DeepSeek V4.1 Flash vs Kimi K2.5
Updated October 21, 2026. 65 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 65 shared
Decision reading
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 58.5 for Kimi K2.5.
Pick Kimi K2.5 when what matters is SAGE (Vals AI) or LMArena Vision: Chinese, where it leads.
Model ADeepSeek V4.1 FlashDeepSeek77.2/1002 independent benchmarksNo cost per solved taskModel BKimi K2.5Moonshot AI58.5/1002 independent benchmarks$0.24 per solved task
- Shared results
- 65
- DeepSeek V4.1 Flash only
- 36
- Kimi K2.5 only
- 33
- 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 58.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 24.7 against 23.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2.5 has a GPQA Diamond result, 84.1%.
Directional only
Math
AIME
No call
Only Kimi K2.5 has a AIME result, 95.6%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2.5 has a MMLU Pro result, 85.9%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1309 against 1249.
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 K2.5 $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Kimi K2.5 has the number, $0.24.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 77.2 from 2 benchmarks, Kimi K2.5 58.5 from 2 benchmarks.
- Widest category
- Coding, 26.5 points apart on average over 10 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 84.7% against Kimi K2.5 17.5%, 67.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.525 against Kimi K2.5 $2.50 per million output tokens, 4.8x apart.
- Context window
- DeepSeek V4.1 Flash takes 1.05M tokens, Kimi K2.5 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 K2.5 | Ties |
|---|---|---|---|---|
| Coding | 10 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 8 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| Web design | 8 | 0 | ||
| General | 23 | 3 | ||
| All shared boards | 62 | 3 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index100
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 70.8% | Only Kimi K2.5 measured |
| SWE-bench Multilingual | Not measured | 67.3% | Only Kimi K2.5 measured |
| LiveBench coding | 90.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev | 1618 | 1437 | DeepSeek V4.1 Flash |
| AA Coding Index | Not measured | 46.8 | Only Kimi K2.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 83.9% | Only Kimi K2.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Kimi K2.5 measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench (Vals AI) | 84.7% | 17.5% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | 7.0% | DeepSeek V4.1 Flash |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 42.0% | 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% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| SciCode | 51.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Kimi K2.5 measured |
| Terminal-Bench | Not measured | 43.2% | Only Kimi K2.5 measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| WeirdML | Not measured | 45.6% | Only Kimi K2.5 measured |
| ALE-Bench | 1092.3 | 821.6 | DeepSeek V4.1 Flash |
| LMArena Text: coding | 1502 | 1483 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1471 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | 1429 | DeepSeek V4.1 Flash |
| LMArena WebDev: HTML | 1612 | 1426 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | 1439 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 10 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 23.5 | DeepSeek V4.1 Flash |
| Epoch Capabilities Index | 155.9 | 148.1 | DeepSeek V4.1 Flash |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1136 | Only Kimi K2.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1114 | Only Kimi K2.5 measured |
| Design Arena: agents building mobile apps | Not measured | 1147 | Only Kimi K2.5 measured |
| Design Arena: agents building Android apps | Not measured | 1124 | Only Kimi K2.5 measured |
| Design Arena: agents building Godot games | Not measured | 1219 | Only Kimi K2.5 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% | 35.8% | DeepSeek V4.1 Flash |
| Tax Agent Bench (Vals AI) | 26.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| Legal Research Bench (Vals AI) | 41.4% | 15.9% | DeepSeek V4.1 Flash |
| Excel Modeling Benchmark (Vals AI) | 57.2% | 28.5% | DeepSeek V4.1 Flash |
| 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 | 95.9% | Only Kimi K2.5 measured |
| APEX-Agents | 39.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| OSWorld | Not measured | 63.3% | Only Kimi K2.5 measured |
| Vending-Bench 2 | Not measured | $1,198 | Only Kimi K2.5 measured |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 84.1% | Only Kimi K2.5 measured |
| 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 |
| MMMU Pro (Vals AI) | Not measured | 84.3% | Only Kimi K2.5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 87.9% | Only Kimi K2.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 30.7% | DeepSeek V4.1 Flash |
| GPQA Diamond (Epoch AI run) | 89.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI | 94.5% | 65.3% | DeepSeek V4.1 Flash |
| ARC-AGI-2 | 72.9% | 11.8% | DeepSeek V4.1 Flash |
