DeepSeek V4.1 Flash
Technical specifications of DeepSeek V4.1 Flash
| Item | DeepSeek V4.1 Flash |
|---|---|
| Model name | DeepSeek V4.1 Flash |
| Provider | DeepSeek |
| Model generation | V4.1 |
| Model status | Newly released / previously available as a limited beta |
| API model ID | deepseek-v4.1-flash |
| Previous beta ID | deepseek-v4.1-flash-expires-on-0910 |
| Input modalities | Text + image |
| Architecture | New model architecture; detailed architecture specifications not yet publicly documented |
| Reasoning | Supported |
| Agent capabilities | Enhanced |
| Multimodal understanding | Native |
| Context length | Not yet independently documented by DeepSeek for V4.1 Flash |
| Maximum output | Not yet independently documented by DeepSeek for V4.1 Flash |
| Parameter count | Not yet publicly disclosed |
Specification note: DeepSeek's public API documentation currently documents
deepseek-v4-flash(DeepSeek-V4-Flash-0731),deepseek-v4-pro, anddeepseek-v4-flash-vision-exp; it does not yet provide a complete permanent model card for V4.1 Flash. Therefore, context length, parameter count, maximum output, and other undocumented specifications should not be copied from V4 Flash and presented as V4.1 specifications.
What is DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash is DeepSeek's newest Flash-tier model, built around a new architecture and designed to improve capability, inference speed, throughput, and scalability while retaining the efficiency-oriented positioning of the Flash family. DeepSeek's initial V4.1 Flash beta was described as having native multimodal support, rather than relying on the separate V4-Flash-Vision-Exp model used by the previous generation.
The model first appeared in a limited API beta under the temporary identifier deepseek-v4.1-flash-expires-on-0910. Developers could keep the existing DeepSeek API endpoint and change only the model identifier. The beta was explicitly time-limited, so that temporary identifier should not be treated as the permanent production model ID.
Main features of DeepSeek V4.1 Flash
- New model architecture: V4.1 Flash introduces a new architecture rather than being a simple post-training update to V4-Flash-0731. DeepSeek's beta announcement described the architecture as a major step toward higher capability ceilings, faster inference, higher throughput, and better scalability.
- Native multimodal understanding: V4.1 Flash natively supports visual understanding, allowing image inputs to be handled alongside text. This represents a more integrated multimodal design than the previous text-focused
deepseek-v4-flashmodel. - Faster inference: Speed is one of the central goals of V4.1 Flash. DeepSeek has positioned the model specifically around higher inference speed and throughput while retaining Flash-tier efficiency.
- Stronger agent capabilities: The new model is designed for demanding coding and agent workflows. Early reported benchmark results show substantial gains over the previous V4 Flash generation on software-engineering and agent evaluations.
- Higher capability ceiling: DeepSeek describes the new architecture as being designed to scale to larger models while improving the capability ceiling of the Flash tier.
- Unified multimodal workflows: Native visual understanding expands the model beyond text-only coding and reasoning into tasks involving screenshots, charts, interfaces, and other visual inputs.
Benchmark performance of DeepSeek V4.1 Flash
Early release information reports the following results for V4.1 Flash:
| Benchmark | Reported result |
|---|---|
| GPQA Diamond | 90.9 |
| HLE | 36.8 |
| HLE with tools | 63.9 |
| Codeforces | 3471 |
| MathArena Apex | 65.6 |
| Terminal-Bench 2.1 | 90.6 |
| Terminal-Bench 3.0 | 30.0 |
| Terminal-Bench 4.0 | 31.2 |
| DeepSWE v1.1 | 74.2 |
| NL2Repo-Bench | 65.4 |
| CyberGym | 88.1 |
| SEC-Bench Pro | 62.8 |
| Automation-Bench | 54.8 |
| Agents' Last Exam | 31.8 |
| Chartography with tools | 78.9 |
| BabyVision with tools | 89.6 |
| ZeroBench-main | 49.0 |
These figures are reported in current coverage of the V4.1 Flash release, but DeepSeek's public API documentation has not yet published a complete official benchmark table for V4.1 Flash. They should therefore be labeled as reported release figures rather than presented as independently verified specifications.
