An exceptionally intelligent and verbose open-weight model with a massive 128k context window, positioned as a high-performance but costly option for complex tasks.
DeepSeek R1 0528 (May '25) emerges as a formidable player in the landscape of open-weight large language models, distinguishing itself through a combination of top-tier intelligence and a capacious 128k token context window. Developed by DeepSeek, this model is engineered for tasks demanding deep reasoning, extensive context comprehension, and detailed, nuanced output. Its performance on the Artificial Analysis Intelligence Index is a testament to its capabilities, scoring an impressive 52, which places it firmly in the upper echelon of models and significantly above the class average of 42. This high score suggests proficiency in complex problem-solving, creative generation, and multi-step reasoning.
However, this intellectual prowess comes at a significant cost. The model's pricing structure is notably premium, with an input cost of $1.08 and an output cost of $3.50 per million tokens, both substantially higher than the average for comparable models ($0.57 for input, $2.10 for output). This pricing is further compounded by the model's extreme verbosity. During our standardized intelligence evaluation, DeepSeek R1 0528 generated a staggering 99 million tokens, more than four times the average of 22 million. This tendency to produce lengthy, detailed responses can lead to rapidly escalating costs, particularly for output-heavy applications. The total cost to run the model through our intelligence benchmark was $381.87, a figure that underscores the financial commitment required to leverage its full potential.
The model's open license offers developers significant flexibility, a key advantage over proprietary counterparts. This, combined with its massive 128k context window, opens up a wide range of advanced use cases. Applications involving long-document analysis, extensive code repository comprehension, or maintaining long-term memory in conversational agents are where DeepSeek R1 can truly shine. Developers can feed it entire legal contracts, technical manuals, or lengthy conversation histories in a single prompt, enabling a level of contextual understanding that is difficult to achieve with smaller-context models. The challenge for developers, therefore, is to harness this power while carefully managing the associated costs through strategic implementation and provider selection.
52 (12 / 51)
N/A tokens/s
$1.08 / 1M tokens
$3.50 / 1M tokens
99M tokens
N/A seconds
| Spec | Details |
|---|---|
| Owner | DeepSeek |
| License | Open |
| Release Version | 0528 (May '25) |
| Model Type | Text Generation |
| Input Modality | Text |
| Output Modality | Text |
| Context Window | 128,000 tokens |
| Intelligence Index Score | 52 |
| Intelligence Rank | #12 out of 51 |
| Base Input Price | $1.08 / 1M tokens |
| Base Output Price | $3.50 / 1M tokens |
| Intelligence Evaluation Cost | $381.87 |
Choosing the right API provider for DeepSeek R1 0528 is a crucial decision that directly impacts both performance and cost. The best choice depends entirely on your application's primary requirement: are you optimizing for the lowest possible price, the fastest response speed, or the quickest time to first token? Our benchmarks reveal clear leaders in each category.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | Deepinfra | With a blended price of just $0.91 per million tokens and the lowest input price ($0.50), Deepinfra is the undisputed budget champion for this model. | Performance is moderate; it is not the fastest provider for either latency or output speed. |
| Fastest Output | Together.ai | Delivering a blistering 299 tokens per second, Together.ai is the top choice for applications where generation speed is paramount, such as streaming long-form content. | While competitively priced ($0.96 blended), it's marginally more expensive than the absolute cheapest option. |
| Lowest Latency | Google Vertex | Google Vertex boasts the fastest time to first token (TTFT) at just 0.24 seconds, making it ideal for interactive, real-time applications like chatbots. | This speed comes at a steep cost. At $2.36 blended, it is by far the most expensive provider benchmarked. |
| Balanced Performance | Together.ai (Throughput) | This provider offers an exceptional all-around package: top-tier speed (299 t/s), low latency (0.41s), and a highly competitive blended price of $0.96. | The 'Throughput' pricing tier may have specific usage patterns or commitments associated with it. |
| Enterprise Choice | Microsoft Azure | Azure provides the reliability, security, and support expected from an enterprise-grade platform, making it a safe choice for large organizations. | Performance is lackluster compared to specialized providers, with slower speed (101 t/s) and higher costs than the budget options. |
Note: Provider benchmarks reflect a snapshot in time from May 2025. Performance and pricing are subject to change. Blended price assumes a 1:3 input-to-output token ratio. Your actual costs will vary based on usage patterns.
