DeepSeek Flash is the commonly call used name for DeepSeek V4.1 Flash: DeepSeek’s latest multimodal AI model optimized for efficient inference, long-context reasoning, coding, and agentic workflows. The model was officially released on September 10, 2026, and is available through the DeepSeek API under the model ID deepseek-flash.
This page uses DeepSeek Flash as the primary keyword while keeping all technical specifications and benchmark results specific to DeepSeek V4.1 Flash.
Technical Specifications of DeepSeek Flash
| Specification | DeepSeek Flash |
|---|---|
| Official API model ID | deepseek-flash |
| Release date | September 10, 2026 |
| Architecture | Causal Encoder-Decoder (CED) with Mixture-of-Experts (MoE) |
| Backbone parameters | 552B |
| Activated parameters | Approximately 8B during prefill; 16B during decoding |
| Context window | Up to 1 million tokens |
| Input modalities | Text and images |
| Reasoning control | Continuously adjustable from 1 to 100 |
| MoE configuration | 1 shared expert and 384 routed experts; 6 routed experts activated per token |
| Global KV-cache footprint | Approximately 890 bytes per token |
| Training corpus | Approximately 45T multimodal tokens |
| License | MIT License |
| Off-peak official pricing | RMB 0.02/M cache-hit input tokens; RMB 1/M cache-miss input tokens; RMB 4/M output tokens |
| Peak pricing | Twice the off-peak rates |
Pricing, availability, and routing policies may change. Confirm the current model ID and rates before deploying a production application.
What Is DeepSeek Flash?
DeepSeek Flash is a multimodal Mixture-of-Experts model designed for long-context reasoning, software engineering, tool calling, and autonomous agent workflows.
The model combines a 552-billion-parameter backbone with sparse activation. It uses approximately 8 billion parameters while processing the input and 16 billion parameters during decoding. This allows DeepSeek Flash to retain substantial model capacity without activating the entire network for every token.
The model supports up to one million tokens of context and can process images and text natively. It is available through the DeepSeek API under the model name deepseek-flash, while older V4 Flash identifiers are routed to the new model for compatibility.
What Are the Main Features of DeepSeek Flash
Causal Encoder-Decoder Architecture
DeepSeek Flash uses a 40-layer Causal Encoder-Decoder architecture consisting of 20 encoder layers and 20 decoder layers.
Instead of generating a separate global KV cache at every decoder layer, the decoder projects its global cache from the encoder’s final hidden states. This reduces the amount of computation required during long-input processing and is especially useful for input-heavy agent workloads.
Sparse Mixture-of-Experts Routing
Each MoE layer contains one shared expert and 384 routed experts. Six routed experts are activated for each token.
Sparse routing lowers the computational cost of inference while allowing the model to maintain a large overall parameter capacity. This design is useful for high-volume services where throughput and cost are as important as maximum intelligence.
One-Million-Token Context
DeepSeek Flash supports a context window of up to 1M tokens. This allows applications to provide very large inputs in a single request, such as:
- Complete software repositories
- Long legal or financial documents
- Technical manuals
- Research archives
- Multi-session agent histories
- Multiple related files
The model card recommends a context window of 1M tokens and at least 256K max_tokens for local inference scenarios.
Compressed Sparse Attention 2
DeepSeek Flash introduces Compressed Sparse Attention 2 (CSA2), which uses Full, Reindex, and Reuse attention modes.
These modes allow the model to share KV information between layers and reuse sparse-attention indices. A hierarchical sparse indexer further limits the candidate pool examined by later layers.
Together with FP4 main-KV caching, these changes reduce the global KV-cache footprint to approximately 890 bytes per token—about one-quarter of the footprint reported for DeepSeek V4 Flash.
Native Multimodal Understanding
DeepSeek Flash processes images and text in the same conversation. Its vision system uses a DeepSeek-ViT encoder, two-dimensional rotary position embeddings, pixel-unshuffle downsampling, and a projection layer that converts visual features into language-model embeddings.
