DeepSeek V4 Flash is the better default for most users. It matched Pro in our coding tests and 20.7K-token retrieval tests, while costing far less. Pro justified its higher price only on the hardest task: it scored 12/12 on a strict launch memo where Flash scored 8/12.
That makes the choice straightforward. Start with Flash for routine coding, retrieval, extraction, and high-volume work. Pay for Pro when one arithmetic error, missed constraint, or malformed deliverable could cost more than the model.
Is DeepSeek V4 officially released?
Ya. As checked on August 13, 2026, the official Models & Pricing page lists DeepSeek-V4-Pro-0813 and DeepSeek-V4-Flash-0731. That is the current status to use—not the July 31 changelog sentence saying Pro would follow soon.
DeepSeek V4 Pro
Version: Pro-0813
Pilihan terbaik: harder coding, agents, reasoning, and constraint-heavy deliverables.
DeepSeek V4 Flash
Version: Flash-0731
Pilihan terbaik: high-throughput production, routine coding, retrieval, and cost-sensitive workloads.
The names can be confusing because the V4 technical report describes Preview variants, while the live API uses dated production versions. Throughout this review, “Pro” means Pro-0813 and “Flash” means Flash-0731 unless a sentence explicitly says “V4 Preview.”
What changed in DeepSeek V4 Pro?
The clearest changes are scale, context capacity, agent-oriented interfaces, and a two-tier product strategy. DeepSeek’s official Pro model card identifies a mixture-of-experts model with 1.6 trillion total parameters and 49 billion active parameters, released under MIT. The accompanying V4 materials describe new attention and routing work aimed at making million-token processing practical.
- Two current tiers: Pro for quality-sensitive work and Flash for throughput and cost.
- Much longer working memory: a listed 1M-token context window for both models.
- Large generation ceiling: up to 384K output tokens in the official specification.
- Agent interfaces: tool calls, JSON output, Responses API, and Anthropic-compatible access.
- Open weights: official V4 model cards and files are available, though serious local deployment remains a major infrastructure project.
That combination makes V4 relevant to repository-scale coding, long research packets, and multi-step agent traces. It also changes the competitive frame: a useful evaluation now needs to look beyond chat quality and ask how the model behaves under tools, strict formats, and long inputs. Our DeepSeek vs ChatGPT comparison gives more background on where those ecosystems differ.
DeepSeek V4 Pro vs Flash: the practical difference
Both versions expose the same headline context and output limits, so this is not a “full model versus short-context model” choice. It is mainly a quality, price, and capacity-planning decision. Flash’s official concurrency ceiling is five times Pro’s, while Pro’s cache-miss input and output tokens cost a little over three times as much.
Pro vs Flash at a glance
Official specifications and prices checked August 13, 2026; hands-on observations come from one run per task and are not official benchmarks.
Choose Flash when…
You need inexpensive scale, routine code fixes, extraction, classification, or many parallel requests-and you can validate high-stakes arithmetic and format constraints.
Choose Pro when…
The output combines code, policy, finance, or many exact constraints, and the extra price is smaller than the cost of a subtle failure.
Hands-on tests: coding, retrieval, and constraint following
We tested both API model IDs through the same Broly Anywhere OpenAI-compatible endpoint on August 13, 2026. Temperature was 0. Each scored result is one run per model and task. Wall-clock time includes gateway and upstream delay, so it is useful as an observation—not a stable latency ranking.
Hands-on scorecard
Pro and Flash both passed every fixed test.
Both answers and all required record citations were correct.
Pro met every rule; Flash missed arithmetic and length constraints.
Test 1: agentic Python debugging
The harness gave each model four tools—list files, read a file, write a file, and run tests—inside a small three-file Python pricing project. This is closer to an agent workflow than asking for a code snippet in chat.
Debug, patch, and verify a Python pricing project
Both models followed the same six-call path, edited only the implementation, and produced 8 passed. Flash completed this run in 47.378 seconds versus Pro’s 95.228 seconds and used fewer returned tokens. There was no quality winner here. For another code-focused perspective, see our DeepSeek vs ChatGPT Python coding test.
Test 2: approximately 20.7K-token dossier retrieval
We attached a dossier containing more than 180 records, repeated distractors, stale drafts, and ten scattered facts. The model had to answer in an exact ten-line format and cite a record for every answer.
Answer a noisy dossier with exact citations
Both models scored 22/22: ten correct answers, ten correct citations, and two format points. Pro completed this run in 10.396 seconds and Flash in 21.737 seconds. The actual prompt usage was about 20.7K tokens. That makes it a useful mid-length retrieval test, but it does tidak validate the full 1M-token window.
