685B Brain. 52B Active.

DeepSeek V4 Pro — Mixture-of-Experts routes each request to specialized sub-networks. Frontier knowledge at mid-size cost. On Yesmind.

Mixture-of-Experts — Active Routing

Code
Gen

Math
Logic

Bio
Med

Legal
Doc

NLP
Core

Vision
Parse

Chem
Mol

Reason
Chain

Trans
late

Data
Struct

Query: "Optimize this SQL query" → 4 of 10 expert groups active · 52B params used

685B

Total Parameters

52B

Active per Query

92%

Compute Savings

80.6%

SWE-bench Verified

$0.435

Per 1M input tokens

MIT

Open Source License

Context Window

What Is DeepSeek V4 Pro?

DeepSeek V4 Pro is a frontier-class large language model that shatters the assumption that cutting-edge AI must be expensive and closed. Released in 2026 under an MIT license, it achieves 80.6% on SWE-bench Verified — matching Claude Opus 4.8 and approaching GPT-5.5 — while costing just $0.435 per million input tokens. That is roughly 30 times cheaper than Claude and 11 times cheaper than GPT-5.5 at equivalent output quality.

The model is built on a Mixture of Experts (MoE) architecture with 1.6 trillion total parameters but only 49 billion active per token. This design gives DeepSeek V4 Pro the breadth of knowledge of a massive model while keeping inference costs close to a mid-size dense model. The result: frontier intelligence without frontier pricing.

DeepSeek V4 Pro supports a 1 million token context window, three adaptive reasoning modes, full OpenAI-compatible API, and agentic function-calling workflows. It was trained on Huawei Ascend AI hardware — the first frontier model built entirely outside the Nvidia ecosystem — demonstrating that supply-chain independence and research excellence can coexist.

On Yesmind, you access DeepSeek V4 Pro directly through our unified interface — no separate API keys, no SDK migration, no extra setup. One subscription, every frontier model.

80.6 %

SWE-bench Verified

$0.43 /M

Input Tokens

1.6 T

Total Parameters

1 M

Token Context

Extreme Cost Efficiency

$0.435/M input — 11× cheaper than GPT-5.5, 29× cheaper than Claude Opus. Cache hits at $0.004/M. A 10M token/day workload: $261/mo vs $7,500 on Claude.

MIT License — True Open Source

Self-host, fine-tune, resell — zero restrictions. Unlike Llama's license: no user-count limits, no naming requirements, no carve-outs. Weights on HuggingFace.

Three Adaptive Reasoning Modes

Standard: fast, no chain-of-thought. Thinking: balanced reasoning for code and analysis. Deep Think: max depth for proofs and architecture. One endpoint, three profiles.

1 Million Token Context Window

Full codebases, legal contracts, research libraries — in one pass. Hybrid attention reduces FLOPs to 27% of V3.2 at 1M context. Long-context that's economically viable.

MoE Architecture — Massive Knowledge, Mid-Size Cost

685B total parameters, 52B active per token. Router selects specialized expert groups per query — frontier knowledge at mid-size compute cost. 92% savings vs dense equivalent.

OpenAI-Compatible API — Zero Migration

Change base_url and model name — that's it. All OpenAI SDKs work. Also supports Anthropic format. Zero lock-in, switch back anytime.

DeepSeek V4 Pro vs Top AI Models

How DeepSeek V4 Pro compares to GPT-5.5, Claude Opus 4.8, and Llama 4 Maverick on benchmarks, pricing, and open-source access.

