Enter opens the highlighted result. Esc closes.

DeepSeek V4.1 Flash 1017 vs Kimi K2.7 Code

DeepSeek V4.1 Flash 1017 leads Kimi K2.7 Code on coding, 70.3 to 53.4 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 6.7x less per output token ($0.60 vs $4.00 per million). Updated October 22, 2026; 23 benchmarks have a result for both.

Replace DeepSeek V4.1 Flash 1017 Replace Kimi K2.7 Code
DeepSeek V4.1 Flash 1017DeepSeek
Coding
70.3
Overall
67.6
Output $/M
$0.60
23
Kimi K2.7 CodeMoonshot AI
Coding
53.4
Overall
56.7
Output $/M
$4.00
0
vs boards won of 23 shared

DeepSeek V4.1 Flash 1017 or Kimi K2.7 Code: which to pick

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code.

Kimi K2.7 Code is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Model ADeepSeek V4.1 Flash 1017DeepSeek70.3/1004 independent benchmarksNo cost per solved taskModel BKimi K2.7 CodeMoonshot AI53.4/1004 independent benchmarks$0.35 per solved task
Shared results
23
DeepSeek V4.1 Flash 1017 only
16
Kimi K2.7 Code only
28
Like-for-like categories
7 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.

At a glance

Coding index
DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Kimi K2.7 Code 53.4 from 4 benchmarks.
Widest category
Coding, 23.6 points apart on average over 7 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 1017 87.7% against Kimi K2.7 Code 47.2%, 40.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V4.1 Flash 1017 $0.60 against Kimi K2.7 Code $4.00 per million output tokens, 6.7x apart.
Context window
DeepSeek V4.1 Flash 1017 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.

Boards won, where both have a result
CategoryDeepSeek V4.1 Flash 1017BarsKimi K2.7 CodeTies
Coding70
Intelligence10
Reasoning30
Math30
Knowledge10
Web design70
General10
All shared boards230
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
Coding benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
LiveBench coding96.0%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
LMArena WebDev16641474DeepSeek V4.1 Flash 1017
AA Coding IndexNot measured60.8Only Kimi K2.7 Code measured
LiveCodeBench (Vals AI)Not measured82.0%Only Kimi K2.7 Code measured
SWE-bench Verified (Vals AI)Not measured78.2%Only Kimi K2.7 Code measured
Terminal-Bench 4 (Vals AI)31.8%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Vibe Code Bench (Vals AI)87.7%47.2%DeepSeek V4.1 Flash 1017
Code migration (Vals AI)44.2%25.4%DeepSeek V4.1 Flash 1017
ProgramBench (Vals AI)Not measured0.0%Only Kimi K2.7 Code measured
Terminal-Bench 2.1 (Vals AI)Not measured67.0%Only Kimi K2.7 Code measured
SkillsBench (Vals AI)Not measured50.0%Only Kimi K2.7 Code measured
SciCode55.9%47.8%DeepSeek V4.1 Flash 1017
Terminal-Bench Hard (Artificial Analysis run)Not measured44.7%Only Kimi K2.7 Code measured
FrontierCodeNot measured30.1%Only Kimi K2.7 Code measured
WeirdMLNot measured54.1%Only Kimi K2.7 Code measured
ALE-Bench1622.0886.2DeepSeek V4.1 Flash 1017
LMArena WebDev: React16671475DeepSeek V4.1 Flash 1017
LMArena WebDev: HTML16871451DeepSeek V4.1 Flash 1017
LMArena WebDev: from an imageNot measured1513Only Kimi K2.7 Code measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
AA Intelligence Index39.525.8DeepSeek V4.1 Flash 1017
Epoch Capabilities IndexNot measured150.1Only Kimi K2.7 Code measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
Agentic work benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
AA Agentic IndexNot measured21.0Only Kimi K2.7 Code measured
Design Arena: agents building web appsNot measured1176Only Kimi K2.7 Code measured
Design Arena: agents building full-stack appsNot measured1175Only Kimi K2.7 Code measured
Design Arena: agents building mobile appsNot measured1143Only Kimi K2.7 Code measured
Design Arena: agents building Android appsNot measured1138Only Kimi K2.7 Code measured
Design Arena: agents building Godot gamesNot measured1147Only Kimi K2.7 Code measured
Design Arena: agents building gamesNot measured1130Only Kimi K2.7 Code measured
Design Arena: HTML slidesNot measured1151Only Kimi K2.7 Code measured
Design Arena: PowerPoint slidesNot measured1129Only Kimi K2.7 Code measured
Design Arena: spreadsheets1245Not measuredOnly DeepSeek V4.1 Flash 1017 measured
LiveBench research79.5%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
LiveBench browser76.0%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
LiveBench long agentic50.9%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Finance Agent (Vals AI)57.0%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Tax Agent Bench (Vals AI)44.4%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Legal Research Bench (Vals AI)45.2%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Harvey's Legal Agent Benchmark (Vals AI)14.2%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Excel Modeling Benchmark (Vals AI)61.8%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
τ²-Bench TelecomNot measured90.1%Only Kimi K2.7 Code measured
APEX-AgentsNot measured37.6%Only Kimi K2.7 Code measured
Vending-Bench 2Not measured$5,083Only Kimi K2.7 Code measured
Neither model has a result on the other 20 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
LiveBench reasoning89.1%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
GPQA Diamond (Artificial Analysis run)Not measured89.6%Only Kimi K2.7 Code measured
Humanity's Last Exam (Artificial Analysis run)43.3%35.0%DeepSeek V4.1 Flash 1017
GPQA Diamond (Epoch AI run)93.2%87.9%DeepSeek V4.1 Flash 1017
SimpleBenchNot measured57.9%Only Kimi K2.7 Code measured
Chess puzzles46.0%14.0%DeepSeek V4.1 Flash 1017
Mystery game puzzles49.0%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
OTIS Mock AIME99.4%95.6%DeepSeek V4.1 Flash 1017
FrontierMath, tiers 1 to 371.2%54.0%DeepSeek V4.1 Flash 1017
FrontierMath, tier 446.3%12.2%DeepSeek V4.1 Flash 1017
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
SimpleQA Verified42.5%36.5%DeepSeek V4.1 Flash 1017
Neither model has a result on the other 17 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
Design Arena: websites13111263DeepSeek V4.1 Flash 1017
Design Arena: UI components13031255DeepSeek V4.1 Flash 1017
Design Arena: game dev13571220DeepSeek V4.1 Flash 1017
Design Arena: data viz13301227DeepSeek V4.1 Flash 1017
Design Arena: 3D13581240DeepSeek V4.1 Flash 1017
Design Arena: all code categories13321252DeepSeek V4.1 Flash 1017
Design Arena: SVGNot measured1176Only Kimi K2.7 Code measured
Design Arena: ASCII art12421196DeepSeek V4.1 Flash 1017
LiveBench design83.4%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
General benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
Vals Index61.3%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
LiveBench data analysis79.5%Not measuredOnly DeepSeek V4.1 Flash 1017 measured
IFBenchNot measured63.1%Only Kimi K2.7 Code measured
Long context reasoning (AA-LCR)82.7%79.3%DeepSeek V4.1 Flash 1017
Surface Evolver BenchNot measured48.8%Only Kimi K2.7 Code 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
Image generation benchmarks for DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
BenchmarkDeepSeek V4.1 Flash 1017Kimi K2.7 CodeLeader
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$0.0004Fits in one request
Kimi K2.7 Code$0.0029Fits in one request

