Model Comparison

DeepSeek VL2 vs GPT-3.5 Turbo

DeepSeek VL2 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek VL2 outperforms in 2 benchmarks (MathVista, MMMU), while GPT-3.5 Turbo is better at 0 benchmarks.

DeepSeek VL2 significantly outperforms across most benchmarks.

Sun Apr 19 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Sun Apr 19 2026 • llm-stats.com
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
OpenAI
GPT-3.5 Turbo
Input tokens$0.50
Output tokens$1.50
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

DeepSeek VL2 accepts 129,280 input tokens compared to GPT-3.5 Turbo's 16,385 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while GPT-3.5 Turbo is limited to 4,096 tokens.

DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
OpenAI
GPT-3.5 Turbo
Input16,385 tokens
Output4,096 tokens
Sun Apr 19 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

DeepSeek VL2 supports multimodal inputs, whereas GPT-3.5 Turbo does not.

DeepSeek VL2 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek VL2

Text
Images
Audio
Video

GPT-3.5 Turbo

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 is licensed under deepseek, while GPT-3.5 Turbo uses a proprietary license.

License differences may affect how you can use these models in commercial or open-source projects.

DeepSeek VL2

deepseek

Open weights

GPT-3.5 Turbo

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek VL2 was released on 2024-12-13, while GPT-3.5 Turbo was released on 2023-03-21.

DeepSeek VL2 is 21 months newer than GPT-3.5 Turbo.

DeepSeek VL2

Dec 13, 2024

1.3 years ago

1.7yr newer
GPT-3.5 Turbo

Mar 21, 2023

3.1 years ago

Knowledge Cutoff

When training data ends

GPT-3.5 Turbo has a documented knowledge cutoff of 2021-09-30, while DeepSeek VL2's cutoff date is not specified.

We can confirm GPT-3.5 Turbo's training data extends to 2021-09-30, but cannot make a direct comparison without DeepSeek VL2's cutoff date.

DeepSeek VL2

—

GPT-3.5 Turbo

Sep 2021

Provider Availability

DeepSeek VL2 is available from Replicate. GPT-3.5 Turbo is available from Azure, OpenAI.

DeepSeek VL2

replicate logo
Replicate

GPT-3.5 Turbo

azure logo
Azure
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M
openai logo
OpenAI
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (129,280 tokens)
Supports multimodal inputs
Has open weights
Higher MathVista score (62.8% vs 0.0%)
Higher MMMU score (51.1% vs 0.0%)
OpenAI

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek VL2
OpenAI
GPT-3.5 Turbo

FAQ

Common questions about DeepSeek VL2 vs GPT-3.5 Turbo

DeepSeek VL2 significantly outperforms across most benchmarks. DeepSeek VL2 is made by DeepSeek and GPT-3.5 Turbo is made by OpenAI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%. GPT-3.5 Turbo scores DROP: 70.2%, MMLU: 69.8%, HumanEval: 68.0%, MGSM: 56.3%, MATH: 43.1%.
DeepSeek VL2 supports 129K tokens and GPT-3.5 Turbo supports 16K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (129K vs 16K), multimodal support (yes vs no), licensing (deepseek vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek VL2 is developed by DeepSeek and GPT-3.5 Turbo is developed by OpenAI.