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

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 21.4. DeepSeek-V4.1-Flash-1017 is 2.0x cheaper per token.

DeepSeek · Moonshot AI · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.0 to 21.4, ranking #15 overall.

On price, DeepSeek-V4.1-Flash-1017 is roughly 2.0x 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 51.0 and ranks #15 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • cost matters — it's about 2.0x 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
  • you need open weights you can self-host or fine-tune

Choose Kimi K2 0905

  • you want predictable pricing at $0.60/M input and $2.50/M output

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
Kimi K2 0905
Moonshot AI
Core performance indexes
LLM Stats Score
51.0
#15
21.4
#209
Reasoning
48.6
#18
21.7
#196
Coding
39.1
#15
19.3
#122
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
$0.60 / M
Output price
$1.20 / M
$2.50 / 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 0905
Math
31.8#67
24.3#127
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 6 for Kimi K2 0905

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and Kimi K2 0905don'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

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 2.0x cheaper than Kimi K2 0905 ($0.60/1M tokens).

For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.1x cheaper than Kimi K2 0905 ($2.50/1M tokens).

In conclusion, Kimi K2 0905 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
Mon Oct 19 2026 • llm-stats.com
DeepSeek
DeepSeek-V4.1-Flash-1017
Input tokens$0.30
Output tokens$1.20
Best providerDeepSeek
Moonshot AI
Kimi K2 0905
Input tokens$0.60
Output tokens$2.50
Best providerNovita
Notice missing or incorrect data?

Model Size

Parameter count comparison

236.8B diff

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

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Kimi K2 0905's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Kimi K2 0905 is limited to 262,144 tokens.

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
Moonshot AI
Kimi K2 0905
Input262,144 tokens
Output262,144 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 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 0905

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Kimi K2 0905 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 K2 0905

Proprietary

Closed source

Release Timeline

When each model was launched

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

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

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

2 days ago

1.1yr newer
Kimi K2 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

Provider Availability

DeepSeek-V4.1-Flash-1017 is available from DeepSeek. Kimi K2 0905 is available from Novita.

DeepSeek-V4.1-Flash-1017

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

Kimi K2 0905

novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.50/1M
* 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 0905 side-by-side, then vote on the output you prefer.

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

FAQ

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

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

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.0 to 21.4. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and Kimi K2 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 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 0905 scores HumanEval: 94.5%, MMLU: 90.2%, MATH: 89.1%, MMLU-Pro: 82.5%, GPQA: 75.8%.

Is DeepSeek-V4.1-Flash-1017 cheaper than Kimi K2 0905?

DeepSeek-V4.1-Flash-1017 is 2.0x cheaper for input tokens. DeepSeek-V4.1-Flash-1017 costs $0.30/M input and $1.20/M output via deepseek. Kimi K2 0905 costs $0.60/M input and $2.50/M output via novita.

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

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and Kimi K2 0905 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 0905?

Key differences include LLM Stats Score (51.0 vs 21.4), context window (1.0M vs 262K), input pricing ($0.30 vs $0.60/M), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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

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

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