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

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 38.7. DeepSeek-V4.1-Flash-1017 is 2.6x cheaper per token.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 38.7, ranking #15 overall.

The models split the 2 individual benchmarks reported for both models evenly.

On price, DeepSeek-V4.1-Flash-1017 is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V4.1-Flash-1017 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V4.1-Flash-1017

  • overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • cost matters — it's about 2.6x cheaper per token
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Oct 2026

Choose Kimi K2.7 Code

  • you want predictable pricing at $0.68/M input and $3.40/M output

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Kimi K2.7 Code
Moonshot AI
Core performance indexes
LLM Stats Score
51.2
#15
38.7
#79
Reasoning
48.8
#18
33.8
#112
Coding
39.3
#14
28.7
#70
Agents
38.7
#8
25.9
#66
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
Input price
$0.30 / M
$0.68 / M
Output price
$1.20 / M
$3.40 / M
Context window
1,048,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
Kimi K2.7 Code
Tool use
31.3#6
23.0#50
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 9 for Kimi K2.7 Code

2 shared

DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (DeepSWE 1.1), while Kimi K2.7 Code is better at 1 benchmark (Program Bench).

Both models are evenly matched across the benchmarks.

Thu Oct 22 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

DeepSeek-V4.1-Flash-1017 costs less

For input processing, DeepSeek-V4.1-Flash-1017 ($0.30/1M tokens) is 2.3x cheaper than Kimi K2.7 Code ($0.68/1M tokens).

For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.8x cheaper than Kimi K2.7 Code ($3.40/1M tokens).

In conclusion, Kimi K2.7 Code is more expensive than DeepSeek-V4.1-Flash-1017.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Thu Oct 22 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash-1017
Input tokens$0.30
Output tokens$1.20
Best providerDeepSeek
Moonshot AI
Kimi K2.7 Code
Input tokens$0.68
Output tokens$3.40
Best providerDeepinfra
Notice missing or incorrect data?

Model Size

Parameter count comparison

236.8B diff

Kimi K2.7 Code has 236.8B more parameters than DeepSeek-V4.1-Flash-1017, making it 31.0% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
Moonshot AI
Kimi K2.7 Code
1.0Tparameters
763.2B
DeepSeek-V4.1-Flash-1017
1000.0B
Kimi K2.7 Code

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Kimi K2.7 Code is limited to 262,144 tokens.

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Moonshot AI
Kimi K2.7 Code
Input262,144 tokens
Output262,144 tokens
Thu Oct 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Kimi K2.7 Code support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Kimi K2.7 Code

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Kimi K2.7 Code

Modified MIT License

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Kimi K2.7 Code was released on 2026-06-12.

DeepSeek-V4.1-Flash-1017 is 4 months newer than Kimi K2.7 Code.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

5 days ago

4mo newer
Kimi K2.7 Code

Jun 12, 2026

4 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.

DeepSeek-V4.1-Flash-1017

deepseek logo
DeepSeek
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Kimi K2.7 Code

deepinfra logo
Deepinfra
Input Price:Input: $0.68/1MOutput Price:Output: $3.40/1M
fireworks logo
Fireworks
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
moonshot logo
Unknown Organization
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
novita logo
Novita
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
together logo
Together
Input Price:Input: $0.95/1MOutput Price:Output: $4.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Kimi K2.7 Code side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Kimi K2.7 Code
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Kimi K2.7 Code.

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

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 38.7. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Kimi K2.7 Code is made by Moonshot AI. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V4.1-Flash-1017 compare to Kimi K2.7 Code in benchmarks?

DeepSeek-V4.1-Flash-1017 scores BabyVision: 92.5%, Chartography: 83.0%, DeepSWE 1.1: 77.0%, NL2Repo: 68.2%, AutomationBench: 60.5%. Kimi K2.7 Code scores MCP-Mark: 81.1%, MCP Atlas: 76.0%, LiveBench: 71.9%, Kimi Code Bench v2: 62.0%, Program Bench: 53.6%.

Is DeepSeek-V4.1-Flash-1017 cheaper than Kimi K2.7 Code?

DeepSeek-V4.1-Flash-1017 is 2.3x cheaper for input tokens. DeepSeek-V4.1-Flash-1017 costs $0.30/M input and $1.20/M output via deepseek. Kimi K2.7 Code costs $0.68/M input and $3.40/M output via deepinfra.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Kimi K2.7 Code?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Kimi K2.7 Code supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-V4.1-Flash-1017 and Kimi K2.7 Code?

Key differences include LLM Stats Score (51.2 vs 38.7), context window (1.0M vs 262K), input pricing ($0.30 vs $0.68/M), licensing (MIT vs Modified MIT License). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Kimi K2.7 Code?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Kimi K2.7 Code is developed by Moonshot AI.

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