The model was trained from scratch on a multimodal corpus containing approximately 45 trillion tokens.
Continuously Adjustable Reasoning
Instead of providing only a few fixed reasoning modes, DeepSeek Flash supports a numerical reasoning-effort setting from 1 to 100.
Lower values can reduce latency and cost for routine tasks, while higher values allocate more computation to difficult coding, mathematics, planning, and agentic workflows.
Agent-Oriented Components
DeepSeek Flash includes several components intended for AI-agent use:
- Engram conditional memory with token-based lookup
- DSpark speculative decoding
- Long-context sparse attention
- Prompt and response encoding utilities
- Support for images, tool calls, and reasoning traces
- Compatibility with agent scaffolds such as Claude Code, Codex, mini-SWE, and DeepSeek Harness
How DeepSeek Flash Works
A typical DeepSeek Flash request follows this sequence:
- Text, images, system instructions, conversation history, and tool definitions are converted into DeepSeek’s conversation format.
- Images are processed by the vision encoder and converted into visual embeddings.
- The MoE router selects a small number of experts for each token.
- CSA2 and hierarchical indexing reduce the attention cost for long contexts.
- The selected reasoning effort determines how much inference computation is allocated.
- DSpark generates and verifies candidate tokens to improve decoding speed.
- For agent workflows, the model generates tool calls, receives tool results, and continues reasoning within the same context.
This workflow makes DeepSeek Flash particularly suitable for applications that read large amounts of information but produce relatively short decisions, code changes, or tool actions.
How Does DeepSeek Flash Perform on Benchmarks?
The following scores come from DeepSeek’s official API update and the V4.1 Flash Hugging Face model card. Results use different evaluation settings, including maximum reasoning effort, specific agent scaffolds, and — in some cases — one-million-token contexts.
| Benchmark | DeepSeek V4.1 Flash |
|---|---|
| GPQA Diamond | 90.9 |
| Humanity’s Last Exam | 36.8 |
| Humanity’s Last Exam, text-only subset | 39.1 |
| Codeforces rating | 3,471 |
| MathArena Apex | 65.6 |
| Terminal-Bench 2.1 | 90.6 |
| Terminal-Bench 3.0 | 30 |
| Terminal-Bench 4.0 | 31.2 |
| DeepSWE v1.1 | 74.2 |
| ProgramBench | 20.3 |
| NL2Repo-Bench | 65.4 |
| CyberGym | 88.1 |
| SEC-Bench Pro | 62.8 |
| ExploitGym | 15.3 |
| Humanity’s Last Exam with tools | 63.9 |
| AutomationBench | 54.8 |
| Agents’ Last Exam | 31.8 |
| Chartography with tools | 78.9 |
| BabyVision with tools | 89.6 |
| ZeroBench-main | 49 |
In practical terms, the results show that DeepSeek Flash is particularly strong in coding, terminal interaction, software maintenance, cybersecurity evaluation, and tool-using agents. Its Terminal-Bench 2.1 score of 90.6 and DeepSWE v1.1 result of 74.2 indicate strong performance on software-engineering workflows. The 88.1 CyberGym score and 62.8 SEC-Bench Pro score also point to useful capabilities for security analysis.
The model reports 56.5 on MMMU-Pro, 77.9 on CVBench, 95.6 on DocVQA, and 86.0 on RefCOCO average, demonstrating that its multimodal abilities extend beyond simple image captioning to visual question answering, document understanding, and reference grounding.
DeepSeek’s model card emphasizes that these are not context-free scores. Agent results vary according to the harness, prompt format, tool definitions, context limit, sampling parameters, and number of samples per task. Developers should therefore validate DeepSeek Flash on their own workload before relying on benchmark rankings.
