DeepSeek V4: Pro vs Flash, API & Pricing

Compare DeepSeek V4 Pro and Flash: specifications, 1M context, API IDs, pricing, thinking modes, official weights, and legacy-alias migration. Last verified: August 5, 2026.

DeepSeek V4 was introduced as a two-model Preview family comprising DeepSeek-V4-Pro and DeepSeek-V4-Flash. The hosted API status later diverged: on July 31, 2026, DeepSeek released DeepSeek-V4-Flash-0731 in public beta behind deepseek-v4-flash, while V4 Pro and the App/Web models remained unchanged. Pro is the larger option for harder reasoning, coding, knowledge, and agent tasks; Flash is the smaller, lower-cost option for higher-throughput workloads. Both current API IDs retain a one-million-token context window and 384K maximum output.

Independent-site disclosure: Chat-Deep.ai is an independent website and is not affiliated with, endorsed by, or operated by DeepSeek. DeepSeek controls its official web service, apps, API, model repositories, availability, and prices.

Contents

• DeepSeek V4 at a glance
• V4 Pro vs Flash
• Specifications and capabilities
• Pricing snapshot
• Access and API model IDs
• Legacy alias migration
• Official model downloads
• Official and independent benchmarks
• Frequently asked questions

DeepSeek V4 at a glance

Fact	Officially documented information
Release naming	Original family: DeepSeek-V4 Preview; current Flash API version: DeepSeek-V4-Flash-0731
Architecture	Mixture of Experts (MoE)
V4 Flash size	284B total parameters; 13B activated
V4 Pro size	1.6T total parameters; 49B activated
Context length	1M tokens for both official API models
Maximum API output	384K tokens
Reasoning	Thinking and non-thinking; current effort mapping differs between Flash and Pro
API formats	OpenAI-compatible and Anthropic-compatible
Documented modality	Text generation; native image, audio, and video inputs are not listed for the V4 API endpoints
Published checkpoints	Pro, Flash, Pro Base, and Flash Base
Weights license	MIT License

The API model IDs and downloadable checkpoint names serve different purposes. deepseek-v4-pro and deepseek-v4-flash identify hosted API models. Names ending in -Base identify downloadable foundation checkpoints, not API model IDs. Similarly, “Max” is a reasoning-effort setting rather than a separate model endpoint.

DeepSeek V4 Pro vs Flash: key differences

The practical choice is straightforward: start with V4 Flash when cost, throughput, or request volume matters most; evaluate V4 Pro when harder reasoning, knowledge, coding, or agent performance can justify its higher token price and lower default concurrency. Flash is not merely a quantized copy of Pro. They are separate models with different total and activated parameter counts.

Specification	DeepSeek V4 Flash	DeepSeek V4 Pro
API model ID	deepseek-v4-flash	deepseek-v4-pro
Total parameters	284B	1.6T
Activated parameters	13B	49B
Context length	1M tokens	1M tokens
Maximum API output	384K tokens	384K tokens
Thinking and non-thinking	Supported	Supported
Default account concurrency	2,500 active connections	500 active connections
Post-trained weight precision	Mixed FP4 and FP8	Mixed FP4 and FP8
Good starting fit	High-volume text processing, routine reasoning, and cost-sensitive applications	Hard reasoning, complex coding, knowledge-heavy work, and difficult agent tasks

DeepSeek V4 Pro: specifications and good use cases

DeepSeek V4 Pro is the larger model from the original Preview family, with 1.6 trillion total parameters and 49 billion activated for each token. DeepSeek positions it for difficult reasoning, knowledge, coding, and agent workloads. Pro is the stronger model to test first when an incorrect result is more expensive than the additional API cost, or when a task contains long chains of dependent decisions.

• Complex repository analysis, debugging, and multi-step software agents.
• Knowledge-heavy research and difficult STEM reasoning.
• Long-horizon agents that must plan, call tools, and recover from failures.
• Selected High or Max reasoning requests where evaluation shows a measurable gain.

Pro should not be selected automatically for every request. Its listed output price is higher than Flash, and its default account concurrency is lower. A production router can send routine work to Flash and escalate only tasks that benefit from Pro in repeatable tests.

