Model Comparison

DeepSeek-R1-0528 vs DeepSeek-V2.5

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-V2.5 is 5.2x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-R1-0528 outperforms in 1 benchmarks (SWE-Bench Verified), while DeepSeek-V2.5 is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Wed Apr 22 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V2.5 costs less

For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 3.6x more expensive than DeepSeek-V2.5 ($0.14/1M tokens).

For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 7.7x more expensive than DeepSeek-V2.5 ($0.28/1M tokens).

In conclusion, DeepSeek-R1-0528 is more expensive than DeepSeek-V2.5.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Apr 22 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
DeepSeek
DeepSeek-V2.5
Input tokens$0.14
Output tokens$0.28
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

435.0B diff

DeepSeek-R1-0528 has 435.0B more parameters than DeepSeek-V2.5, making it 184.3% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
DeepSeek
DeepSeek-V2.5
236.0Bparameters
671.0B
DeepSeek-R1-0528
236.0B
DeepSeek-V2.5

Context Window

Maximum input and output token capacity

DeepSeek-R1-0528 accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. DeepSeek-R1-0528 can generate longer responses up to 131,072 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
DeepSeek
DeepSeek-V2.5
Input8,192 tokens
Output8,192 tokens
Wed Apr 22 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while DeepSeek-V2.5 uses deepseek.

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

DeepSeek-R1-0528

MIT

Open weights

DeepSeek-V2.5

deepseek

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while DeepSeek-V2.5 was released on 2024-05-08.

DeepSeek-R1-0528 is 13 months newer than DeepSeek-V2.5.

DeepSeek-R1-0528

May 28, 2025

10 months ago

1.1yr newer
DeepSeek-V2.5

May 8, 2024

2.0 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic.

DeepSeek-R1-0528

deepinfra logo
Deepinfra
Input Price:Input: $0.50/1MOutput Price:Output: $2.15/1M
deepseek logo
DeepSeek
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
novita logo
Novita
Input Price:Input: $0.70/1MOutput Price:Output: $2.50/1M

DeepSeek-V2.5

deepseek logo
DeepSeek
Input Price:Input: $0.14/1MOutput Price:Output: $0.28/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.70/1MOutput Price:Output: $1.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $2.00/1MOutput Price:Output: $2.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher SWE-Bench Verified score (44.6% vs 16.8%)
DeepSeek
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
DeepSeek
DeepSeek-V2.5

FAQ

Common questions about DeepSeek-R1-0528 vs DeepSeek-V2.5

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and DeepSeek-V2.5 is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. DeepSeek-V2.5 scores GSM8k: 95.1%, MT-Bench: 90.2%, HumanEval: 89.0%, BBH: 84.3%, AlignBench: 80.4%.
DeepSeek-V2.5 is 3.6x cheaper for input tokens. DeepSeek-R1-0528 costs $0.50/M input and $2.15/M output via deepinfra. DeepSeek-V2.5 costs $0.14/M input and $0.28/M output via deepseek.
DeepSeek-R1-0528 supports 131K tokens and DeepSeek-V2.5 supports 8K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 8K), input pricing ($0.50 vs $0.14/M), licensing (MIT vs deepseek). See the full comparison above for benchmark-by-benchmark results.