A highly intelligent and cost-effective open-weight model with a large context window, best suited for tasks where raw speed is not the primary concern.
DeepSeek V3.2 (Non-reasoning) emerges as a formidable contender in the open-weight model landscape, carving out a niche for itself by delivering exceptional intelligence at a remarkably low price point. Developed by DeepSeek AI, this model is specifically tailored for tasks that do not require complex, multi-step reasoning. Instead, it excels at knowledge retrieval, summarization, classification, and creative generation. Its standout feature is its performance on the Artificial Analysis Intelligence Index, where it achieves the top rank in its class, demonstrating a deep understanding and fluency with language and concepts.
This impressive cognitive capability is paired with an aggressive pricing strategy. With input and output token costs sitting well below the market average, DeepSeek V3.2 presents a compelling economic argument for developers and businesses. The cost to evaluate the model on our comprehensive intelligence benchmark was just $24.66, a fraction of what it costs for many similarly-sized proprietary models. This makes it an ideal choice for processing large volumes of text, powering RAG (Retrieval-Augmented Generation) systems, or handling any workload where budget is a key constraint. The generous 128,000-token context window further enhances its value, allowing it to analyze and synthesize information from extensive documents in a single pass.
However, the model's strengths in intelligence and cost are balanced by a significant trade-off: speed. With an average output of around 31 tokens per second on its native platform, it is notably slower than many of its peers. This can be a limiting factor for applications requiring real-time interaction, such as dynamic chatbots or live co-writing assistants. The time to first token (TTFT), a measure of latency, is also on the higher side. This means users will experience a more noticeable pause before the model begins generating its response. Prospective users must weigh these factors carefully, deciding whether the elite intelligence and low operational cost justify the sacrifice in performance and responsiveness for their specific use case.
The availability of DeepSeek V3.2 across a diverse range of API providers creates a healthy, competitive ecosystem. As our analysis shows, performance and pricing can vary dramatically from one provider to another. Some providers have optimized their infrastructure to deliver significantly higher throughput and lower latency, mitigating the model's inherent slowness, albeit sometimes at a slightly higher price. This allows users to select a provider that aligns with their specific priorities, whether that's maximizing speed, minimizing cost, or achieving a balanced blend of both.
52 (#1 / 30)
30.8 tokens/s
$0.28 / 1M tokens
$0.42 / 1M tokens
14M tokens
1.10 s
| Spec | Details |
|---|---|
| Model Owner | DeepSeek AI |
| License | DeepSeek Model License |
| Context Window | 128,000 tokens |
| Input Modalities | Text |
| Output Modalities | Text |
| Model Family | DeepSeek |
| Variant Focus | Non-reasoning, General Purpose |
| Architecture | Proprietary Transformer-based |
| Quantization Support | Yes (e.g., FP8 available via select providers) |
| Fine-Tuning | Supported by some API providers |
| Launch Date | 2024 |
Choosing the right API provider for DeepSeek V3.2 is crucial, as it involves a direct trade-off between speed and cost. While some providers offer blistering performance that mitigates the model's native slowness, others focus on delivering the absolute lowest price. Your choice will depend entirely on your application's priorities.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Highest Throughput | Fireworks | Delivers an exceptional 186 tokens/s, more than 6x the baseline speed, making interactive use feasible. | Not the absolute cheapest option and latency is slightly higher than Deepinfra. |
| Lowest Latency | Fireworks | At 0.23s TTFT, it provides the most responsive experience, minimizing the initial wait time for a response. | Slightly more expensive than the most budget-friendly providers. |
| Lowest Cost | Deepinfra | Offers one of the best blended prices at $0.30/M tokens, making it the go-to for budget-critical, asynchronous workloads. | Significantly slower output speed (around 60 t/s) compared to the fastest providers. |
| Balanced Choice | Baseten | Provides an excellent compromise with the second-fastest speed (137 t/s) and reasonable latency (0.74s) at a competitive price. | More expensive than pure cost-leaders like Deepinfra or SiliconFlow. |
| Quantized Value | SiliconFlow (FP8) | Offers a very low blended price ($0.31) using FP8 quantization, providing great value for cost-sensitive projects. | Speed is modest (57 t/s), and FP8 quantization may have minor impacts on output quality for some tasks. |
| Official Source | DeepSeek | Using the model directly from its creators ensures you are getting the canonical version. | Performance is poor, with high latency (1.10s) and slow output speed compared to optimized third-party providers. |
Provider performance benchmarks are snapshots in time and can change. Prices are based on $/1M input and output tokens. Blended price assumes a 1:2 input-to-output token ratio. Always check provider websites for the latest information.