DeepSeek V4 Flash: specifications and good use cases

DeepSeek V4 Flash has 284 billion total parameters and 13 billion activated per token. The hosted deepseek-v4-flash ID now serves DeepSeek-V4-Flash-0731 in public beta and is currently the only documented DeepSeek model for the native Responses API. Its smaller active footprint, lower official API price, and higher default concurrency make it the practical starting point for routine chat, extraction, classification, summarization, coding agents, and first-pass agent work.

• High-volume classification, extraction, summarization, and support workflows.
• Cost-sensitive chat and content-processing applications.
• First-stage routing before difficult requests are escalated to Pro.
• Behavior-preserving migration from the older deepseek-chat and deepseek-reasoner aliases.

Flash can approach Pro on some reasoning tasks when given a larger thinking budget, but that does not make their behavior identical. Test both models on your documents, prompts, failure cases, latency targets, and budget before changing a production route.

DeepSeek V4 specifications and API capabilities

The table below describes the official DeepSeek API. OpenRouter, cloud platforms, other hosted providers, and self-hosted runtimes can expose different model names, feature sets, context limits, and operational behavior.

Capability	V4 Flash	V4 Pro	Important condition
Thinking mode	Yes	Yes	Enabled by default
Non-thinking mode	Yes	Yes	Disable thinking explicitly
JSON Output	Yes	Yes	Validate the result in application code
Tool calls	Yes	Yes	Your application executes the requested tool
Strict tool calls	Beta	Beta	Requires the /beta base URL and supports a documented JSON Schema subset
Chat Prefix Completion	Beta	Beta	Requires the /beta URL and an assistant message with prefix: true
FIM Completion	Beta	Beta	Non-thinking only; 4K maximum output; requires the /beta URL
Context caching	Yes	Yes	Enabled by default; only qualifying repeated prefixes receive cache-hit pricing
OpenAI Chat Completions format	Yes	Yes	Change the base URL and model ID
Native Responses API	Yes	Not currently documented	Flash only; stateless; no stored conversations, image input, or file input
Anthropic-compatible format	Yes	Yes	Compatibility does not imply support for every Anthropic field

Documentation consistency note (verified August 5, 2026): DeepSeek’s Models & Pricing matrix marks FIM Completion as available in non-thinking mode for both deepseek-v4-flash and deepseek-v4-pro, while the current FIM guide and /beta/completions API reference show only deepseek-v4-pro. Treat model eligibility as unresolved across the official sources: do not assume Flash FIM support from the summary matrix alone. Use the beta base URL, keep FIM in non-thinking mode, check the current endpoint reference, and run a bounded compatibility test with your own account before production use.

One-million-token context and 384K maximum output

Both official API models support a one-million-token context window and a maximum output of 384K tokens. The prompt and generated completion must fit within the context window; these figures should not be added together as a 1.384M-token total. One million tokens also does not mean one million words or guarantee perfect retrieval from a long prompt.

For independent hosted-API evidence beyond DeepSeek-published scores, see our measured DeepSeek V4 long-context results across four prompt tiers, four objective tasks, latency, token usage, and cost.

Long-context results improve when documents are filtered, labeled, ordered, and divided into meaningful sections. Retrieval, citations, and post-generation verification remain important. See the separate DeepSeek V4 context and output limits guide for token planning and failure cases.

Thinking, non-thinking, High, and Max

Thinking is enabled by default. Current effort mapping is model-specific. For Flash, requested low remains low, high remains high, xhigh maps to high, and max remains max. For Pro, requested low maps to high, high remains high, xhigh maps to max, and max remains max. Treat these mappings as dated provider behavior and recheck the official Thinking Mode guide. Max is a configuration, not a separate model endpoint.

In thinking mode, temperature, top_p, presence_penalty, and frequency_penalty have no effect. When a thinking turn makes a tool call, the returned reasoning_content must be passed back during the subsequent tool loop. Review the official thinking-mode rules and our implementation-focused DeepSeek thinking mode guide before deploying multi-turn agents.

Default account concurrency

DeepSeek publishes per-account concurrency limits of 2,500 active connections for V4 Flash and 500 for V4 Pro. These are concurrent connections, not requests per minute or requests per second. All API keys under one account share the limits. Exceeding the applicable allowance returns HTTP 429. DeepSeek also documents a no-additional-cost capacity request process, subject to its assessment of business needs. See the official rate-limit and isolation documentation.

DeepSeek V4 pricing snapshot

Price snapshot verified: August 5, 2026. Prices are in U.S. dollars per one million tokens and may be changed by DeepSeek.

