DeepSeek: DeepSeek V4 Flash
Analysis Summary
DeepSeek V4 Flash is a cost-focused model with strong coding capability and a one-million-token context window. Tool use and function calling support practical automation, while its reasoning and instruction-following results provide a useful base for structured generation and analysis. The text-only modality limits visual workflows, but the model is well suited to code and document tasks.
For businesses, it fits software assistance, repository analysis, SEO production, classification, and tool-driven workflows where throughput matters. Its agentic capability is less advanced than its coding capability, so complex autonomous processes should include validation and narrower task boundaries. The long context is valuable for large specifications and codebases.
Pricing is highly attractive at $0.14 per million input tokens and $0.28 per million output tokens. Adopt it for cost-sensitive coding and automation, especially when premium reasoning is unnecessary.
Assessed August 9, 2026
Editorial notes
DeepSeek V4 Flash combines strong coding, a one-million-token context window, reliable function calling, and very low pricing, making it useful for high-volume engineering and structured content workflows.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
DeepSeek V4 Flash combines strong coding, a one-million-token context window, reliable function calling, and very low pricing, making it useful for high-volume engineering and structured content workflows.
Benchmark scores
Magenta = intelligence · Ink = technical/agentic · Cyan = content & long-context · Grey = community benchmarks. Data: Artificial Analysis, Hugging Face.
51.8 Intelligence Index·69.1 Coding Index·48.4 Agentic Index
How DeepSeek: DeepSeek V4 Flash compares
DeepSeek: DeepSeek V4 Flash ranks #28 of 424 AI models we track for overall intelligence, #28 of 197 for coding, #23 of 179 for agentic tasks. Its 1M-token context window is larger than 95% of the models we list. At $0.14 per million input tokens it is cheaper than 62% of comparable models.
Dark bar = input · light bar = output, scaled to the priciest peer.
1M tokens ≈ 8 full-length novels or ~2,500 pages of business documents in a single request.
Strongest on value. The pulled-in technical corner is the trade-off, and if the shape matters more than the price, this is your model.
Compare shapes side-by-side →Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.14 | $0.000140 |
| Output | $0.28 | $0.000280 |
What would DeepSeek: DeepSeek V4 Flash cost your business?
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A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 701 models → Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save DeepSeek: DeepSeek V4 Flash for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout DeepSeek: DeepSeek V4 Flash
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and..
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Frequently asked questions about DeepSeek: DeepSeek V4 Flash
How much does DeepSeek: DeepSeek V4 Flash cost?
DeepSeek: DeepSeek V4 Flash costs $0.14 per million input tokens and $0.28 per million output tokens.
What is the context window of DeepSeek: DeepSeek V4 Flash?
DeepSeek: DeepSeek V4 Flash has a context window of 1,048,576 tokens (1M).
Is DeepSeek: DeepSeek V4 Flash good for coding?
On our coding benchmark index, DeepSeek: DeepSeek V4 Flash ranks #28 of 197 models, placing it in the top quartile of the field for code generation and debugging.
What can DeepSeek: DeepSeek V4 Flash do?
DeepSeek: DeepSeek V4 Flash supports tool use and function calling.
Who created DeepSeek: DeepSeek V4 Flash?
DeepSeek: DeepSeek V4 Flash is developed by DeepSeek and was released on April 24, 2026.