| SimpleBench | Not measured | 46.8% | Only Kimi K2.5 measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | 1453 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1466 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1460 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1282 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1297 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 95.6% | Only Kimi K2.5 measured |
| OTIS Mock AIME | 98.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: math | 1478 | 1470 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1473 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 85.9% | Only Kimi K2.5 measured |
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| CaseLaw (Vals AI) | Not measured | 58.7% | Only Kimi K2.5 measured |
| CorpFin (Vals AI) | Not measured | 68.3% | Only Kimi K2.5 measured |
| TaxEval (Vals AI) | Not measured | 74.2% | Only Kimi K2.5 measured |
| MortgageTax (Vals AI) | Not measured | 66.5% | Only Kimi K2.5 measured |
| MedQA (Vals AI) | Not measured | 94.4% | Only Kimi K2.5 measured |
| MedCode (Vals AI) | 41.2% | 39.3% | DeepSeek V4.1 Flash |
| SimpleQA Verified | 42.9% | 34.3% | DeepSeek V4.1 Flash |
| LMArena Text: science | 1469 | 1459 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1448 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1441 | DeepSeek V4.1 Flash |
| LMArena Text: business and finance | 1461 | 1427 | DeepSeek V4.1 Flash |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1309 | 1249 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1314 | 1236 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1331 | 1207 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1290 | 1219 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1356 | 1205 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1320 | 1236 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1243 | 1149 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | 1165 | 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 index233
| Benchmark | DeepSeek V4.1 Flash | Kimi K2.5 | 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% | 49.9% | Kimi K2.5 |
| MedScribe (Vals AI) | 85.5% | 76.4% | DeepSeek V4.1 Flash |
| IFBench | Not measured | 70.2% | Only Kimi K2.5 measured |
| Long context reasoning (AA-LCR) | 84.0% | 78.0% | 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 |
| Fiction.LiveBench | Not measured | 86.1% | Only Kimi K2.5 measured |
| CL-bench | Not measured | 19.3% | Only Kimi K2.5 measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| CL-bench Life | Not measured | 13.2% | Only Kimi K2.5 measured |
| LMArena Text | 1464 | 1445 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1431 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1422 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1269 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1453 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1433 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1494 | DeepSeek V4.1 Flash |
| LMArena Text: German | 1470 | 1441 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1423 | DeepSeek V4.1 Flash |
| LMArena Text: Korean | 1436 | 1412 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1454 | DeepSeek V4.1 Flash |
| LMArena Text: Spanish | 1459 | 1450 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1435 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1445 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1444 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1445 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1425 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1417 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1274 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1328 | Kimi K2.5 |
| LMArena Vision: reading text | 1282 | 1276 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1284 | Kimi K2.5 |
| LMArena Vision: captioning | Not measured | 1257 | Only Kimi K2.5 measured |
| LMArena Vision: recognizing things | Not measured | 1285 | Only Kimi K2.5 measured |
| LMArena Vision: creative writing | 1275 | 1269 | DeepSeek V4.1 Flash |
| 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 | DeepSeek V4.1 Flash | Kimi K2.5 | 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 K2.5$0.0018Fits 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 K2.5$0.033Fits 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 K2.5$0.125Fits 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 K2.5 |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | September 10, 2026 | January 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.049 | $0.50 |
| Output $/Mtok | $0.525 | $2.50 |
| Coding index | 77.2 | 58.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.24 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Kimi K2.5 for coding?
Pick DeepSeek V4.1 Flash for coding: it scores 77.2 on the Vibe Coding Index against 58.5 for Kimi K2.5. Pick Kimi K2.5 when what matters is SAGE (Vals AI) or LMArena Vision: Chinese, where it leads.
Which is cheaper, DeepSeek V4.1 Flash or Kimi K2.5?
DeepSeek V4.1 Flash costs $0.049 per million input tokens and $0.525 per million output tokens; Kimi K2.5 costs $0.50 and $2.50. Cost per solved task is known for Kimi K2.5 only.
Which has the bigger context window, DeepSeek V4.1 Flash or Kimi K2.5?
DeepSeek V4.1 Flash, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4.1 Flash vs Kimi K2.5 comparison based on?
65 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 K2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.