Test 3: a strict launch decision memo
This task combined a 120–180 word summary, an exact four-row table, fixed risk and unknown counts, a short decision, valid JSON, and a first-year cost calculation from finance, legal, engineering, sales, support, and security notes.
Produce a launch memo with arithmetic and strict output rules
At a 2,048 completion-token limit, both models used the available completion budget on returned reasoning and produced no visible answer. We preserved those records, then reran the unchanged prompt with an 8,192-token limit. Pro scored 12/12. Flash scored 8/12 because it reported $217,100 instead of $216,900, carried the $200 error into the variance, and wrote a 94-word summary instead of the required 120–180 words.
The lesson is specific, not universal: Flash was already excellent at the coding and retrieval tasks, while Pro was safer on the densest combination of arithmetic, structure, and policy constraints. If your workflow relies on an agent shell, our OpenClaw vs Claude Code vs OpenCode comparison helps match the model to the surrounding tool.
DeepSeek V4 benchmarks: what the official data proves
The safest benchmark source is the DeepSeek V4 technical report, but its tables and charts describe V4 Preview models. They are useful for understanding the architecture’s intended capability and test setup; they are not independent scores for the dated Pro-0813 API release.
Preview scores are not Pro-0813 scores
Architecture and benchmark evidence from DeepSeek.
Dated production versions listed in API docs.
Preview scores cannot be relabeled as Pro-0813 scores.
The July 31 Flash update also publishes agent results and names the harness, settings, and Codex support. Those details matter because cross-vendor leaderboards can change with scaffolding, reasoning budget, tool definitions, and retry rules. A result without its harness is not enough to declare an unconditional winner.
For the same reason, our three tasks sit beside the official evidence instead of replacing it. They tell you what happened under one disclosed setup. If you are comparing agent products rather than just model APIs, our Codex vs Claude Code guide explains why the surrounding workflow can change the user experience.
DeepSeek V4 API pricing
DeepSeek’s API pricing is unusually aggressive at launch. The table below reproduces the official per-million-token figures checked on August 13, 2026. The official page also warns that prices will rise significantly in the future, so treat these as dated launch-era numbers.
Price per 1M tokens
A simple uncached workload with 10 million input tokens and 2 million output tokens would be $1.96 on Flash and $6.09 on Pro at these rates. That illustration excludes cache hits, provider markups, retries, and any later price change. Teams comparing vendors should use one token mix and billing basis; our Claude Opus 4.6 API pricing guide shows why input, output, and caching need separate rows.
What the 1M context window means—and where the limits are
A 1M-token context window is capacity, not a promise of perfect recall at every position. It gives the API room for very large repositories, document collections, research packets, or agent histories. The quality question is whether the model can retrieve the right detail after relevant evidence is buried among hundreds of thousands of distracting tokens.
DeepSeek’s Preview report includes a multi-round coreference resolution (MRCR) chart from 8K to 1M input tokens. The curve stays strong at shorter lengths but declines after 128K. That is normal evidence of a hard long-context problem, and it is why “supports 1M” should never be translated into “lossless at 1M.”
The API surface has separate limitations. DeepSeek’s Responses API guide describes a stateless implementation: features such as previous_response_id, conversations, store, and background are unsupported. Image and file inputs are also unsupported, so you need to extract text or build the state layer outside the model.
The Anthropic-compatible API table likewise marks image and document message variants as not supported. Compatibility therefore means familiar request shapes and tooling—not feature-for-feature parity with every Anthropic model or client.
How to try DeepSeek V4 Pro and Flash
You have three practical routes. Choose based on whether you want a quick chat, production API control, or a multi-model workspace.
Choose your access path
DeepSeek Web/App
Best for trying the model through DeepSeek’s own consumer interface.
API resmi
Best for token billing, tools, JSON, agent frameworks, and production control.
GlobalGPT
Best for opening Pro or Flash in a broader multi-model workspace without building a client first.
Route 1: official DeepSeek chat
Start at DeepSeek’s official site if you simply want to see the current consumer experience. Consumer access and API billing are separate; an available chat interface does not mean the API is free.
Route 2: official API and coding agents
Gunakan deepseek-v4-pro atau deepseek-v4-flash with the documented OpenAI-compatible interfaces. Both support thinking and non-thinking modes, JSON output, tool calls, Responses API, and Anthropic-compatible access. FIM applies only in non-thinking mode.
