DeepSeek V4.1 Flash 1017 vs Kimi K2 0905
DeepSeek V4.1 Flash 1017 leads Kimi K2 0905 on the Artificial Analysis Intelligence Index, 39.5 to 15.3, and DeepSeek V4.1 Flash 1017 costs 4.2x less per output token ($0.60 vs $2.50 per million). Updated October 22, 2026; 6 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or Kimi K2 0905: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Kimi K2 0905 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 6
- DeepSeek V4.1 Flash 1017 only
- 33
- Kimi K2 0905 only
- 11
- Like-for-like categories
- 5 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 DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 15.3.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1088.
Same board, both measured
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, Kimi K2 0905 $0.0019 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Kimi K2 0905 not benchmarked.
- Widest category
- Coding, 45.2 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: DeepSeek V4.1 Flash 1017 1622.0 against Kimi K2 0905 267.1, 45.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Kimi K2 0905 $2.50 per million output tokens, 4.2x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, Kimi K2 0905 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 1017 | Bars | Kimi K2 0905 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Web design | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 6 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 61.0% | Only Kimi K2 0905 measured |
| SciCode | 55.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only Kimi K2 0905 measured |
| WeirdML | Not measured | 36.7% | Only Kimi K2 0905 measured |
| ALE-Bench | 1622.0 | 267.1 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 15.3 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1245 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | Not measured | 73.4% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 76.7% | Only Kimi K2 0905 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 6.4% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Kagi LLM Benchmark | Not measured | 52.2% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 57.3 | Only Kimi K2 0905 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 57.3% | Only Kimi K2 0905 measured |
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 81.9% | Only Kimi K2 0905 measured |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | 1088 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1303 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1357 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1330 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1358 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1332 | 1098 | DeepSeek V4.1 Flash 1017 |
| Design Arena: ASCII art | 1242 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| IFBench | Not measured | 41.7% | Only Kimi K2 0905 measured |
| Long context reasoning (AA-LCR) | 82.7% | 53.0% | DeepSeek V4.1 Flash 1017 |
| Fiction.LiveBench | Not measured | 66.7% | Only Kimi K2 0905 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K2 0905 | 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 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 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 1017 | MoonshotAI Kimi K2 0905 |
|---|---|---|
| Maker | DeepSeek | Moonshot AI |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | September 4, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $0.60 |
| Output $/Mtok | $0.60 | $2.50 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Kimi K2 0905 for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Kimi K2 0905 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Kimi K2 0905?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Kimi K2 0905 costs $0.60 and $2.50. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Kimi K2 0905?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Kimi K2 0905 comparison based on?
6 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and Kimi K2 0905's on 0. Where only one model has a result, the page says so instead of naming a winner.