To understand the real-world cost implications of DeepSeek V3.2, let's estimate the price for a few common tasks. These calculations use a cost-optimized provider's pricing (e.g., $0.27/M input, $0.40/M output) to illustrate the model's affordability. Note that these costs do not account for the time taken to generate the response.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Article Summarization | 15,000 tokens (a long report) | 750 tokens (a concise summary) | RAG, content analysis, research | ~$0.0044 |
| Chatbot Session | 3,000 tokens (conversation history) | 150 tokens (next response) | Customer support, conversational AI | ~$0.00087 |
| Blog Post Generation | 100 tokens (a detailed prompt) | 2,000 tokens (a draft article) | Content creation, marketing copy | ~$0.00083 |
| Large Document Q&A | 100,000 tokens (a legal contract) | 500 tokens (an answer to a specific query) | Legal tech, compliance, knowledge extraction | ~$0.0272 |
| Email Classification (Batch) | 500,000 tokens (1,000 emails) | 10,000 tokens (1,000 category labels) | Data processing, automation | ~$0.139 |
These examples highlight the model's extreme cost-effectiveness. Even processing a 100,000-token document costs less than three cents, and generating an entire blog post costs a fraction of a cent. For asynchronous or batch-processing workloads where speed is secondary, DeepSeek V3.2 offers unparalleled economic value.
While DeepSeek V3.2 is already inexpensive, you can further optimize its performance and cost with a few key strategies. The goal is to leverage its strengths—intelligence and a large context window—while mitigating its primary weakness: speed.
Your choice of API provider is the single most important decision. It dictates the balance of speed vs. cost.
Since the model is slightly verbose, you can guide it to be more concise. This saves on output tokens and reduces generation time.
Don't be afraid to fill the context window. It's more efficient than making multiple smaller calls.
If you must use a slower provider for a user-facing application, use UI/UX tricks to manage the user's perception of speed.
The "Non-reasoning" variant of DeepSeek V3.2 is optimized for tasks that rely on stored knowledge, pattern recognition, and language fluency. This includes summarization, translation, question-answering, and creative writing. It is not designed for tasks requiring multi-step logical deduction, mathematical calculations, or complex planning, for which a "Reasoning" or "Code" variant of a model would be better suited.
DeepSeek V3.2 (Non-reasoning) competes very favorably on intelligence and knowledge-based tasks, outperforming many models in its size class. It is significantly cheaper than closed-source frontier models like GPT-4o. However, it is much slower and lacks the advanced reasoning, multimodality (image/audio input), and tool-use capabilities of a model like GPT-4o.
It depends on the provider. Using the base model via DeepSeek's own API would likely result in a poor user experience due to high latency and slow generation. However, when served by a highly optimized provider like Fireworks or Baseten, the speed becomes acceptable for many chat applications, especially if responses are streamed to the user.
The DeepSeek Model License is a permissive, open license that allows for commercial use and distribution. However, like many open model licenses, it includes use-case restrictions. You should always review the full license text to ensure your application complies with its terms before deploying it in a commercial product.
Serving large language models efficiently is a complex engineering challenge. Differences in performance arise from several factors:
FP8 (8-bit floating point) is a form of quantization where the model's weights are stored with less precision than the standard 16-bit (FP16/BF16). This reduces the model's memory footprint and can significantly speed up inference. For example, SiliconFlow offers an FP8 version of DeepSeek V3.2. For most tasks, the impact on output quality is negligible, but it provides a substantial boost in performance and cost-efficiency. It's an excellent option for maximizing value, but it's always wise to test it for your specific use case to ensure quality remains high.