Features	DeepSeek V4 Pro DeepSeek AI	GPT-5.5 OpenAI	Claude Opus 4.8 Anthropic	Llama 4 Maverick Meta
SWE-bench Verified	80.6%	~80%	80.6%	~65%
LiveCodeBench	93.5%	~90%	~88%	TBD
Codeforces Rating	3,206	TBD	TBD	TBD
Context Window	1M tokens	1M tokens	200K+	1M tokens
Input Price	$0.435/M	$5/M	$15/M	Free (open)
Output Price	$0.87/M	$30/M	$25/M	Free (open)
Open Source
Self-Hostable
Reasoning Modes	3 (Non-Think / High / Max)	Standard + Thinking	Standard + Extended	Standard
Training Hardware	Huawei Ascend	Nvidia	Nvidia	Nvidia

How DeepSeek V4 Pro Works

MoE Routing — frontier knowledge at mid-size inference cost

Your Prompt Enters the Model

Every request is tokenized and passed to DeepSeek V4 Pro's routing layer. The MoE gating network instantly evaluates which expert sub-networks are most relevant to your specific input — code, math, language, or reasoning — before a single compute-heavy operation begins.

49B Active Parameters Handle Inference

From the 1.6 trillion total parameters, only ~49 billion activate per token. The selected experts process your request with the depth of a massive model, but at the compute cost of a mid-size dense model. This is why DeepSeek V4 Pro delivers frontier output quality at $0.435/M rather than $5–$15/M.

Output Delivered with Optional Deep Reasoning

The response is assembled from expert outputs and returned through your chosen reasoning mode. Non-Think for instant answers. Think High for step-by-step chains. Think Max for maximum-depth problem solving. One model, three cost-quality operating points — you control the tradeoff per request.

The Open Source Advantage

Why MIT license changes everything for AI deployment

MIT License — No Commercial Restrictions

The MIT license is the most permissive major open-source license. Use DeepSeek V4 Pro in commercial products, SaaS applications, and enterprise software with zero royalty obligations. No user-count limits. No derivative naming requirements. No field-of-use restrictions. Unlike Meta's Llama license, there are no carve-outs that exclude large deployments. Build products, sell them, and retain all revenue.

Self-Host on Your Own Infrastructure

Download the model weights from HuggingFace and run DeepSeek V4 Pro entirely on your own hardware — cloud, on-premise, or air-gapped. No external API calls, no per-token metering, no vendor lock-in. You control uptime, latency, and scaling decisions. Organizations that have invested in GPU infrastructure can now run a genuine frontier model at marginal cost.

Fine-Tune for Your Domain

Full fine-tuning access means you can adapt DeepSeek V4 Pro to your specific data, terminology, and tasks. Train on proprietary codebases to improve technical accuracy. Fine-tune on company documents to match your communication style. Build domain-specific variants without sharing your training data with any third party. The resulting fine-tuned model is yours — fully owned and fully deployable under MIT.

Data Sovereignty — Prompts Stay Yours

When you self-host, your prompts, context, and completions never leave your infrastructure. No cloud provider sees your queries. No vendor retains conversation history. No training data policies apply. For finance, healthcare, legal, and government use cases where data residency and confidentiality are regulatory requirements, self-hosted DeepSeek V4 Pro is the first frontier-class model that makes compliance genuinely achievable.

What Makes DeepSeek V4 Pro Different

30× Cheaper Than Comparable Closed Models

At $0.435/M input tokens, DeepSeek V4 Pro costs 11× less than GPT-5.5 ($5/M) and 34× less than Claude Opus 4.8 ($15/M). With prompt caching at $0.003625/M, repeated-context workloads approach near-zero cost. A production pipeline processing 10M output tokens daily costs $261/month on DeepSeek versus $7,500/month on Claude. The same budget that covered one use case can now fund thirty.

MIT License — The Only Truly Free Frontier Model

Open-source AI has existed for years, but no open-weight model has matched closed-model frontier benchmarks until DeepSeek V4 Pro. The MIT license removes every commercial restriction: build products, deploy at any scale, fine-tune freely, and keep your derivatives. It is the most permissive license attached to the most capable open-weight model ever released.

1.6T MoE — Deep Without Expensive

1.6 trillion parameters provide encyclopedic world knowledge across code, science, math, and language. Only 49 billion activate per inference — matching a mid-size dense model's compute footprint. Manifold-constrained hyper-connections and hybrid KV cache architecture ensure these 49B active parameters punch well above their weight. You get frontier output quality without frontier inference cost.

