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DeepSeek-V4.1-Flash-1017 vs K-EXAONE-236B-A23B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 25.8. DeepSeek-V4.1-Flash-1017 is 1.3x cheaper per token.

DeepSeek · LG AI Research · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 25.8, ranking #15 overall.

On price, DeepSeek-V4.1-Flash-1017 is roughly 1.3x 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 agents — it leads those capability indexes
  • cost matters — it's about 1.3x 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 K-EXAONE-236B-A23B

  • you want predictable pricing at $0.60/M input and $1.00/M output

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
K-EXAONE-236B-A23B
LG AI Research
Core performance indexes
LLM Stats Score
51.2
#15
25.8
#176
Reasoning
48.8
#18
26.8
#164
Agents
38.7
#8
4.0
#181
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
$0.60 / M
Output price
$1.20 / M
$1.00 / M
Context window
1,048,576
32,768

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
DeepSeek-V4.1-Flash-1017
K-EXAONE-236B-A23B
Math
31.8#69
28.0#104
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 6 for K-EXAONE-236B-A23B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and K-EXAONE-236B-A23Bdon'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 K-EXAONE-236B-A23B ($0.60/1M tokens).

For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 1.2x more expensive than K-EXAONE-236B-A23B ($1.00/1M tokens).

In conclusion, K-EXAONE-236B-A23B 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
LG AI Research
K-EXAONE-236B-A23B
Input tokens$0.60
Output tokens$1.00
Best providerFriendliAI
Notice missing or incorrect data?

Model Size

Parameter count comparison

527.2B diff

DeepSeek-V4.1-Flash-1017 has 527.2B more parameters than K-EXAONE-236B-A23B, making it 223.4% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
LG AI Research
K-EXAONE-236B-A23B
236.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
236.0B
K-EXAONE-236B-A23B

Context Window

Maximum input and output token capacity

DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to K-EXAONE-236B-A23B's 32,768 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while K-EXAONE-236B-A23B is limited to 32,768 tokens.

DeepSeek
DeepSeek-V4.1-Flash-1017
Input1,048,576 tokens
Output393,216 tokens
LG AI Research
K-EXAONE-236B-A23B
Input32,768 tokens
Output32,768 tokens
Thu Oct 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas K-EXAONE-236B-A23B 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

K-EXAONE-236B-A23B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while K-EXAONE-236B-A23B 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

K-EXAONE-236B-A23B

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while K-EXAONE-236B-A23B was released on 2025-12-31.

DeepSeek-V4.1-Flash-1017 is 10 months newer than K-EXAONE-236B-A23B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

5 days ago

9mo newer
K-EXAONE-236B-A23B

Dec 31, 2025

9 months ago

Knowledge Cutoff

When training data ends

K-EXAONE-236B-A23B has a documented knowledge cutoff of 2025-10-01, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.

We can confirm K-EXAONE-236B-A23B's training data extends to 2025-10-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

K-EXAONE-236B-A23B

Oct 2025

Provider Availability

DeepSeek-V4.1-Flash-1017 is available from DeepSeek. K-EXAONE-236B-A23B is available from FriendliAI.

DeepSeek-V4.1-Flash-1017

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

K-EXAONE-236B-A23B

friendli logo
FriendliAI
Input Price:Input: $0.60/1MOutput Price:Output: $1.00/1M
* 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 K-EXAONE-236B-A23B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
K-EXAONE-236B-A23B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs K-EXAONE-236B-A23B.

Which is better, DeepSeek-V4.1-Flash-1017 or K-EXAONE-236B-A23B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 25.8. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and K-EXAONE-236B-A23B is made by LG AI Research. 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 K-EXAONE-236B-A23B 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%. K-EXAONE-236B-A23B scores AIME 2025: 92.8%, MMMLU: 85.7%, MMLU-Pro: 83.8%, LiveCodeBench v6: 80.7%, t2-bench: 73.2%.

Is DeepSeek-V4.1-Flash-1017 cheaper than K-EXAONE-236B-A23B?

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. K-EXAONE-236B-A23B costs $0.60/M input and $1.00/M output via friendli.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and K-EXAONE-236B-A23B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and K-EXAONE-236B-A23B supports 33K 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 K-EXAONE-236B-A23B?

Key differences include LLM Stats Score (51.2 vs 25.8), context window (1.0M vs 33K), 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 K-EXAONE-236B-A23B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and K-EXAONE-236B-A23B is developed by LG AI Research.

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