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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 26.0.

DeepSeek · LG AI Research · Updated for 2026

Which is better?

DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 26.0, ranking #12 overall.

In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash 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

  • overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
  • your work emphasizes reasoning — 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 Sep 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
DeepSeek
EXAONE 4.5 33B
LG AI Research
Core performance indexes
LLM Stats Score
51.8
#12
26.0
#151
Reasoning
48.9
#17
26.6
#144
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$0.22 / M
— / M
Output price
$0.66 / M
— / M
Context window
1,040,000
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

4 shared
Index
DeepSeek-V4.1-Flash
EXAONE 4.5 33B
Math
35.2#43
28.4#93
Vision
29.5#31
13.4#95
Tool use
35.1#2
10.6#122
Multimodal
34.3#13
16.3#80
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

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

1 shared

DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while EXAONE 4.5 33B is better at 0 benchmarks.

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

Fri Sep 11 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

730.2B diff

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

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

Context Window

Maximum input and output token capacity

Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4.1-Flash
Input1,040,000 tokens
Output393,216 tokens
LG AI Research
EXAONE 4.5 33B
Input- tokens
Output- tokens
Fri Sep 11 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both DeepSeek-V4.1-Flash 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

Text
Images
Audio
Video

EXAONE 4.5 33B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4.1-Flash 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

MIT

Open weights

EXAONE 4.5 33B

Proprietary

Closed source

Release Timeline

When each model was launched

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

DeepSeek-V4.1-Flash is 5 months newer than EXAONE 4.5 33B.

DeepSeek-V4.1-Flash

Sep 10, 2026

0 days ago

5mo newer
EXAONE 4.5 33B

Apr 9, 2026

5 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'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's cutoff date.

DeepSeek-V4.1-Flash

—

EXAONE 4.5 33B

Dec 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

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

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

FAQ

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

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

DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 26.0. DeepSeek-V4.1-Flash 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 compare to EXAONE 4.5 33B in benchmarks?

DeepSeek-V4.1-Flash scores CodeForces: 100.0%, GPQA: 90.9%, Terminal-Bench 2.1: 90.6%, BabyVision: 89.6%, CyberGym: 88.1%. 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 and EXAONE 4.5 33B?

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

Key differences include LLM Stats Score (51.8 vs 26.0), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

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

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

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