DeepSeek also publishes an official Codex integration guide. If you are deciding between agent shells first, compare Claude Code pricing and usage limits before assuming model token cost is the whole bill.
Claude Code users can follow the same endpoint-and-model logic described in our guide to configuring a model in Claude Code, while checking DeepSeek’s live compatibility table for unsupported fields.
Route 3: GlobalGPT
GlobalGPT has dedicated entries for both models. The verified conversion route is DeepSeek V4 Pro on GlobalGPT. A Flash product page also exists, but we did not find a verified Flash-specific campaign parameter, so we are not inventing one.
For terminal workflows, the GlobalGPT CLI in Claude Code setup explains how a multi-model platform can fit into an existing coding routine. It does not replace native repository tools; it changes how you reach and compare models.
Which DeepSeek V4 model should you choose?
Match the model to the cost of failure
Flash
Pick it for high-volume chat, extraction, routine code, retrieval, and agents with strong validation. It passed two of our three tasks perfectly at the lowest official price.
Pro
Pick it when strict arithmetic, formatting, policy, and multi-domain reasoning meet in one deliverable. It was the only model to score 12/12 on our memo.
GlobalGPT
Pick the platform route when you want to try the model or compare alternatives without first writing an API client.
For many teams, Flash is the smartest first deployment: it is much cheaper, supports the same stated context and output limits, and handled our coding and 20.7K retrieval tests without a quality miss. Add deterministic checks for numbers, schema, and required sections.
Move to Pro when review time or failure cost dominates token savings. The price gap is real, but so is the value of getting a dense compliance or launch memo right on the first pass. If your shortlist includes other assistants, the broader DeepSeek vs ChatGPT choice guide can help separate model quality from product ecosystem.
Keputusan akhir
DeepSeek V4 Flash is the value winner; DeepSeek V4 Pro is the assurance upgrade. Both models were genuinely capable in our tests, and neither needs a 1M-token stunt to be useful. Flash matched Pro on agentic coding and mid-length retrieval. Pro separated itself when the task combined arithmetic, legal status, exact structure, JSON consistency, and a tight word-count rule.
The biggest reasons to be careful are not hidden: current prices are signposted to rise, the 1M window does not guarantee lossless recall, image and document inputs remain unsupported in the documented compatibility layers, and Preview benchmark charts are not Pro-0813 benchmark tables.
Start with Flash for validated, high-volume work. Pay for Pro when mistakes are more expensive than tokens. And if you want to compare the experience before integrating an API, try DeepSeek V4 Pro on GlobalGPT.
DeepSeek V4 FAQ
Is DeepSeek V4 officially released?
Yes. On August 13, 2026, DeepSeek’s official API documentation listed DeepSeek-V4-Pro-0813 and DeepSeek-V4-Flash-0731 as the current V4 versions.
What is the difference between DeepSeek V4 Pro and V4 Flash?
Both list 1M context and 384K maximum output. Flash is cheaper and has a higher concurrency ceiling; Pro is the higher-quality tier and performed better on our strict multi-constraint memo.
How much does the DeepSeek V4 API cost?
On August 13, 2026, Flash cost $0.0028 cache-hit input, $0.14 cache-miss input, and $0.28 output per million tokens. Pro cost $0.003625, $0.435, and $0.87. DeepSeek warns that prices will rise.
Does DeepSeek V4 really support a 1M-token context window?
Yes, both current versions list a 1M-token context window. That is a capacity limit, not a guarantee of perfect retrieval: DeepSeek’s Preview report shows MRCR performance declining at extreme lengths.
Can I use DeepSeek V4 with Codex or Claude Code?
Yes. DeepSeek documents a Codex setup through its Responses API and offers an Anthropic-compatible interface for tools such as Claude Code, although not every Anthropic field or feature is supported.
Is DeepSeek V4 open source or open weight?
DeepSeek publishes official V4 weights and lists an MIT license on the Pro model card. Running the full Pro model locally still requires serious storage, memory, and inference infrastructure.
Can I use DeepSeek V4 for free?
DeepSeek may provide consumer chat access, but its official API is billed by tokens. Any third-party platform access depends on that platform’s current plan, so do not treat web access and API pricing as the same thing.
Can I use DeepSeek V4 on GlobalGPT?
Yes. GlobalGPT has dedicated DeepSeek V4 Pro and Flash entries. The verified tracked Pro route opens the model directly, while the general tracked GlobalGPT route can be used when a Flash-specific campaign link is unavailable.
