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DeepSeek-V4.1-Flash-1017 vs Kimi-k1.5

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 16.9.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 16.9, ranking #15 overall.

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 — it leads those capability indexes
  • you want the most recent training data — it shipped Oct 2026
  • you need open weights you can self-host or fine-tune

Choose Kimi-k1.5

  • you are already invested in the Moonshot AI ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Kimi-k1.5
Moonshot AI
Core performance indexes
LLM Stats Score
51.0
#15
16.9
#242
Reasoning
48.6
#18
16.1
#239
Cost, coverage & limits
Benchmark wins
—
—
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

3 shared
Index
DeepSeek-V4.1-Flash-1017
Kimi-k1.5
Math
31.8#67
20.2#171
Vision
32.2#26
10.9#130
Multimodal
35.0#14
14.6#110
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 9 for Kimi-k1.5

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Kimi-k1.5don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

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-k1.5
Input- tokens
Output- tokens
Mon Oct 19 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and Kimi-k1.5 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-k1.5

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Kimi-k1.5 uses a proprietary 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-k1.5

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while Kimi-k1.5 was released on 2025-01-20.

DeepSeek-V4.1-Flash-1017 is 21 months newer than Kimi-k1.5.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.7yr newer
Kimi-k1.5

Jan 20, 2025

1.7 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-k1.5 side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
Kimi-k1.5
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs Kimi-k1.5.

Which is better, DeepSeek-V4.1-Flash-1017 or Kimi-k1.5?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 16.9. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Kimi-k1.5 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-k1.5 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-k1.5 scores MATH-500: 96.2%, CLUEWSC: 91.4%, C-Eval: 88.3%, MMLU: 87.4%, IFEval: 87.2%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and Kimi-k1.5?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Kimi-k1.5 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-k1.5?

Key differences include LLM Stats Score (51.0 vs 16.9), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and Kimi-k1.5?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and Kimi-k1.5 is developed by Moonshot AI.

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