The most notable reported improvements are in agentic coding and software engineering. A Terminal-Bench 2.1 score of 90.6 and DeepSWE v1.1 score of 74.2 indicate that V4.1 Flash is targeting substantially more capable autonomous coding workflows than the earlier V4 Flash.
DeepSeek V4.1 Flash vs DeepSeek V4 Flash
| Capability | DeepSeek V4.1 Flash | DeepSeek V4 Flash |
|---|---|---|
| Generation | V4.1 | V4 |
| Architecture | New architecture | V4 architecture |
| Native vision | Yes | No |
| Separate vision model required | No | V4-Flash-Vision-Exp for image input |
| Agent capability | Enhanced | Strong |
| Coding performance | Reported major improvement | Strong |
| Inference speed | Improved positioning | Fast |
| Status | New release / recently beta-tested | Stable API model |
| Public technical documentation | Still emerging | Fully documented |
The distinction is important: V4.1 Flash should not simply be described as V4 Flash with vision added. DeepSeek describes V4.1 Flash as using a new architecture with native multimodal capabilities and improvements in capability, speed, and scalability.
The previous deepseek-v4-flash remains documented by DeepSeek as DeepSeek-V4-Flash-0731, with a 1M-token context window, 384K maximum output, thinking/non-thinking modes, tool calls, JSON output, Responses API support, and Anthropic API compatibility. Those specifications belong to V4 Flash and should not automatically be attributed to V4.1 Flash until DeepSeek publishes equivalent V4.1 documentation.
Recommended use cases
1. Multimodal coding agents
V4.1 Flash's native visual understanding makes it particularly interesting for coding agents that need to interpret screenshots, UI layouts, diagrams, or visual debugging information alongside source code.
2. Autonomous software engineering
The reported Terminal-Bench, DeepSWE, NL2Repo, and CyberGym results suggest that V4.1 Flash is well suited to repository-level coding, debugging, terminal interaction, security testing, and other agentic software-engineering tasks.
3. High-throughput AI agents
The Flash positioning and emphasis on faster inference make V4.1 Flash a strong candidate for applications running many model calls in parallel, particularly coding agents and automated development pipelines.
4. Visual document and interface analysis
Native multimodal support expands potential use cases to screenshots, charts, diagrams, UI analysis, and other workflows where a text-only model would require a separate vision model.
5. Complex reasoning at Flash-tier efficiency
The reported GPQA Diamond, HLE, MathArena Apex, and agent benchmark results indicate that V4.1 Flash is intended to push much higher reasoning capability into the Flash tier.
Limitations and considerations
The biggest limitation for developers evaluating V4.1 Flash today is documentation maturity. DeepSeek's public API model table still lists deepseek-v4-flash, deepseek-v4-pro, and deepseek-v4-flash-vision-exp, while the temporary V4.1 beta identifier was explicitly scheduled to expire on September 10.
Consequently, specifications such as parameter count, context length, maximum output, detailed architecture, and stable API guarantees should not be inferred from V4 Flash.
Developers should also distinguish the temporary beta identifier:
deepseek-v4.1-flash-expires-on-0910
from the permanent model name:
deepseek-v4.1-flash
The former was a time-boxed test endpoint and should not be used as a long-term production identifier.
API integration
The V4.1 Flash beta used the existing DeepSeek API endpoint and required changing the model name rather than changing the API base URL. This means the model is designed to fit into existing DeepSeek/OpenAI-compatible integration patterns.
DeepSeek's established API supports OpenAI-compatible and Anthropic-compatible interfaces, while the current stable V4 models also support the Responses API. However, V4.1-specific API compatibility should be treated as provisional until DeepSeek updates its official model documentation.