DeepSeek V4.1 Flash 1017 Performance and Intelligence Benchmarks
DeepSeek's latest reasoning model achieves high intelligence scores with significant output speed.
1Model Performance Overview
DeepSeek V4.1 Flash 1017, released October 17, 2026, achieves a score of 45 on the Artificial Analysis Intelligence Index. This result positions the model significantly above the median score of 19 for comparable open-weight models. It demonstrates strong reasoning capabilities while maintaining a competitive edge in both speed and cost efficiency.
2Intelligence Index Methodology
The index evaluates models across ten distinct benchmarks, including AA-Briefcase, GDPval-AA, and Terminal-Bench 4.0. These tests measure agentic knowledge work, scientific research workflows, and coding proficiency. By aggregating these results, the index provides a standardized metric for comparing model reliability, hallucination rates, and overall reasoning quality across various complex tasks.
3Speed and Throughput Metrics
The model delivers an impressive output speed of 230.4 tokens per second, far exceeding the 77.9 tokens per second median for similar models. This high throughput is paired with a time to first token of 1.08 seconds, ensuring rapid responsiveness for users requiring real-time interaction or high-volume data processing tasks.
4Cost Efficiency Analysis
Pricing is set at $0.30 per million input tokens and $1.20 per million output tokens. These rates are notably lower than the respective medians of $0.43 and $1.68. The average cost per task on the Intelligence Index is $0.26, making it an attractive option for developers seeking high-performance reasoning at scale.
5Architecture and Parameters
DeepSeek V4.1 Flash 1017 utilizes a 552B total parameter count, with 16B active parameters per token during inference. This Mixture of Experts architecture allows the model to achieve high intelligence while optimizing computational resources. The design balances the need for extensive knowledge storage with efficient, targeted execution during each forward pass.
6Context Window Capabilities
The model supports a 1M token context window, equivalent to approximately 1,500 A4 pages of 12-point Arial font. This capacity is particularly relevant for Retrieval Augmented Generation workflows, where the model must process and reason over large datasets, legal documents, or extensive code repositories to provide accurate, context-aware responses.
7Multimodal Input Support
DeepSeek V4.1 Flash 1017 is a multimodal model capable of processing both text and image inputs. It can analyze, describe, and answer complex questions regarding visual data, expanding its utility beyond standard text-based reasoning. This versatility makes it suitable for diverse applications requiring integrated visual and textual comprehension capabilities.
8Verbosity and Output Patterns
During evaluation, the model generated 210M output tokens, significantly higher than the 140M median. This indicates a high level of verbosity, which is characteristic of its reasoning process. Users should anticipate detailed, comprehensive responses, as the model utilizes extended chain-of-thought steps to work through complex problems before finalizing its output.
9Reasoning Model Characteristics
As a dedicated reasoning model, it employs extended thinking processes to navigate intricate logic. This approach ensures that the model systematically evaluates potential solutions before generating an answer. Such mechanisms are essential for tasks requiring high accuracy in fields like software engineering, scientific research, and complex agentic work environments.
10Licensing and Availability
The model weights are available under an MIT license, though commercial use may be restricted depending on specific deployment conditions. It is categorized as an open-weights model, allowing researchers and developers to integrate its reasoning capabilities into private or specialized environments, provided they adhere to the stated licensing guidelines.
11Strategic Market Positioning
DeepSeek V4.1 Flash 1017 occupies a competitive quadrant in the current AI landscape, balancing high intelligence with cost-effective inference. By outperforming the median in speed, price, and reasoning benchmarks, it serves as a robust alternative for organizations prioritizing performance-per-dollar in their AI infrastructure and agentic workflow deployments.
Original article: Hacker News — DeepSeek V4.1 Flash 1017 Performance and Intelligence Benchmarks