Model Comparison

DeepSeek R1 Distill Qwen 14B vs Gemini 2.5 Flash

Gemini 2.5 Flash significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek R1 Distill Qwen 14B outperforms in 0 benchmarks, while Gemini 2.5 Flash is better at 2 benchmarks (AIME 2024, GPQA).

Gemini 2.5 Flash significantly outperforms across most benchmarks.

Sat Mar 28 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
Sat Mar 28 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 14B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Google
Gemini 2.5 Flash
Input tokens$0.30
Output tokens$2.50
Best providerGoogle
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Only Gemini 2.5 Flash specifies input context (1,048,576 tokens). Only Gemini 2.5 Flash specifies output context (65,536 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 14B
Input- tokens
Output- tokens
Google
Gemini 2.5 Flash
Input1,048,576 tokens
Output65,536 tokens
Sat Mar 28 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.5 Flash supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 14B does not.

Gemini 2.5 Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Distill Qwen 14B

Text
Images
Audio
Video

Gemini 2.5 Flash

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 14B is licensed under MIT, while Gemini 2.5 Flash uses a proprietary license.

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

DeepSeek R1 Distill Qwen 14B

MIT

Open weights

Gemini 2.5 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 14B was released on 2025-01-20, while Gemini 2.5 Flash was released on 2025-05-20.

Gemini 2.5 Flash is 4 months newer than DeepSeek R1 Distill Qwen 14B.

DeepSeek R1 Distill Qwen 14B

Jan 20, 2025

1.2 years ago

Gemini 2.5 Flash

May 20, 2025

10 months ago

4mo newer

Knowledge Cutoff

When training data ends

Gemini 2.5 Flash has a documented knowledge cutoff of 2025-01-31, while DeepSeek R1 Distill Qwen 14B's cutoff date is not specified.

We can confirm Gemini 2.5 Flash's training data extends to 2025-01-31, but cannot make a direct comparison without DeepSeek R1 Distill Qwen 14B's cutoff date.

DeepSeek R1 Distill Qwen 14B

—

Gemini 2.5 Flash

Jan 2025

Outputs Comparison

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Key Takeaways

DeepSeek
Has open weights
Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Higher AIME 2024 score (88.0% vs 80.0%)
Higher GPQA score (82.8% vs 59.1%)
DeepSeekDeepSeek R1 Distill Qwen 14B
GoogleGemini 2.5 Flash
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Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 14B
Google
Gemini 2.5 Flash

FAQ

Common questions about DeepSeek R1 Distill Qwen 14B vs Gemini 2.5 Flash

Gemini 2.5 Flash significantly outperforms across most benchmarks. DeepSeek R1 Distill Qwen 14B is made by DeepSeek and Gemini 2.5 Flash is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek R1 Distill Qwen 14B scores MATH-500: 93.9%, AIME 2024: 80.0%, GPQA: 59.1%, LiveCodeBench: 53.1%. Gemini 2.5 Flash scores Global-MMLU-Lite: 88.4%, AIME 2024: 88.0%, FACTS Grounding: 85.3%, GPQA: 82.8%, MMMU: 79.7%.
DeepSeek R1 Distill Qwen 14B supports an unknown number of tokens and Gemini 2.5 Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Distill Qwen 14B is developed by DeepSeek and Gemini 2.5 Flash is developed by Google.