DeepSeek V4.1 Flash 1017 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4.1 Flash 1017$0.0093Fits in one request
Kimi K2.7 Code$0.059Fits in one request

DeepSeek V4.1 Flash 1017 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4.1 Flash 1017$0.036Fits in one request
Kimi K2.7 Code$0.230Fits in one request

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.

Specifications of DeepSeek V4.1 Flash 1017 and Kimi K2.7 Code
SpecificationDeepSeek V4.1 Flash 1017MoonshotAI Kimi K2.7 Code
MakerDeepSeekMoonshot AI
WeightsOpen weightsOpen weights
ReleasedOctober 17, 2026June 12, 2026
Context window1.05M262K
Input $/Mtok$0.15$0.95
Output $/Mtok$0.60$4.00
Coding index70.353.4
Independent measurements behind it44
Cost per solved taskNot computable$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4.1 Flash 1017 better than Kimi K2.7 Code for coding?

Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 53.4 for Kimi K2.7 Code. Kimi K2.7 Code is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Which is cheaper, DeepSeek V4.1 Flash 1017 or Kimi K2.7 Code?

DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Kimi K2.7 Code costs $0.95 and $4.00. Cost per solved task is known for Kimi K2.7 Code only.

Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Kimi K2.7 Code?

DeepSeek V4.1 Flash 1017, with 1.05M tokens against 262K.

How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Kimi K2.7 Code comparison based on?

23 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 K2.7 Code's on 4. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/deepseek-v4-1-flash-1017-vs-kimi-k2-7-code" title="DeepSeek V4.1 Flash 1017 vs Kimi K2.7 Code, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/deepseek-v4-1-flash-1017-vs-kimi-k2-7-code">LLMBenchmark.ai, DeepSeek V4.1 Flash 1017 vs Kimi K2.7 Code</a></p>