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DeepSeek-V4.1-Flash-1017 vs Kimi K2-Instruct-0905

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 21.3.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 21.3, ranking #15 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash-1017 wins 1; this is a narrower head-to-head signal than the composite indexes.

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 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Oct 2026

Choose Kimi K2-Instruct-0905

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Kimi K2-Instruct-0905
Moonshot AI
Core performance indexes
LLM Stats Score
51.0
#15
21.3
#210
Reasoning
48.6
#18
21.6
#199
Coding
39.1
#15
9.5
#191
Agents
38.6
#9
-4.2
#205
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.30 / M
— / M
Output price
$1.20 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
DeepSeek-V4.1-Flash-1017
Kimi K2-Instruct-0905
Math
31.8#67
21.4#154
Tool use
30.9#7
6.3#162
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 29 for Kimi K2-Instruct-0905

1 shared

DeepSeek-V4.1-Flash-1017 outperforms in 1 benchmarks (Humanity's Last Exam), while Kimi K2-Instruct-0905 is better at 0 benchmarks.

DeepSeek-V4.1-Flash-1017 significantly outperforms across most benchmarks.

Mon Oct 19 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

236.8B diff

Kimi K2-Instruct-0905 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-Instruct-0905
1.0Tparameters
763.2B
DeepSeek-V4.1-Flash-1017
1000.0B
Kimi K2-Instruct-0905

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Moonshot AI
Kimi K2-Instruct-0905
Input- tokens
Output- tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas Kimi K2-Instruct-0905 does not.

DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4.1-Flash-1017

Text
Images
Audio
Video

Kimi K2-Instruct-0905

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-V4.1-Flash-1017

MIT

Open weights

Kimi K2-Instruct-0905

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Kimi K2-Instruct-0905 was released on 2025-09-05.

DeepSeek-V4.1-Flash-1017 is 14 months newer than Kimi K2-Instruct-0905.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.1yr newer
Kimi K2-Instruct-0905

Sep 5, 2025

1.1 years 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

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and Kimi K2-Instruct-0905 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Kimi K2-Instruct-0905
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Kimi K2-Instruct-0905.

Which is better, DeepSeek-V4.1-Flash-1017 or Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 21.3. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Kimi K2-Instruct-0905 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-Instruct-0905 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-Instruct-0905 scores MATH-500: 97.4%, MMLU-Redux: 92.7%, IFEval: 89.8%, AutoLogi: 89.5%, MMLU: 89.5%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Kimi K2-Instruct-0905 supports an unknown number of 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-Instruct-0905?

Key differences include LLM Stats Score (51.0 vs 21.3), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Kimi K2-Instruct-0905?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Kimi K2-Instruct-0905 is developed by Moonshot AI.

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