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DeepSeek-V4-Pro-0813 vs EXAONE 4.5 33B

Comparing DeepSeek-V4-Pro-0813 and EXAONE 4.5 33B across benchmarks, pricing, and capabilities.

DeepSeek · LG AI Research · Updated for 2026

Which is better?

DeepSeek-V4-Pro-0813 and EXAONE 4.5 33B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose DeepSeek-V4-Pro-0813

  • you want the most recent training data — it shipped Aug 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.

Benchmark wins
Input price
$0.43 / M
— / M
Output price
$0.87 / M
— / M
Context window
1,048,576
Released
Aug 2026
Apr 2026
License
MIT
Proprietary

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V4-Pro-0813 and EXAONE 4.5 33Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

1567.0B diff

DeepSeek-V4-Pro-0813 has 1567.0B more parameters than EXAONE 4.5 33B, making it 4748.5% larger.

DeepSeek
DeepSeek-V4-Pro-0813
1.6Tparameters
LG AI Research
EXAONE 4.5 33B
33.0Bparameters
1600.0B
DeepSeek-V4-Pro-0813
33.0B
EXAONE 4.5 33B

Context Window

Maximum input and output token capacity

Only DeepSeek-V4-Pro-0813 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Pro-0813 specifies output context (393,216 tokens).

DeepSeek
DeepSeek-V4-Pro-0813
Input1,048,576 tokens
Output393,216 tokens
LG AI Research
EXAONE 4.5 33B
Input- tokens
Output- tokens
Thu Aug 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

EXAONE 4.5 33B supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.

EXAONE 4.5 33B can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V4-Pro-0813

Text
Images
Audio
Video

EXAONE 4.5 33B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V4-Pro-0813 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-Pro-0813

MIT

Open weights

EXAONE 4.5 33B

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-V4-Pro-0813 was released on 2026-08-13, while EXAONE 4.5 33B was released on 2026-04-09.

DeepSeek-V4-Pro-0813 is 4 months newer than EXAONE 4.5 33B.

DeepSeek-V4-Pro-0813

Aug 13, 2026

1 weeks ago

4mo newer
EXAONE 4.5 33B

Apr 9, 2026

4 months ago

Knowledge Cutoff

When training data ends

EXAONE 4.5 33B has a documented knowledge cutoff of 2024-12-01, while DeepSeek-V4-Pro-0813'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-Pro-0813's cutoff date.

DeepSeek-V4-Pro-0813

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-Pro-0813 and EXAONE 4.5 33B side-by-side, then vote on the output you prefer.

DeepSeek-V4-Pro-0813
✓ Preferred
EXAONE 4.5 33B
Open in Playground

FAQ

Common questions about DeepSeek-V4-Pro-0813 vs EXAONE 4.5 33B.

Which is better, DeepSeek-V4-Pro-0813 or EXAONE 4.5 33B?

DeepSeek-V4-Pro-0813 (DeepSeek) and EXAONE 4.5 33B (LG AI Research) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V4-Pro-0813 compare to EXAONE 4.5 33B in benchmarks?

DeepSeek-V4-Pro-0813 scores Terminal-Bench 2.1: 87.9%, CyberGym: 83.3%, Toolathlon: 74.1%, DSBench-FullStack: 71.1%, DSBench-Hard: 67.2%. 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-Pro-0813 and EXAONE 4.5 33B?

DeepSeek-V4-Pro-0813 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-Pro-0813 and EXAONE 4.5 33B?

Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V4-Pro-0813 and EXAONE 4.5 33B?

DeepSeek-V4-Pro-0813 is developed by DeepSeek and EXAONE 4.5 33B is developed by LG AI Research.