Official API model	Cached input	Uncached input	Output
deepseek-v4-flash (DeepSeek-V4-Flash-0731)	$0.0028	$0.14	$0.28
deepseek-v4-pro	$0.003625	$0.435	$0.87

Cache-hit pricing applies only to input tokens that qualify through DeepSeek’s context cache; it is not the price of every input token. Thinking has no separate token rate, but longer reasoning can increase billed output. Check the official model and pricing table before approving a production budget, or use the DeepSeek pricing guide and calculator for per-request estimates.

How to access DeepSeek V4

Official web and app access

DeepSeek’s release notice states that V4 Preview is available through its web service, mobile apps, and API. Consumer-interface labels such as Expert Mode and Instant Mode belong to DeepSeek’s products. Do not assume that every interface label maps permanently and one-to-one to Pro or Flash unless the interface identifies the underlying model.

Official API model IDs and base URLs

• V4 Flash: deepseek-v4-flash
• V4 Pro: deepseek-v4-pro
• OpenAI-compatible base URL: https://api.deepseek.com
• Anthropic-compatible base URL: https://api.deepseek.com/anthropic

The API’s model-list response identifies those two V4 endpoints. Read DeepSeek model names and API IDs if you need to distinguish product labels, hosted API names, downloadable checkpoints, and provider-specific aliases.

This minimal request uses V4 Flash with thinking explicitly disabled:

curl https://api.deepseek.com/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ${DEEPSEEK_API_KEY}" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [
      {
        "role": "user",
        "content": "Summarize the differences between DeepSeek V4 Pro and Flash."
      }
    ],
    "thinking": {"type": "disabled"},
    "stream": false
  }'

For authentication, Node.js, Python, streaming, error handling, retries, and production safeguards, use the complete DeepSeek API documentation.

DeepSeek V4 API IDs and post-cutoff alias status

Legacy alias	Documented behavior during transition	Required explicit replacement
deepseek-chat	V4 Flash, non-thinking	deepseek-v4-flash with thinking disabled
deepseek-reasoner	V4 Flash, thinking	deepseek-v4-flash with thinking enabled

Do not migrate deepseek-reasoner automatically to Pro. Its behavior-preserving target is V4 Flash with thinking enabled. Test Pro separately where its quality advantage can justify the price and lower default concurrency. The complete DeepSeek V4 migration guide covers request changes and regression checks.

DeepSeek V4 download: official model weights

DeepSeek publishes four V4 repositories through the verified deepseek-ai organization on Hugging Face. The post-trained models use mixed FP4 and FP8 precision, while the Base checkpoints use mixed FP8 precision.

Official repository	Purpose	Parameters
DeepSeek-V4-Flash	Post-trained Flash model	284B total / 13B active
DeepSeek-V4-Pro	Post-trained Pro model	1.6T total / 49B active
DeepSeek-V4-Flash-Base	Foundation checkpoint for research and adaptation	284B total / 13B active
DeepSeek-V4-Pro-Base	Foundation checkpoint for research and adaptation	1.6T total / 49B active

“Open-weight” is the precise description: model weights are downloadable under the MIT License. That does not mean every training dataset, complete training pipeline, hosted service component, or provider deployment has been published. Use the official organization to avoid mislabeled uploads, and verify the publisher, conversion method, license, and checksums of third-party GGUF or other quantized derivatives.

Can you run DeepSeek V4 locally?

Yes, because official weights and inference instructions are available. However, neither full checkpoint is an ordinary laptop model. Flash has 284B total parameters, and Pro has 1.6T. Storage size is not the same as RAM or VRAM, and long contexts add substantial cache and serving requirements.

There is no single trustworthy “minimum GPU” figure for every precision, quantization, context length, batch size, runtime, and parallelism design. Self-hosted context and output limits depend on the serving stack and available infrastructure rather than automatically inheriting the official API limits. Use the local DeepSeek installation guide for deployment planning.

What changed from DeepSeek V3.2 and R1?

V4 keeps the Mixture-of-Experts approach and Multi-Token Prediction design used by earlier DeepSeek models while introducing architectural and training changes intended to make very long contexts more efficient.

