Deepseek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 represents the latest evolution in DeepSeek's high-efficiency flagship MoE family. Built on an asymmetric Causal-Encoder-Decoder architecture (552B total parameters with only 8B input activation), it drastically reduces KV-cache overhead while unlocking native multimodal vision capabilities. The 1017 release introduces breakthrough "wish-driven programming" capabilities, allowing users to generate complex 3D interactive applications, game mechanics, and web frontends with minimal prompts and fast-turnaround agentic execution.
Related Models
Deepseek V4.1 Flash
DeepSeek V4.1 Flash is DeepSeek's open-weights, ultra-efficient vision-language Mixture-of-Experts (MoE) foundation model. Built on a novel Causal Encoder-Decoder (CED) architecture, it distributes 552B total parameters while activating only 8B parameters during prefill and 16B parameters during decode. Featuring native visual-text joint embedding, Compressed Sparse Attention, Engram memory, and a 4x KV-cache memory reduction, it effortlessly processes up to a 1M-token context window at high throughput. Engineered for extreme cost efficiency and frontier agentic coding, DeepSeek V4.1 Flash powers autonomous software engineering, multimodal document RAG, UI browser automation, and high-concurrency API integrations.
Deepseek V4 Pro 0813
DeepSeek-V4-Pro-0813 is a flagship MoE model released on August 13, 2026, serving as the official GA release built for complex logical reasoning, code construction, and AI Agent collaboration. Architecture & Performance: 1.6T parameters (49B active), built-in DSpark speculative decoding, with significantly enhanced inference throughput. Compatible with OpenAI/Anthropic APIs, deeply integrated with DeepSeek Harness, and delivers outstanding performance on Terminal Bench.
Deepseek V4 Flash 0731
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model from DeepSeek, featuring 284B total parameters with 13B activated per token and a 1-million-token context window. Designed for fast inference and high-throughput workloads, it delivers strong reasoning and coding capabilities while maintaining excellent cost efficiency. Its hybrid attention architecture enables efficient long-context processing. The model supports **high** and **xhigh** reasoning levels, with **xhigh** representing the maximum reasoning effort. It is ideal for coding assistants, conversational systems, and agentic workflows where responsiveness, scalability, and cost efficiency are essential.
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Deepseek V4.1 Flash 1017 API
Deepseek V4.1 Flash 1017 is an efficiency-optimized Mixture-of-Experts (MoE) large language model developed by DeepSeek, officially released on October 17, 2026. As a minor update to the V4.1 Flash model, it delivers Pareto improvements in both performance and cost. It enhances reasoning, coding, agentic tasks, and visual benchmarks through self-feedback reinforcement learning. Task execution time and output length are significantly reduced, generating more concise tokens and lowering single-task costs. It also introduces "wish-driven programming" capabilities, enabling the generation of complex 3D interactive applications, game mechanics, and web front-ends from minimal prompts. The model is specifically optimized for agent frameworks such as DeepSeek Harness, CodeBuddy, and WorkBuddy, achieving a reasoning speed of approximately 250 tok/s.
On the iCreat platform, Deepseek V4.1 Flash 1017 is accessible via the OpenAI-compatible Chat Completions endpoint. It features a 1 million token context window and a maximum output of approximately 384,000 tokens. The platform offers unified pricing unaffected by peak/off-peak hours, with input at $0.3, output at $1.2, and cache read at $0.006 per million tokens.
Model Positioning
Deepseek V4.1 Flash 1017 is positioned as an efficiency-optimized agent model tailored for high-frequency, complex programming and automated task execution. By significantly shortening task execution time and output length while maintaining the same API unit price, it achieves a lower total cost per task. It is designed for developers and enterprises seeking top-tier agentic performance and cost-efficiency, particularly those leveraging agent frameworks like Harness, CodeBuddy, and WorkBuddy.
Core Capabilities
Efficiency-Optimized MoE Architecture
Built on an asymmetric Causal Encoder-Decoder (CED) architecture with a total of 552B parameters. It activates 8B parameters for input and 16B parameters for output, ensuring rapid processing and generation while maintaining a reasoning speed of approximately 250 tok/s.
