DeepSeek v4 Pro Explained: Benchmarks, Pricing, and Fable 5 Comparison
See what DeepSeek v4 Pro offers, where its benchmark results stand out, how much the API costs, and when it may be a better fit than Claude Fable 5.
DeepSeek v4 Pro has attracted considerable attention—and for good reason. It combines a one-million-token context window with improved reasoning, coding, and AI agent capabilities, while maintaining the open-weight approach and competitive API pricing for which DeepSeek is known.
For everyday users, this means the model can work with longer documents, larger codebases, and more complex instructions. For developers, the more interesting story is its combination of open weights, strong benchmark results, agent-oriented features, and relatively low operating costs.
This article provides an accessible introduction to DeepSeek v4 Pro, covering its main features, benchmark performance, pricing, and how it compares with Anthropic’s Claude Fable 5.
What Is DeepSeek v4 Pro?
DeepSeek v4 Pro is the high-performance model in the DeepSeek V4 family. While DeepSeek V4 Flash focuses on speed and cost efficiency, the Pro version is designed for more demanding tasks, including advanced reasoning, software development, professional knowledge work, and multi-step agent workflows.
According to DeepSeek, the model has:
- 1.6 trillion total parameters;
- Approximately 49 billion active parameters per inference step;
- A one-million-token context window;
- Open weights released under the MIT License.
DeepSeek v4 Pro uses a Mixture-of-Experts, or MoE, architecture. A simple way to understand MoE is to imagine a large team of specialists. The entire model contains a huge number of parameters, but only the experts most relevant to a particular request are activated.
This approach allows DeepSeek to increase the model’s overall capacity without using all 1.6 trillion parameters every time it generates a token.
DeepSeek has also introduced a compressed, sparse hybrid attention architecture. According to its technical report, when working with a one-million-token context, DeepSeek v4 Pro requires only about 27% of the single-token inference FLOPs and 10% of the KV cache used by DeepSeek V3.2.
In practical terms, the model is not only able to accept much longer inputs; it is also designed to process them more efficiently. More technical details are available in the official DeepSeek V4 model card.
Why Does a One-Million-Token Context Window Matter?
A one-million-token context window is more than a larger chat history. It allows the model to receive and analyze a substantial amount of information in a single request.
Potential applications include:
- Analyzing a large software repository;
- Comparing hundreds of pages of research;
- Reviewing multiple contracts or financial reports;
- Maintaining context during long-running agent tasks;
- Answering questions across a large internal knowledge base;
- Planning changes that affect many interconnected files.
For non-technical users, the main benefit is straightforward: fewer documents need to be divided into smaller pieces before they can be analyzed.
For developers, a larger context window can reduce the need for complicated retrieval pipelines. However, it does not eliminate them completely. A model may still overlook details in an extremely long prompt, so retrieval, structured memory, and output verification remain important for critical applications.
Three Reasoning Modes
DeepSeek v4 Pro supports three reasoning modes designed for different levels of task complexity.
- Non-think mode prioritizes speed and is suitable for routine questions, summaries, and straightforward decisions.
- Think High uses more reasoning computation for planning, coding, mathematics, and complex problem-solving.
- Think Max gives the model its highest reasoning budget and is intended for exceptionally difficult coding, mathematical, and agentic tasks.
This flexibility allows developers to match the model’s reasoning cost to the difficulty of each request. There is little value in applying maximum reasoning effort to a simple classification task, while complex software engineering work may benefit significantly from it.
DeepSeek v4 Pro also supports tool calling, structured JSON output, code completion, the OpenAI Chat Completions format, the Responses API, and an Anthropic-compatible API. These integrations make it easier to use the model with existing AI applications and coding agents. See the official DeepSeek V4 release announcement for additional details.
DeepSeek v4 Pro Benchmark Results
DeepSeek’s published Think Max results show particularly strong performance in coding, mathematics, scientific reasoning, and agent-oriented tasks.
| Benchmark | DeepSeek v4 Pro Max | What It Measures |
|---|---|---|
| LiveCodeBench | 93.5% | Code generation using recent programming problems |
| Codeforces | 3,206 rating | Competitive programming |
| GPQA Diamond | 90.1% | Advanced scientific reasoning |
| SWE-bench Verified | 80.6% | Solving real software repository issues |
| Terminal-Bench 2.0 | 67.9% | Terminal use and agent capabilities |
| BrowseComp | 83.4% | Web research and information synthesis |
| MRCR 1M | 83.5 | Information retrieval across a one-million-token context |
The coding results are among the most notable. A strong score on SWE-bench Verified suggests that DeepSeek v4 Pro can do more than generate isolated code snippets: it can inspect a repository, understand an issue, and make changes intended to pass real tests.