Technical component	Role described in the DeepSeek report
Compressed Sparse Attention (CSA)	Compresses key-value entries and applies sparse selection to reduce long-context work
Heavily Compressed Attention (HCA)	Uses more aggressive key-value compression while retaining dense attention
Manifold-Constrained Hyper-Connections (mHC)	Strengthens residual connections while constraining signal propagation for stability
Muon optimizer	Supports faster convergence and training stability
DeepSeekMoE and on-policy distillation	Routes tokens through selected experts and consolidates domain-specialist capabilities

DeepSeek reports that Pro, at a one-million-token context, uses 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek V3.2. It reports 10% of the FLOPs and 7% of the KV cache for Flash under the same comparison. These are developer-reported efficiency figures, not measurements reproduced by Chat-Deep.ai. Architecture, training, precision, and evaluation details appear in the DeepSeek V4 technical report.

V4’s hosted transition also combines general responses, thinking, long-context processing, and tool use inside the Pro and Flash family. Historical R1 and V3.2 checkpoints can still matter for reproducible research and existing self-hosted systems. See the DeepSeek models guide when choosing across model families.

DeepSeek V4 benchmarks: official and independent results

DeepSeek-published Max results

The following scores were published by DeepSeek for the Max reasoning configuration. They are not independent Chat-Deep.ai measurements. Scores can change with prompts, reasoning budgets, tools, scaffolding, test harnesses, and evaluation rules.

Benchmark	V4 Flash Max	V4 Pro Max	What it tests
GPQA Diamond (Pass@1)	88.1	90.1	Graduate-level science reasoning
LiveCodeBench-v6 (Pass@1-CoT)	91.6	93.5	Competitive coding
SWE Verified (Resolved)	79.0	80.6	Repository issue resolution
Terminal Bench 2.0 (Acc)	56.9	67.9	Agent work in terminal environments
MRCR 1M (MMR)	78.7	83.5	Retrieval across a million-token context

The table supports a practical, limited conclusion: Pro shows a clearer advantage on difficult knowledge and agent tasks, while Flash can remain close on several reasoning and coding evaluations when given a larger reasoning budget. It does not establish that either model is the strongest option for every workload.

Independent evaluation: what CAISI found

In an independent evaluation, the U.S. Center for AI Standards and Innovation described DeepSeek V4 Pro as the most capable PRC model it had evaluated. CAISI found a wider gap from leading U.S. systems on several held-out and agentic tests than DeepSeek’s selected benchmark comparisons suggested. It also found V4 Pro more cost-efficient than GPT-5.4 mini on five of seven evaluated tasks, with relative cost ranging from 53% lower to 41% higher depending on the task.

Those findings should not be mixed directly with DeepSeek’s table. They used CAISI’s serving setup, system prompts, scaffolding, reasoning settings, token budgets, and prices available during its evaluation. Review the full CAISI evaluation of DeepSeek V4 Pro for its methodology and caveats.

DeepSeek V4 capabilities and limitations

Where V4 can fit well

• Agentic coding: repository analysis, debugging, planning, tool use, and multi-step software tasks.
• Long-document workflows: technical documentation, policies, logs, transcripts, research collections, and contract sets.
• Reasoning: mathematics, science, planning, and structured analysis.
• High-volume text processing: classification, extraction, summarization, and support workflows routed through Flash.
• Structured applications: JSON responses and tool requests combined with application-side validation.
• Open-weight research: experimentation or self-hosting where suitable infrastructure is available.

Important limitations

• Split release status: DeepSeek-V4-Flash-0731 is an official public-beta API release, while V4 Pro remained unchanged in the July 31 update after the original Preview announcement. Behavior, availability, limits, and prices can still change.
• Documented text modality: the V4 API pages do not list native image, audio, or video input. Do not confuse V4 with Janus or DeepSeek-VL.
• Company-published benchmarks: selected public scores do not guarantee results on your workload.
• Large local footprint: official checkpoints require substantial storage, memory, and serving infrastructure.
• Long-context fallibility: a 1M window does not eliminate missed details, hallucinations, or prompt-injection risk.
• JSON limitations: JSON Output is not a guarantee of full JSON Schema compliance, and outputs still require validation.
• Tool execution: the model requests a tool call; your application validates the arguments, executes the tool, and returns the result.
• Privacy and governance: do not send secrets, credentials, regulated records, or personal data without an approved processing, access, logging, and retention design.

Review what not to paste into DeepSeek before using any hosted model with sensitive information.