To understand the real-world cost implications of using DeepSeek R1 0528, let's examine a few hypothetical scenarios. These examples illustrate how the model's unique characteristics—its large context, high intelligence, and expensive, verbose output—affect the final cost. For these calculations, we'll use the pricing from the most cost-effective provider, Deepinfra ($0.50/1M input, $2.15/1M output).
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Long-Document Summarization | 100k tokens | 2k tokens | Analyzing a large legal contract or research paper to extract key points. Leverages the 128k context window. | ~$0.054 |
| Retrieval-Augmented Generation (RAG) | 120k tokens | 1k tokens | Answering a complex query using an entire technical manual as context. A classic large-context use case. | ~$0.062 |
| Creative Content Generation | 500 tokens | 8k tokens | Writing a detailed article or generating a complex code function. This is an output-heavy task. | ~$0.017 |
| Complex Chatbot Session | 15k tokens | 20k tokens | A long, multi-turn conversation requiring memory of the entire discussion. Balanced input/output. | ~$0.051 |
| Single Complex Query | 2k tokens | 500 tokens | A simple question-and-answer task that doesn't leverage the large context window. | ~$0.002 |
The takeaway is clear: DeepSeek R1 0528 provides the most value on input-heavy tasks that fully utilize its 128k context window. Output-heavy generative tasks, while a strength, can become disproportionately expensive if the model's verbosity isn't carefully managed.
Given its premium pricing and high verbosity, actively managing the cost of DeepSeek R1 0528 is not just recommended—it's essential. Failing to implement cost-control strategies can lead to budget overruns, especially at scale. Here are several key tactics to keep your expenses in check while still benefiting from the model's powerful capabilities.
Your choice of API provider is the single biggest lever you can pull to control costs. The price difference is substantial.
DeepSeek R1's natural tendency is to be extremely verbose. This is a major cost driver. You must guide it to be more concise.
max_tokens: Always set a reasonable max_tokens limit in your API calls to prevent runaway generation and cap the cost of any single request.The model's large context window is a key feature; using it efficiently can reduce costs by minimizing the number of API calls.
Many applications receive repetitive user queries. Caching the model's responses to common prompts is a highly effective cost-saving measure.
DeepSeek R1 0528 is an open-weight large language model from DeepSeek, released in May 2025. It is characterized by its high intelligence score (52 on the Artificial Analysis Intelligence Index), a very large 128,000-token context window, and a tendency for highly detailed, verbose outputs. It is designed for complex text-based tasks but comes with a premium price point.
DeepSeek R1 0528 positions itself at the high-performance end of the spectrum. It generally outperforms other open-weight models of a similar size in terms of raw intelligence and reasoning capabilities. However, this performance comes at a cost, as it is significantly more expensive to run than many popular open models, both in terms of its base token price and its high output verbosity.
A 128k context window means the model can consider up to 128,000 tokens (roughly 95,000 words) of text in a single prompt. This is a massive advantage for applications that need to process and reason over large amounts of information, such as:
The high cost is due to a combination of two main factors:
It's a trade-off. On one hand, its high intelligence and large context window can enable incredibly smart and context-aware conversational experiences. On the other hand, the cost can be prohibitive for a high-traffic chatbot. Furthermore, latency (time to first token) is critical for a good user experience. While a provider like Google Vertex offers excellent latency (0.24s), it is very expensive. Slower, cheaper providers might feel sluggish to the end-user. It would be best suited for specialized, low-volume expert chatbots rather than a general-purpose, high-volume one.
An 'Open License' (in this context, often referring to licenses like Apache 2.0 or MIT, though the specific license for DeepSeek R1 should be verified) generally means that the model weights are publicly available. This allows developers to download the model, modify it, and run it on their own infrastructure (self-hosting) or use it through various API providers. This contrasts with proprietary or 'closed' models (like OpenAI's GPT-4) where the model weights are not public and access is restricted to the owner's API.