Terminal-Bench and BrowseComp are also important because they evaluate behaviors associated with AI agents. These tasks require the model to use tools, navigate an environment, collect information, and complete multiple connected steps.
The complete results and evaluation settings can be found in the official benchmark tables.
Benchmarks should still be interpreted carefully. Results can change depending on the prompt, reasoning budget, tool environment, time limit, and number of attempts. A high benchmark score does not guarantee that a model will perform equally well on every real-world workload.
DeepSeek v4 Pro Pricing
As of August 14, 2026, the listed DeepSeek v4 Pro API prices are:
| Usage type | Price per million tokens |
|---|---|
| Cached input | $0.003625 |
| Uncached input | $0.435 |
| Output | $0.87 |
DeepSeek has announced that peak and off-peak pricing will take effect at 16:00 UTC on August 16, 2026.
| Usage type | Off-peak price | Peak price |
|---|---|---|
| Cached input | $0.022 | $0.044 |
| Uncached input | $0.66 | $1.32 |
| Output | $1.98 | $3.96 |
Off-peak rates are approximately half the peak rates. This gives developers an additional way to reduce costs for batch processing, offline analysis, and other workloads that do not require an immediate response. Because AI pricing changes frequently, readers should confirm the latest numbers on the official DeepSeek API pricing page.
DeepSeek v4 Pro vs. Claude Fable 5
Claude Fable 5 is Anthropic’s flagship model for highly demanding reasoning, professional knowledge work, and long-running AI agents.
Both models support a one-million-token context window, but their product strategies are quite different.
| Category | DeepSeek v4 Pro | Claude Fable 5 |
|---|---|---|
| Primary positioning | Open reasoning, coding, and agent model | Premium model for long-running agents and knowledge work |
| Context window | 1 million tokens | 1 million tokens |
| Maximum output | Up to 384K tokens | Up to 128K tokens |
| Reasoning options | Non-think, Think High, and Think Max | Always-on adaptive thinking |
| Model availability | Open weights under the MIT License | Closed-source API |
| Uncached input price* | $0.66–$1.32 per million tokens | $10 per million tokens |
| Output price* | $1.98–$3.96 per million tokens | $50 per million tokens |
| Notable strengths | Coding, mathematics, cost efficiency, private deployment | Long-horizon autonomy, vision, and enterprise workflows |
DeepSeek prices shown are the peak and off-peak rates effective August 16, 2026..
Pricing Difference
Claude Fable 5 costs $10 per million input tokens and $50 per million output tokens. Anthropic also offers a 90% input-token discount for prompt caching.
Using DeepSeek’s updated pricing, Fable 5’s uncached input costs approximately 7.6 to 15.2 times more than DeepSeek v4 Pro. Its output costs approximately 12.6 to 25.3 times more.
This difference can become substantial in applications that repeatedly process large documents, codebases, or agent histories. A small experiment may cost very little on either model, but the pricing gap becomes more important at production scale.
Current Fable 5 pricing and availability are listed on Anthropic’s official model page.
Performance and Use Cases
Anthropic positions Fable 5 as a model capable of working autonomously for extended periods. The company reports leading results on coding evaluations such as FrontierCode, CursorBench, and ViBench, as well as strong performance in finance, analytics, vision, and other forms of professional knowledge work.
Fable 5 is particularly interesting for tasks involving:
- Long-running software engineering projects;
- Complex research and analytical workflows;
- Documents containing charts, tables, or diagrams;
- Visual inspection of applications and interfaces;
- High-value enterprise tasks where accuracy is more important than token cost.
DeepSeek v4 Pro takes a different approach. Its major advantage is bringing competitive reasoning and agent capabilities together with open weights and much lower API prices.
Is DeepSeek v4 Pro Worth Using?
DeepSeek v4 Pro is compelling because it combines several important features in one model: a one-million-token context window, strong coding and reasoning performance, agent capabilities, open weights, and highly competitive pricing.
For individual users, it can help with research, programming, and long-document analysis. For developers, it is a practical candidate for coding agents, enterprise search, document-processing systems, and automated workflows. For organizations with the necessary infrastructure, its MIT-licensed weights also create opportunities for private deployment and customization.
Claude Fable 5 represents a different path: a premium, closed-source model optimized for ambitious, long-running, high-value work. DeepSeek v4 Pro aims to make a significant portion of frontier-model capability available through a more open and affordable platform.
The best choice ultimately depends on more than a benchmark score. Reliability, latency, total task cost, data privacy, deployment options, and the amount of human review required are often more important than a model’s position on a leaderboard.
