DeepSeek V4.1 Flash vs Kimi K2.7 Code
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.
vs boards won of 32 shared
Decision reading
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code.
Pick Kimi K2.7 Code when what matters is AA Intelligence Index or Surface Evolver Bench, where it leads.
Model ADeepSeek V4.1 FlashDeepSeek77.2/1002 independent benchmarksNo cost per solved taskModel BKimi K2.7 CodeMoonshot AI39.1/1001 independent benchmark$0.35 per solved task
- Shared results
- 32
- DeepSeek V4.1 Flash only
- 69
- Kimi K2.7 Code only
- 19
- 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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash scores 77.2 against 39.1.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Kimi K2.7 Code
Kimi K2.7 Code leads, 25.8 against 24.7.
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
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1309 against 1264.
Same board, both measured
Long documents
Listed context window
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash $0.0003, 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
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, Kimi K2.7 Code 39.1 from 1 benchmark.
- Widest category
- Coding, 15.9 points apart on average over 12 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 84.7% against Kimi K2.7 Code 47.2%, 37.5 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against Kimi K2.7 Code $3.35 per million output tokens, 6.4x apart.
- Context window
- DeepSeek V4.1 Flash 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 | DeepSeek V4.1 Flash | Bars | Kimi K2.7 Code | Ties |
|---|---|---|---|---|
| Coding | 12 | 0 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 1 | 0 | ||
| Web design | 8 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 30 | 2 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index120
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1474 | DeepSeek V4.1 Flash |
| 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 |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 47.2% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | 25.4% | DeepSeek V4.1 Flash |
| ProgramBench (Vals AI) | 0.5% | 0.0% | DeepSeek V4.1 Flash |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 67.0% | DeepSeek V4.1 Flash |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | 50.0% | DeepSeek V4.1 Flash |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| SciCode | 51.9% | 47.8% | DeepSeek V4.1 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 44.7% | Only Kimi K2.7 Code measured |
| FrontierCode | 43.9% | 30.1% | DeepSeek V4.1 Flash |
| WeirdML | Not measured | 54.1% | Only Kimi K2.7 Code measured |
| ALE-Bench | 1092.3 | 886.2 | DeepSeek V4.1 Flash |
| LMArena Text: coding | 1502 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: software and IT | 1496 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: React | 1628 | 1475 | DeepSeek V4.1 Flash |
| LMArena WebDev: HTML | 1612 | 1451 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | 1513 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 25.8 | Kimi K2.7 Code |
| Epoch Capabilities Index | 155.9 | 150.1 | DeepSeek V4.1 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash | 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 |
| LiveBench research | 86.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench browser | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LiveBench long agentic | 40.1% | Not measured | Only DeepSeek V4.1 Flash measured |
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 90.1% | Only Kimi K2.7 Code measured |
| APEX-Agents | 39.5% | 37.6% | DeepSeek V4.1 Flash |
| Vending-Bench 2 | Not measured | $5,083 | Only Kimi K2.7 Code measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench reasoning | 86.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.6% | Only Kimi K2.7 Code measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 35.0% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | 87.9% | DeepSeek V4.1 Flash |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| SimpleBench | Not measured | 57.9% | Only Kimi K2.7 Code measured |
| Chess puzzles | 48.0% | 14.0% | DeepSeek V4.1 Flash |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: expert | 1501 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts in English | 1488 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: diagrams | 1297 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: homework | 1307 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 95.6% | DeepSeek V4.1 Flash |
| FrontierMath, tiers 1 to 3 | 67.4% | 54.0% | DeepSeek V4.1 Flash |
| FrontierMath, tier 4 | 26.8% | 12.2% | DeepSeek V4.1 Flash |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: math | 1478 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: mathematical work | 1485 | Not measured | Only DeepSeek V4.1 Flash 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 | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| SimpleQA Verified | 42.9% | 36.5% | DeepSeek V4.1 Flash |
| LMArena Text: science | 1469 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: medicine and healthcare | 1484 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: legal and government | 1443 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: business and finance | 1461 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1264 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1314 | 1259 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1331 | 1220 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1226 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1241 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1253 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1243 | 1176 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | 1196 | DeepSeek V4.1 Flash |
| LiveBench design | 79.2% | Not measured | Only DeepSeek V4.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.7 Code | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 47.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedScribe (Vals AI) | 85.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IFBench | Not measured | 63.1% | Only Kimi K2.7 Code measured |
| Long context reasoning (AA-LCR) | 84.0% | 79.3% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMCA | 47.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Surface Evolver Bench | 46.3% | 48.8% | Kimi K2.7 Code |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: instruction following | 1473 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: creative writing | 1437 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision | 1278 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: English | 1473 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: non-English | 1449 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Chinese | 1501 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: German | 1470 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Japanese | 1432 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Korean | 1436 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: French | 1465 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Spanish | 1459 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Russian | 1467 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Polish | 1457 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: multi-turn | 1461 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: longer queries | 1473 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: writing and language | 1450 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: entertainment and media | 1445 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: English | 1288 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: Chinese | 1327 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1282 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: humor | 1260 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1275 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | 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
DeepSeek V4.1 Flash$0.0003Fits in one request
Kimi K2.7 Code$0.0023Fits in one request
DeepSeek V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.0040Fits in one request
Kimi K2.7 Code$0.044Fits in one request
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.015Fits in one request
Kimi K2.7 Code$0.168Fits in one request
DeepSeek V4.1 Flash 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 | DeepSeek V4.1 Flash | MoonshotAI Kimi K2.7 Code |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | June 12, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.049 | $0.6712 |
| Output $/Mtok | $0.525 | $3.35 |
| Coding index | 77.2 | 39.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $0.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Kimi K2.7 Code for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code. Pick Kimi K2.7 Code when what matters is AA Intelligence Index or Surface Evolver Bench, where it leads.
Which is cheaper, DeepSeek V4.1 Flash or Kimi K2.7 Code?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 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, DeepSeek V4.1 Flash or Kimi K2.7 Code?
DeepSeek V4.1 Flash, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4.1 Flash vs Kimi K2.7 Code comparison based on?
32 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash's coding index rests on 2 independent benchmarks and Kimi K2.7 Code's on 1. Where only one model has a result, the page says so instead of naming a winner.