DeepSeek vs ChatGPT

Executive summary

This report compares DeepSeek (web/app + Open Platform API + open-weights releases) with ChatGPT (consumer and business product) and the underlying OpenAI platform (API, tools, governance controls) as of February 17, 2026. Sources prioritize official product pages, developer docs, pricing pages, and primary benchmark disclosures from 2024–2026; where data is missing or ambiguous, it is explicitly labeled “unspecified.” 

At a high level, the choice most often comes down to (1) governance/compliance and integrated product features (where ChatGPT Business/Enterprise tends to lead) versus (2) cost control and deployability (where DeepSeek’s low API pricing and open-weight availability can be decisive). 

Key findings:

• Product maturity and “batteries included” tooling: ChatGPT’s paid tiers include a broad suite of multimodal and productivity features—voice (including voice-with-video), vision, image generation, “deep research,” collaborative projects, and enterprise admin features like SAML SSO, admin console, and SOC 2 / ISO certifications on Business/Enterprise. 
• API cost structure: DeepSeek’s current Open Platform token rates are extremely low—especially when cache hits apply—compared with OpenAI’s flagship standard-tier pricing. DeepSeek also exposes context caching details (hit/miss tokens) in usage metadata, which can materially shift unit economics for repeated prefixes. 
• Data residency and regulatory posture risk: DeepSeek’s privacy policy states personal information is processed and stored in the People’s Republic of China, and retained “as long as necessary,” including (for certain data) “as long as you have an account,” and describes use for foundation model training/optimization by its corporate group. Separately, Reuters reports multiple governments restricting DeepSeek on official devices/systems over privacy/security concerns. 
• Benchmarks: OpenAI’s GPT‑5.2 launch materials publish strong scores on SWE-bench Verified, GPQA Diamond, and other evals; DeepSeek’s research paper reports strong MMLU-family scores and competitive performance on SWE Verified and GPQA Diamond, but typically well below GPT‑5.2 on those specific figures as published. Direct comparisons require caution due to differences in eval setup, versions, and “with tools vs no tools” conditions. 
• Hallucination rates (two lenses):
  • OpenAI’s GPT‑5.2 system card reports factual error/hallucination rates on prompts representative of real ChatGPT production conversations (browse-on and browse-off), showing improvements for GPT‑5.2 Thinking vs GPT‑5.1 Thinking. 
  • Vectara’s hallucination leaderboard (summarization-focused) includes both DeepSeek models and OpenAI GPT‑5.2 variants, enabling an apples-to-apples comparison within that specific summarization evaluation. 

Practical recommendation shortcut:

• Choose ChatGPT Business/Enterprise when you need centralized enterprise controls, compliance posture, and integrated multimodal workflows in one workspace. 
• Choose DeepSeek when your top constraint is cost-per-token and/or you need open weights for self-hosting, offline use, or extensive customization, and you can accept (or mitigate) data residency/compliance concerns by self-hosting or strict data handling. 

Scope and methodology

This comparison covers:

• DeepSeek’s product surface: web/app entry points and Open Platform API. 
• ChatGPT plans (Free/Go/Plus/Pro/Business/Enterprise) and adjacent OpenAI platform capabilities (API endpoints, tools, SDKs, processing tiers). 
• Feature dimensions requested by the user: model types, multimodality, context, customization, retrieval/knowledge bases, tool use, plugins/extensions, API/SDKs, latency/throughput, security/privacy/data retention/compliance, enterprise admin controls, analytics. 

Notes on limitations:

• Some plan pages render prices dynamically; when numeric values were not reliably present in the HTML view, official help-center or launch posts were used (e.g., Go, Plus, Pro, Business). 
• “Latency” and “throughput” are often not published as fixed guarantees. This report relies on published rate-limit behavior and service-tier descriptions where possible, and marks exact latency/throughput as unspecified if not stated. 

Product and ecosystem overview

DeepSeek overview

DeepSeek’s landing page positions its current flagship “DeepSeek‑V3.2” as “reasoning-first models built for agents,” available via web, app, and API, with free access promoted for end users. 

For developers, DeepSeek’s Open Platform provides an API at https://api.deepseek.com with two primary model identifiers:

• deepseek-chat (non-thinking mode) and
• deepseek-reasoner (thinking mode)

These correspond to “DeepSeek‑V3.2” with a 128K context limit in the API (and DeepSeek notes this differs from the app/web version). 

A defining differentiator of the DeepSeek ecosystem is the availability of open-weight model releases. The DeepSeek-R1 repository states that both the code and model weights are under the MIT License, explicitly allowing commercial use and derivative works (including distillation). 
DeepSeek’s V3 lineage is more nuanced: the DeepSeek-V3 repository describes MIT licensing for code but indicates the Base/Chat model use is “subject to the Model License,” while at least one prominent checkpoint (DeepSeek‑V3‑0324) is explicitly MIT-licensed for both repository and weights on its model card. 

ChatGPT and OpenAI platform overview

ChatGPT is a multi-tier product with personal plans (Free/Go/Plus/Pro) and organizational plans (Business/Enterprise). The plan comparison page enumerates capabilities across models, features, privacy, and security/administration. 

As of early 2026, the ChatGPT plan page indicates that ChatGPT’s core model access is centered on GPT‑5.2 variants (Instant/Thinking/Pro) with plan-dependent access levels (e.g., GPT‑5.2 Thinking is not available on Free/Go, but is available on Plus and above). 