Trained on Huawei Ascend — First Non-Nvidia Frontier

DeepSeek V4 Pro was trained exclusively on Huawei Ascend 950 and Cambricon AI accelerators. It is the first verified frontier-class model trained without a single Nvidia GPU. This demonstrates that AI supply chain independence is no longer theoretical — a team operating outside the Nvidia ecosystem has matched or exceeded models trained on the world's most expensive GPU clusters.

Benchmark Performance

DeepSeek V4 Pro across coding, reasoning, math, and science evaluations

 SWE-bench Verified (Coding) 80.6% 
Ties Claude Opus 4.8 for top open-weight score. Outperforms every prior open-source model on real-world software engineering tasks.

 LiveCodeBench (Competitive Programming) 93.5% 
Beats all closed models including GPT-5.5 (~90%) and Claude Opus 4.8 (~88%). Best-in-class result on live coding challenges.

Codeforces Rating 3,206

Grandmaster-level competitive programming rating. No other open-weight model comes close on this rigorous algorithmic benchmark.

MMLU-Pro (Multidisciplinary Knowledge) Top Tier

Frontier performance across STEM, humanities, and professional domains. Matches closed-model leaders on knowledge-intensive tasks.

GPQA Diamond (PhD-Level Science) Top 3

Expert-level performance on graduate science questions requiring deep reasoning across biology, chemistry, and physics.

Terminal-Bench 2.0 (Agentic) 67.9%

Outperforms Claude Opus 4.8 (65.4%) on autonomous multi-step terminal tasks. Strong agentic intelligence score of 63.8.

Who Uses DeepSeek V4 Pro — and How

Autonomous Coding Agents

80.6% SWE-Bench, 67.9% Terminal-Bench. Build CI/CD automation and multi-step debugging agents at scale. Same task that costs $1.40 on GPT-5.5 runs for $0.12 here.

Prompt

“Refactor this module: extract DB calls into repository layer, add type hints, write pytest tests, generate migration guide”

Document Analysis at Scale

847 contracts in 23 minutes for $14.80. 1M token context — entire document libraries in one pass. No chunking, no RAG complexity. Parse, classify, structure — all in one pipeline.

Prompt

“Analyze this 600-page merger agreement: extract warranties, flag non-standard clauses, summarize risks”

Self-Hosted Enterprise AI

MIT license — deploy on your infra, prompts never leave your network. GDPR, HIPAA, SOC 2 compliant by design. Fine-tune on proprietary data. Or use via Yesmind API — zero setup.

Prompt

“Summarize this internal earnings call and flag statements requiring legal review before 10-Q filing”

Research & Scientific Analysis

Deep Think mode for PhD-level reasoning. GPQA Diamond top tier, AIME '26: 83%, Codeforces Grandmaster (3,206). Multi-step derivations at $0.42 per analysis.

Prompt

“Analyze CRISPR off-target effects across 2025-2026 literature, cross-reference datasets, flag contradictions”

3 Reasoning Modes — One Model

Tune cost, speed, and depth per request without switching models

Non-Think

Direct, instant answers for straightforward queries. No chain-of-thought processing, minimal latency, lowest cost per token. Use for classification, summarization, translation, simple Q&A, and high-volume tasks where speed and economy matter more than depth.

Think High

Structured step-by-step reasoning for multi-part problems. The model explicitly plans before answering — breaking complex tasks into sub-problems, verifying intermediate steps, and building coherent long-form solutions. Best for coding tasks, analytical writing, data analysis, and technical explanations.

Think Max

Maximum reasoning depth for the hardest problems. Extended multi-step chains, recursive verification, exhaustive hypothesis testing. Codeforces 3,206 and GPQA Diamond performance operate in this mode. Use for competitive programming, PhD-level research questions, advanced mathematics, and architectural design decisions where accuracy outweighs speed.

