Model Comparison

DeepSeek-R1-0528 vs DeepSeek-V3.2-Speciale

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks. DeepSeek-V3.2-Speciale is 2.9x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-R1-0528 outperforms in 0 benchmarks, while DeepSeek-V3.2-Speciale is better at 5 benchmarks (AIME 2025, CodeForces, HMMT 2025, Humanity's Last Exam, SWE-Bench Verified).

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.

Wed May 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3.2-Speciale costs less

For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 1.8x more expensive than DeepSeek-V3.2-Speciale ($0.28/1M tokens).

For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 5.1x more expensive than DeepSeek-V3.2-Speciale ($0.42/1M tokens).

In conclusion, DeepSeek-R1-0528 is more expensive than DeepSeek-V3.2-Speciale.*

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

Lowest available price from all providers
Wed May 13 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
DeepSeek
DeepSeek-V3.2-Speciale
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

14.0B diff

DeepSeek-V3.2-Speciale has 14.0B more parameters than DeepSeek-R1-0528, making it 2.1% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
DeepSeek
DeepSeek-V3.2-Speciale
685.0Bparameters
671.0B
DeepSeek-R1-0528
685.0B
DeepSeek-V3.2-Speciale

Context Window

Maximum input and output token capacity

Both models have the same input context window of 131,072 tokens. Both models can generate responses up to 131,072 tokens.

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Wed May 13 2026 • llm-stats.com

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

DeepSeek-R1-0528

MIT

Open weights

DeepSeek-V3.2-Speciale

MIT

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while DeepSeek-V3.2-Speciale was released on 2025-12-01.

DeepSeek-V3.2-Speciale is 6 months newer than DeepSeek-R1-0528.

DeepSeek-R1-0528

May 28, 2025

11 months ago

DeepSeek-V3.2-Speciale

Dec 1, 2025

5 months ago

6mo newer

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-V3.2-Speciale is available from DeepSeek.

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-V3.2-Speciale

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek

No standout differentiators in the data we have for this pair.

DeepSeek
Less expensive input tokens
Less expensive output tokens
Higher AIME 2025 score (96.0% vs 87.5%)
Higher CodeForces score (90.0% vs 64.3%)
Higher HMMT 2025 score (99.2% vs 79.4%)
Higher Humanity's Last Exam score (30.6% vs 17.7%)
Higher SWE-Bench Verified score (73.1% vs 44.6%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
DeepSeek
DeepSeek-V3.2-Speciale

FAQ

Common questions about DeepSeek-R1-0528 vs DeepSeek-V3.2-Speciale.

Which is better, DeepSeek-R1-0528 or DeepSeek-V3.2-Speciale?

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and DeepSeek-V3.2-Speciale is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-R1-0528 compare to DeepSeek-V3.2-Speciale in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%.

Is DeepSeek-R1-0528 cheaper than DeepSeek-V3.2-Speciale?

DeepSeek-V3.2-Speciale is 1.8x cheaper for input tokens. DeepSeek-R1-0528 costs $0.50/M input and $2.15/M output via deepinfra. DeepSeek-V3.2-Speciale costs $0.28/M input and $0.42/M output via deepseek.

What are the context window sizes for DeepSeek-R1-0528 and DeepSeek-V3.2-Speciale?

DeepSeek-R1-0528 supports 131K tokens and DeepSeek-V3.2-Speciale supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between DeepSeek-R1-0528 and DeepSeek-V3.2-Speciale?

Key differences include input pricing ($0.50 vs $0.28/M). See the full comparison above for benchmark-by-benchmark results.