Pareto Improvement in Performance and Cost
Through self-feedback reinforcement learning, the 1017 version improves reasoning, coding, agentic, and visual benchmarks while reducing task execution time and output length. This results in more concise token generation and a significant reduction in single-task costs without increasing API unit prices.
Wish-Driven Programming
Introduces a novel capability where users can generate complex 3D interactive applications, game mechanics, and web front-ends using extremely minimal prompts, streamlining the rapid prototyping and development process.
Agentic Framework Optimization
Specifically optimized for DeepSeek Harness, CodeBuddy, and WorkBuddy. It achieves the highest scores on official benchmarks such as DeepSWE v1.1, Automation-Bench, Agents' Last Exam, and Apex Bench v1.1, officially leading Claude Opus 5, GPT 5.6 Sol, Kimi K3, and GLM 5.3.
Deep Thinking and Flexible Reasoning
Supports a deep thinking mode via the thinking object and offers five reasoning levels (minimal, low, medium, high, max) with a default of medium. The API request body supports reasoning_effort with options high and xhigh (highest intensity).
Pricing
| Token Type | Price (USD) | Unit |
|---|---|---|
| Input | $0.3 | Per million tokens |
| Output | $1.2 | Per million tokens |
| Cache read | $0.006 | Per million tokens |
Note: The above are iCreat platform prices, consistent with DeepSeek's official overseas pricing baseline. The official API uses peak/off-peak time-based pricing (off-peak: input $0.15, output $0.60, cache read $0.003 per million tokens; peak: double the off-peak rate). The 1017 version maintains the same API unit price. iCreat uses unified pricing unaffected by time periods; iCreat platform prices prevail.
Application Scenarios
- Complex 3D interactive application and game mechanism generation via wish-driven programming
- Web front-end rapid prototyping and development
- Automated software engineering tasks using agent frameworks like DeepSeek Harness, CodeBuddy, and WorkBuddy
- Long-context document processing and analysis requiring up to 1 million tokens
- Advanced reasoning and coding tasks demanding high efficiency and low latency
Model Comparison
Comparison Table 1: Deepseek V4.1 Flash 1017 vs V4.1 Flash
| Model | Release Date | Core Upgrades | Context Window | Pricing (Input/Output/Cache) |
|---|---|---|---|---|
| Deepseek V4.1 Flash 1017 | 2026-10-17 | Reasoning/coding/agentic/visual benchmark improvements; shorter task time and output; wish-driven programming | 1 million tokens | $0.3 / $1.2 / $0.006 |
| Deepseek V4.1 Flash | 2026-09-10 | Initial CED asymmetric architecture; DeepSWE v1.1 74.2% | 1 million tokens | $0.3 / $1.2 / $0.006 |
Note: Both versions share the same iCreat unit price. The 1017 version achieves lower total single-task costs through shorter task times and output lengths, realizing a Pareto improvement in performance and cost.
Comparison Table 2: Cross-Vendor Agent Model Comparison
| Model | Developer | Pricing (Input/Output) | Context Window | Open Source | Benchmark Highlights |
|---|---|---|---|---|---|
| Deepseek V4.1 Flash 1017 | DeepSeek | $0.3 / $1.2 | 1 million tokens | Yes (MIT) | DeepSWE, Automation-Bench, Agents' Last Exam, Apex Bench leading (official) |
| Gemini 4 Argon | $2 / $10 (promo, $4/$20 from Dec) | 1 million output limit | No | DeepSWE v1.1 77.9% (third-party) | |
| Claude Opus 5.5 | Anthropic | $4 / $20 | - | No | Terminal-Bench 4.0 66.4%; FrontierSWE v2 62.3% |
Note: DeepSeek offers the lowest price among the three and is the only MIT open-source model. Official reports state 1017 leads Opus 5 and GPT 5.6 Sol on DeepSWE, Automation-Bench, Agents' Last Exam, and Apex Bench (vendor benchmark methodologies vary; exercise caution when comparing).
Why Choose Deepseek V4.1 Flash 1017?