Which DeepSeek V4 model should you choose?

Workload	Starting choice	Reason
Routine chat, extraction, or classification	Flash, non-thinking	Lower cost and less unnecessary reasoning
High-volume production API	Flash	Lower token prices and higher listed default concurrency
Long-document summarization	Flash first	Test Pro only where evaluation shows a quality gain
Complex coding or repository repair	Pro, High	Stronger starting position for difficult coding and agent work
Science, mathematics, or difficult planning	Pro, High	More active capacity for knowledge-heavy reasoning
Unusually hard boundary tests	Pro, Max	Uses the largest documented reasoning budget
Cost-sensitive agent workflow	Flash, High	Evaluate whether it meets the task before escalation
Local research	Flash weights first	Smaller than Pro, although still infrastructure-intensive

A measurable routing policy is usually better than selecting Max or Pro for every request. Start with the least expensive configuration that passes your acceptance criteria, record failures, and escalate only the categories that show a repeatable benefit.

Frequently asked questions

What is DeepSeek V4?

DeepSeek V4 is a Mixture-of-Experts model family introduced in the April 24, 2026 Preview announcement. It includes V4 Pro and V4 Flash. DeepSeek later released DeepSeek-V4-Flash-0731 as an official public-beta API version on July 31, while V4 Pro remained unchanged. Both current API IDs list thinking and non-thinking modes and a 1M-token context window, and the family has MIT-licensed downloadable weights.

Is DeepSeek V4 officially released?

The original V4 family was an official Preview announcement, not a rumor. Since then, DeepSeek-V4-Flash-0731 has received its own official public-beta API release. DeepSeek said the July 31 update did not change V4 Pro or the App/Web models, so do not apply one blanket release label to every V4 surface.

What is the difference between DeepSeek V4 Pro and Flash?

Pro has 1.6T total parameters and 49B activated parameters and is positioned for harder reasoning, coding, knowledge, and agent tasks. Flash has 284B total and 13B activated and is positioned for lower cost and higher-throughput work. Both official API models list the same 1M context and 384K maximum output.

What are the DeepSeek V4 API model names?

The explicit model IDs are deepseek-v4-pro and deepseek-v4-flash. Pro Max, Flash Max, and non-thinking are configurations rather than separate API model names.

Does DeepSeek V4 support a one-million-token context?

Yes. DeepSeek lists a 1M-token context length for Pro and Flash. The prompt and completion must fit within that context, and the size does not guarantee perfect recall of every detail.

Where can I download DeepSeek V4?

Use the official Pro, Flash, Pro Base, and Flash Base repository links in the download table above. Verify any third-party quantization independently.

Is DeepSeek V4 open source or open weight?

The precise description is open weight: DeepSeek publishes downloadable V4 weights under the MIT License. That statement should not be expanded to imply that every dataset, service component, or infrastructure layer has been released.

Does DeepSeek V4 support images or video?

The official V4 API documentation and model cards describe text-generation models and do not list native image, audio, or video input for V4 Pro or Flash. A workflow that uses external tools or separate multimodal models does not make the V4 endpoint itself multimodal.

Does DeepSeek V4 replace deepseek-chat and deepseek-reasoner?

Yes for current integration planning. DeepSeek documented both strings as temporary compatibility aliases and set July 24, 2026 as their retirement cutoff. The current official catalog names only deepseek-v4-flash and deepseek-v4-pro; use those explicit IDs in new and production code.

Is DeepSeek V4 good for coding?

DeepSeek positions V4 for coding and agent workloads, and its published evaluations include competitive coding and repository-repair tests. Pro is the stronger starting choice for difficult coding agents, while Flash can be more economical for routine generation, explanation, classification, and high-volume code tasks. Test both on your repositories before deployment.

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Editorial verification: Specifications, API features, prices, migration dates, model repositories, and benchmark labels were rechecked against DeepSeek’s official release notice, API documentation, model cards, and technical report on August 5, 2026. The cited independent evaluation retains its original July 28 test date. Hosted-provider and self-hosted behavior can differ from the official DeepSeek API.

Related DeepSeek V4 guides

• DeepSeek API documentation
• DeepSeek API pricing
• DeepSeek V4 context and output limits
• Migrate legacy aliases to DeepSeek V4
• Run DeepSeek locally
• DeepSeek model names explained