
| All shared boards | 4 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index22 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena WebDev | 1474 | Not measured | Only Kimi K2.7 Code measured |
| AA Coding Index | 60.8 | Not measured | Only Kimi K2.7 Code measured |
| LiveCodeBench (Vals AI) | 82.0% | Not measured | Only Kimi K2.7 Code measured |
| SWE-bench Verified (Vals AI) | 78.2% | Not measured | Only Kimi K2.7 Code measured |
| Vibe Code Bench (Vals AI) | 47.2% | Not measured | Only Kimi K2.7 Code measured |
| Code migration (Vals AI) | 25.4% | Not measured | Only Kimi K2.7 Code measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Kimi K2.7 Code measured |
| Terminal-Bench 2.1 (Vals AI) | 67.0% | Not measured | Only Kimi K2.7 Code measured |
| SkillsBench (Vals AI) | 50.0% | Not measured | Only Kimi K2.7 Code measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.5% | Only Qwen2.5 Coder 32B Instruct measured |
| SciCode | 47.8% | Not measured | Only Kimi K2.7 Code measured |
| Terminal-Bench Hard (Artificial Analysis run) | 44.7% | Not measured | Only Kimi K2.7 Code measured |
| FrontierCode | 30.1% | Not measured | Only Kimi K2.7 Code measured |
| WeirdML | 54.1% | Not measured | Only Kimi K2.7 Code measured |
| ALE-Bench | 886.2 | Not measured | Only Kimi K2.7 Code measured |
| LMArena Text: coding | Not measured | 1276 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: software and IT | Not measured | 1261 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena WebDev: React | 1475 | Not measured | Only Kimi K2.7 Code measured |
| LMArena WebDev: HTML | 1451 | Not measured | Only Kimi K2.7 Code measured |
| LMArena WebDev: from an image | 1513 | Not measured | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 25.8 | 6.7 | Kimi K2.7 Code |
| Epoch Capabilities Index | 150.1 | 119.5 | Kimi K2.7 Code |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 21.0 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building web apps | 1176 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building full-stack apps | 1175 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building mobile apps | 1143 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building Android apps | 1138 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building Godot games | 1147 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: agents building games | 1130 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: HTML slides | 1151 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: PowerPoint slides | 1129 | Not measured | Only Kimi K2.7 Code measured |
| τ²-Bench Telecom | 90.1% | Not measured | Only Kimi K2.7 Code measured |
| APEX-Agents | 37.6% | Not measured | Only Kimi K2.7 Code measured |
| Vending-Bench 2 | $5,083 | Not measured | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.6% | 41.7% | Kimi K2.7 Code |
| Humanity's Last Exam (Artificial Analysis run) | 35.0% | 3.5% | Kimi K2.7 Code |
| GPQA Diamond (Epoch AI run) | 87.9% | Not measured | Only Kimi K2.7 Code measured |
| SimpleBench | 57.9% | Not measured | Only Kimi K2.7 Code measured |
| Chess puzzles | 14.0% | Not measured | Only Kimi K2.7 Code measured |
| LMArena Text: hard prompts | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: expert | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1269 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | 95.6% | Not measured | Only Kimi K2.7 Code measured |
| FrontierMath, tiers 1 to 3 | 54.0% | Not measured | Only Kimi K2.7 Code measured |
| FrontierMath, tier 4 | 12.2% | Not measured | Only Kimi K2.7 Code measured |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1243 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 63.5% | Only Qwen2.5 Coder 32B Instruct measured |
| SimpleQA Verified | 36.5% | Not measured | Only Kimi K2.7 Code measured |
| MMLU | Not measured | 79.1% | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | Not measured | 1221 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1187 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: legal and government | Not measured | 1257 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: business and finance | Not measured | 1224 | Only Qwen2.5 Coder 32B Instruct 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 | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1263 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: UI components | 1255 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: game dev | 1220 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: data viz | 1227 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: 3D | 1240 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: all code categories | 1252 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: SVG | 1176 | Not measured | Only Kimi K2.7 Code measured |
| Design Arena: ASCII art | 1196 | Not measured | 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 index16 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| IFBench | 63.1% | Not measured | Only Kimi K2.7 Code measured |
| Long context reasoning (AA-LCR) | 79.3% | Not measured | Only Kimi K2.7 Code measured |
| Surface Evolver Bench | 48.8% | Not measured | Only Kimi K2.7 Code measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | Not measured | 1230 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: instruction following | Not measured | 1223 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: creative writing | Not measured | 1174 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: English | Not measured | 1248 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: non-English | Not measured | 1205 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Chinese | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Russian | Not measured | 1228 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1222 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: longer queries | Not measured | 1251 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: writing and language | Not measured | 1191 | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1150 | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct | 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B Instruct 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 | MoonshotAI Kimi K2.7 Code | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Moonshot AI | Qwen |
| Weights | Open weights | Open weights |
| Released | June 12, 2026 | November 11, 2024 |
| Context window | 262K | 33K |
| Input $/Mtok | $0.95 | $0.66 |
| Output $/Mtok | $4.00 | $1.00 |
| Coding index | 53.4 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $0.35 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Kimi K2.7 Code better than Qwen2.5 Coder 32B Instruct for coding?
Only Kimi K2.7 Code has a coding score, 53.4 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 Coder 32B Instruct for output tokens 4.0x cheaper ($1.00 vs $4.00 per million).
Which is cheaper, Kimi K2.7 Code or Qwen2.5 Coder 32B Instruct?
Kimi K2.7 Code costs $0.95 per million input tokens and $4.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for Kimi K2.7 Code only.
Which has the bigger context window, Kimi K2.7 Code or Qwen2.5 Coder 32B Instruct?
Kimi K2.7 Code, with 262K tokens against 33K.
How many benchmarks is the Kimi K2.7 Code vs Qwen2.5 Coder 32B Instruct comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Kimi K2.7 Code's coding index rests on 4 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.