- Lower total task costs achieved through reduced execution time and more concise output, without increasing unit prices
- Top-tier performance in agentic and coding benchmarks, officially leading models like Claude Opus 5 and GPT 5.6 Sol
- Innovative "wish-driven programming" for rapid generation of complex 3D and web applications from minimal prompts
- Optimized for popular agent frameworks including DeepSeek Harness, CodeBuddy, and WorkBuddy
- Massive 1 million token context window with up to 384K tokens of maximum output
- MIT open-source weights available on Hugging Face and ModelScope for autonomous deployment
Specifications
| Field | Value |
|---|---|
| Model Name | Deepseek V4.1 Flash 1017 |
| Developer | DeepSeek |
| Model ID | deepseek-v4-1-flash-261017 |
| Release Date | 2026-10-17 |
| Model Type | Efficiency-optimized MoE Large Language Model |
| Architecture | Asymmetric Causal Encoder-Decoder (CED) |
| Total Parameters | 552B |
| Input Activation Parameters | 8B |
| Output Activation Parameters | 16B |
| Base URL | https://api.icreat.ai/v1 |
| Endpoint | POST /chat/completions |
| Authentication | Authorization: Bearer ${ICREAT_API_KEY} |
| Context Window | 1 million tokens |
| Max Output | ~384,000 tokens |
| KV Cache | 890 bytes/token |
| Reasoning Levels | minimal, low, medium, high, max (default: medium) |
| Thinking Mode | Supported via thinking object (e.g., {"type": "enabled"}) |
| Streaming | Supported (stream: true/false) |
| Function Calling | Supported (OpenAI compatible) |
| Context Caching | Supported |
| Open-Source License | MIT |
| Billing Unit | Per million tokens |
| API Mode | Synchronous POST (JSON / SSE streaming) |
Architecture
Deepseek V4.1 Flash 1017 utilizes an asymmetric Causal Encoder-Decoder (CED) architecture. As a Mixture-of-Experts (MoE) model, it has a total of 552B parameters, activating only 8B parameters for input processing and 16B parameters for output generation. This asymmetric design allows for highly efficient inference, contributing to its approximate 250 tok/s reasoning speed while maintaining top-tier performance across various benchmarks.
Notes
- The
modelparameter must use the iCreat model code:deepseek-v4-1-flash-261017. - iCreat uses unified pricing unaffected by peak/off-peak hours; the official API unit price remains unchanged in the 1017 version, and task cost reductions stem from shorter task times and output lengths.
- The
reasoning_effortparameter in the request body acceptshighandxhigh(highest intensity). The model overall supports five reasoning levels: minimal, low, medium, high, and max (default: medium). - The
thinkingobject (e.g.,{"type": "enabled"}) controls the deep thinking mode, depending on model support. - The context window is 1 million tokens, with a maximum output of approximately 384K tokens.
- Weights are openly available under the MIT license on Hugging Face and ModelScope for autonomous deployment.
- DeepSeek officially provides Search and Fetch interfaces ($0.75 per 1,000 successful calls, failed calls not billed), which are DeepSeek official API capabilities and not parameters of this iCreat endpoint.
Frequently Asked Questions
How do I call Deepseek V4.1 Flash 1017?
You can call it using the OpenAI-compatible /v1/chat/completions endpoint. Set the model field to deepseek-v4-1-flash-261017, use Bearer token authentication with your iCreat API key, and you can enable streaming by setting stream to true.
How is the cost calculated?
The cost is calculated based on token usage: input is $0.3, output is $1.2, and cache read is $0.006 per million tokens. The 1017 version's unit price is the same as V4.1 Flash. While the official API uses peak/off-peak time-based pricing, iCreat uses unified pricing unaffected by time periods. The usage field in the response provides the token statistics.
What is the difference between this and V4.1 Flash?
This is a minor update featuring improvements in reasoning, coding, agentic, and visual benchmarks. Task execution time and output length are shortened, resulting in lower single-task costs. It also introduces "wish-driven programming." The unit price remains unchanged.
How do I set the reasoning intensity?
The model supports five reasoning levels: minimal, low, medium, high, and max, with a default of medium. In the API request body, the reasoning_effort parameter can be set to high or xhigh (highest intensity).
What is "wish-driven programming"?
It is a rapid agentic execution capability that generates complex 3D interactive applications, game mechanics, and web front-ends from extremely minimal prompts. It is specifically optimized for agent frameworks like Harness, CodeBuddy, and WorkBuddy.
Is it open-source?
Yes, it is open-sourced under the MIT license. The weights are available on Hugging Face and ModelScope, allowing for autonomous deployment.