Frequently Asked Questions

DeepSeek V4 Pro is an open-source large language model developed by DeepSeek AI, released in 2026 under the MIT license. It uses a Mixture of Experts architecture with 1.6 trillion total parameters and 49 billion active per token, achieving 80.6% on SWE-bench Verified — matching the best closed-source models — at $0.435 per million input tokens. It supports a 1M token context window, three reasoning modes, and a fully OpenAI-compatible API.

DeepSeek V4 Pro costs $0.435/M input tokens and $0.87/M output tokens via API. Prompt cache hits cost $0.003625/M — essentially free for repeated context. This makes it approximately 11× cheaper than GPT-5.5 ($5/M input) and 34× cheaper than Claude Opus 4.8 ($15/M input) for equivalent output quality. Self-hosting incurs zero per-token costs beyond your own infrastructure.

Yes. DeepSeek V4 Pro is released under the MIT license — the most permissive major open-source license. Commercial use, fine-tuning, self-hosting, and creating derivative models are all permitted with no restrictions, no royalties, and no user-count limits. Model weights are publicly available on HuggingFace. This is a stricter open-source commitment than Meta's Llama license, which includes commercial-use caveats.

Mixture of Experts (MoE) means the model has 1.6 trillion total parameters across many specialized sub-networks called 'experts.' For each token processed, a routing layer selects only ~49 billion parameters to activate — the experts most relevant to that specific input. The result: knowledge breadth of a 1.6T-parameter model at the inference cost of a ~49B-parameter model. This architectural efficiency is why DeepSeek V4 Pro achieves frontier benchmarks at mid-size model pricing.

Non-Think delivers fast, direct answers without chain-of-thought processing — ideal for simple queries and high-volume workloads. Think High enables structured step-by-step reasoning for multi-part problems, debugging, and analysis. Think Max applies maximum reasoning depth for the hardest challenges — competitive programming, PhD-level research, complex mathematics. All three modes are available on the same model endpoint; you select the operating point per request.

Yes. The MIT license grants full rights to deploy DeepSeek V4 Pro on your own hardware — cloud, on-premise, or air-gapped. Model weights are available on HuggingFace. You will need significant GPU capacity given the model's size; however, the MoE architecture's 49B active-parameter footprint means per-token inference cost is lower than a dense model of equivalent parameter count. Self-hosting is the recommended path for organizations with data sovereignty requirements.

On SWE-bench Verified, both models score approximately 80% — statistical parity for real-world coding tasks. DeepSeek V4 Pro leads on LiveCodeBench (93.5% vs ~90%) and competitive programming. GPT-5.5 leads on some agentic benchmarks like Terminal-Bench (82.7% vs 67.9%). The critical difference is cost and openness: DeepSeek V4 Pro costs 11× less per token, supports self-hosting, and is fully MIT-licensed — GPT-5.5 is closed-source with no self-host option.

DeepSeek V4 Pro supports a 1 million token context window — equivalent to roughly 750,000 words or several full-length novels. Its hybrid Compressed Sparse Attention (CSA) plus Hybrid Chunk Attention (HCA) architecture processes 1M contexts at only 27% of the FLOPs required by V3.2. This makes long-context processing economically viable at scale, unlike dense models where 1M context multiplies compute and cost proportionally.

Yes. DeepSeek V4 Pro uses an OpenAI-compatible API format. Migrating from GPT models requires changing one parameter: the base_url. No new SDK installation, no prompt rewrites, no workflow restructuring. Anthropic-format requests are also supported for teams migrating from Claude. Existing LangChain, LlamaIndex, and direct API integrations work without modification.

DeepSeek V4 Pro was trained exclusively on Huawei Ascend 950 and Cambricon AI accelerators — making it the first verified frontier-class language model trained without Nvidia GPUs. This has significant implications for AI supply chain resilience. It demonstrates that frontier model training is no longer exclusively dependent on Nvidia's CUDA ecosystem, and validates Huawei's AI hardware as a viable alternative for large-scale model development.