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DeepSeek-V4.1-Flash-1017 vs EXAONE 4.5 33B

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 25.5.

DeepSeek · LG AI Research · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 25.5, 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 51.2 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 EXAONE 4.5 33B

  • you are already invested in the LG AI Research ecosystem

At a glance

The differences that matter most.

DeepSeek-V4.1-Flash-1017
DeepSeek
EXAONE 4.5 33B
LG AI Research
Core performance indexes
LLM Stats Score
51.2
#15
25.5
#177
Reasoning
48.8
#18
26.1
#172
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

4 shared
Index
DeepSeek-V4.1-Flash-1017
EXAONE 4.5 33B
Math
31.8#69
28.3#101
Vision
32.3#25
13.3#114
Tool use
31.3#6
9.8#147
Multimodal
35.0#14
16.1#100
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for DeepSeek-V4.1-Flash-1017 · 15 for EXAONE 4.5 33B

No common benchmarks found

DeepSeek-V4.1-Flash-1017 and EXAONE 4.5 33Bdon'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

Model Size

Parameter count comparison

730.2B diff

DeepSeek-V4.1-Flash-1017 has 730.2B more parameters than EXAONE 4.5 33B, making it 2212.7% larger.

DeepSeek
DeepSeek-V4.1-Flash-1017
763.2Bparameters
LG AI Research
EXAONE 4.5 33B
33.0Bparameters
763.2B
DeepSeek-V4.1-Flash-1017
33.0B
EXAONE 4.5 33B

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
LG AI Research
EXAONE 4.5 33B
Input- tokens
Output- tokens
Thu Oct 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash-1017 and EXAONE 4.5 33B 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

EXAONE 4.5 33B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash-1017 is licensed under MIT, while EXAONE 4.5 33B 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

EXAONE 4.5 33B

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4.1-Flash-1017 was released on 2026-10-17, while EXAONE 4.5 33B was released on 2026-04-09.

DeepSeek-V4.1-Flash-1017 is 6 months newer than EXAONE 4.5 33B.

DeepSeek-V4.1-Flash-1017

Oct 17, 2026

3 days ago

6mo newer
EXAONE 4.5 33B

Apr 9, 2026

6 months ago

Knowledge Cutoff

When training data ends

EXAONE 4.5 33B has a documented knowledge cutoff of 2024-12-01, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.

We can confirm EXAONE 4.5 33B's training data extends to 2024-12-01, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.

DeepSeek-V4.1-Flash-1017

—

EXAONE 4.5 33B

Dec 2024

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against DeepSeek-V4.1-Flash-1017 and EXAONE 4.5 33B side-by-side, then vote on the output you prefer.

DeepSeek-V4.1-Flash-1017
✓ Preferred
EXAONE 4.5 33B
Open in Playground

FAQ

Common questions about DeepSeek-V4.1-Flash-1017 vs EXAONE 4.5 33B.

Which is better, DeepSeek-V4.1-Flash-1017 or EXAONE 4.5 33B?

DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 25.5. DeepSeek-V4.1-Flash-1017 is made by DeepSeek and EXAONE 4.5 33B 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 EXAONE 4.5 33B 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%. EXAONE 4.5 33B scores AIME 2025: 92.9%, AIME 2026: 92.6%, IFEval: 89.6%, AI2D: 89.0%, MathVista-Mini: 85.0%.

What are the context window sizes for DeepSeek-V4.1-Flash-1017 and EXAONE 4.5 33B?

DeepSeek-V4.1-Flash-1017 supports 1.0M tokens and EXAONE 4.5 33B 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 EXAONE 4.5 33B?

Key differences include LLM Stats Score (51.2 vs 25.5), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4.1-Flash-1017 and EXAONE 4.5 33B?

DeepSeek-V4.1-Flash-1017 is developed by DeepSeek and EXAONE 4.5 33B is developed by LG AI Research.

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