DeepSeek Flash vs DeepSeek V4 Pro vs DeepSeek V4 Flash
| Model | Architecture focus | Total/backbone parameters | Activated parameters | Multimodal | Context |
|---|---|---|---|---|---|
| DeepSeek Flash | CED MoE | 552B | 8B prefill / 16B decode | Native | 1M |
| DeepSeek V4 Pro | MoE | 1.6T | 49B | Yes | 1M |
| DeepSeek V4 Flash | MoE | 284B | 13B | Limited/variant-dependent | 1M |
DeepSeek reports that DeepSeek Flash outperforms V4 Pro across performance, speed, cost, and total completion time in its internal and external testing. As a result, DeepSeek plans to route deepseek-v4-pro requests to DeepSeek Flash after September 14, 2026, until V4.1 Pro becomes available.
Compared with the earlier V4 Flash, DeepSeek Flash offers a different architecture, native multimodal processing, stronger agent benchmarks, and substantially lower KV-cache requirements.
What DeepSeek Flash Best for
Coding Assistants
DeepSeek Flash can analyze large repositories, explain unfamiliar code, generate patches, write tests, and diagnose failures. Its long context is useful for cross-file dependency analysis and repository-level changes.
Autonomous Software Agents
The model is suitable for agents that execute commands, edit files, run tests, inspect logs, and continue planning after receiving tool results.
Long-Document Analysis
Organizations can provide contracts, manuals, research papers, financial records, and internal documentation in a single context instead of aggressively splitting the material.
Multimodal Document Processing
Native vision capabilities support:
- Chart interpretation
- Screenshot analysis
- Scanned-document understanding
- Invoice and table extraction
- Visual quality inspection
- Document question answering
Research and Data Analysis
DeepSeek Flash can synthesize information across extensive sources, compare competing explanations, and perform multi-step analysis with external tools.
Security and Code Auditing
The model’s CyberGym, SEC-Bench Pro, and ExploitGym results indicate potential for security research and code-audit assistance. Security-related outputs should always be tested in controlled environments and reviewed by qualified professionals.
High-Volume API Automation
Sparse activation, KV-cache compression, speculative decoding, and adjustable reasoning make DeepSeek Flash attractive for applications that must manage cost and latency at scale.
How Does CometAPI Provide Access to the DeepSeek Flash API?
CometAPI can provide a unified gateway for accessing DeepSeek Flash through a standard API workflow.
A typical integration process is:
- Create a CometAPI account and generate an API key.
- Configure the CometAPI base URL in your application.
- Select the current DeepSeek Flash model ID listed in the CometAPI dashboard.
- Send chat, multimodal, or agent requests.
- Monitor token usage, latency, errors, and costs in the CometAPI console.
CometAPI’s currently supported model ID, parameter names, and image-input format should be confirmed in its latest documentation before production deployment.
Why Should You Choose CometAPI for DeepSeek Flash?
CometAPI can be useful when you want to use DeepSeek Flash without operating the model-serving infrastructure yourself.
Potential benefits include:
- One API interface for multiple AI providers
- Centralized API-key and usage management
- Unified billing and budget monitoring
- Easier comparison between DeepSeek Flash and alternative models
- Reduced work for provider-specific integrations
- Familiar OpenAI-style request formats
- Simpler model switching and fallback configuration
- Centralized observability for multi-model applications
This approach is particularly useful for startups, agencies, and development teams that want to test DeepSeek Flash before investing in dedicated GPU infrastructure.
When Is CometAPI the Better Choice?
CometAPI is likely to be the better choice when:
- You need to launch a prototype quickly.
- You want to test multiple models through one API.
- Your team does not want to operate large GPU clusters.
- You need unified usage and cost controls.
- You want a fallback model during provider congestion.
- Your traffic is moderate, irregular, or still growing.
- You need to evaluate DeepSeek Flash’s multimodal and agent capabilities before committing to self-hosting.
Direct DeepSeek access or self-hosting may be preferable when you require strict control over data residency, network topology, inference configuration, or high-volume predictable traffic.