For developers, OpenAI’s platform has shifted toward the Responses API (POST /v1/responses) as the recommended unified generation endpoint, with migration guidance from Chat Completions (POST /v1/chat/completions). 

OpenAI’s platform further differentiates itself via built-in tools such as web search and file search (vector stores), and via a broad set of official SDKs/libraries and documentation. 

Feature and platform comparison

Side-by-side feature comparison

The table below focuses on current, documented capabilities (not aspirational roadmaps). If a feature is implementable only by “building it yourself” (e.g., via tool calls), it is described explicitly that way.

Security, privacy, and compliance implications

DeepSeek’s privacy policy explicitly places storage and processing in China and allows use of data for model training/optimization within its corporate group, which is particularly consequential for regulated industries and for organizations needing EU/US data residency assurances. In addition, Reuters reports a widening cross-government pattern of restrictions on DeepSeek in official contexts, reflecting elevated perceived risk. 

ChatGPT’s plan grid draws a bright line between personal tiers (opt-out available) and Business/Enterprise (no training on content), and enumerates compliance certifications and a suite of admin controls in those tiers.  For enterprises evaluating vendor risk, these published controls reduce the amount of bespoke security engineering required to reach common procurement baselines (SSO, centralized admin, usage analytics, certifications). 

Pricing and cost modeling

Subscription pricing

Official pricing for some ChatGPT plans is best sourced from OpenAI launch/help pages due to dynamic rendering on plan pages.

API pricing and processing tiers

DeepSeek’s API pricing is published per 1M tokens, with explicit cache-hit and cache-miss distinctions.  OpenAI’s pricing is similarly per 1M tokens, but additionally segments Flex / Standard / Priority as processing tiers with different price points and implied latency tradeoffs. 

Per-token pricing snapshot (flagship text)

Interpretation cautions:

• Output tokens dominate cost for long generations; the same “1M in / 1M out” workload differs by ~22× between DeepSeek (cache miss) and OpenAI Standard for GPT‑5.2 (based on published rates), before considering caching strategies or batch usage. 
• DeepSeek’s caching is prefix-deduplication oriented and can sharply reduce input costs for repeated system prompts and shared context; DeepSeek reports cache hit/miss tokens in the response usage object. 
• OpenAI explicitly frames “Priority processing” as faster processing and “Flex” as lower prices with higher latency, and calls out Batch for time-insensitive workloads. 

Benchmarks and observed performance

Accuracy benchmarks

Published benchmark highlights (selected)

The most comparable cross-vendor numbers come from (a) OpenAI’s GPT‑5.2 launch post and (b) DeepSeek’s R1 paper table of experimental results. Note that eval protocols can differ (tool availability, scoring, dataset versions, and “pass@1” vs other metrics). 

A practical reading of these disclosures is that OpenAI’s GPT‑5.2 is positioned as the stronger “top-end” performer on the headline coding/science benchmarks it highlights publicly, while DeepSeek-R1 remains competitive for many academic benchmarks and offers a very different cost/deployment profile. 

Hallucination rates

Because “hallucination rate” depends heavily on task design, two complementary sources are used:

OpenAI’s production-traffic-style factuality measurement (GPT‑5.2 system card)

OpenAI reports hallucination rates on prompts “representative of real ChatGPT production conversations,” with separate browse-on and browse-off settings. In the browse-enabled chart for GPT‑5.2 Thinking, the figure shows 0.8% incorrect claims and 5.8% of responses with ≥1 major incorrect claim; browse-disabled shows 3.1% incorrect claims and 10.9% of responses with ≥1 major incorrect claim. 

This is a strong signal for organizations whose primary risk is day-to-day factuality in conversational use, but it is not directly comparable to summarization-only evals. 

Vectara’s summarization hallucination leaderboard (cross-model, same eval)

Vectara’s hallucination leaderboard evaluates how often models introduce hallucinations when summarizing documents, using its HHEM model, and explicitly lists a “last updated” timestamp (Feb 5, 2026), dataset characteristics, and included models—covering both DeepSeek and OpenAI GPT‑5.2 variants. 

Selected entries from that leaderboard:

• DeepSeek‑V3.2‑Exp: 5.3% hallucination rate
• DeepSeek‑V3.2: 6.3%
• DeepSeek‑R1: 11.3%
• OpenAI gpt‑5.2‑low (Dec 11, 2025): 8.4%
• OpenAI gpt‑5.2‑high (Dec 11, 2025): 10.8% 

Vectara also published an analysis claiming DeepSeek‑R1 hallucinates more than DeepSeek‑V3 under their measurement framework (with a much higher rate for R1 than V3 in that specific summarization eval). 

Interpretation: within summarization tasks, DeepSeek V3.x variants can look comparatively strong, while DeepSeek R1’s reasoning orientation can correlate with higher hallucination rates in that particular evaluation suite; GPT‑5.2 variants fall in the same general band on that leaderboard depending on the specific GPT‑5.2 configuration. 

Latency and throughput

Neither vendor publishes a universal “p95 latency” guarantee for all customers in the sources used, so the comparison focuses on operational controls each provides:

• DeepSeek states it does not constrain user rate limits but warns of delays under high traffic; it may stream keep-alives and can close a connection if inference hasn’t started after 10 minutes. 
• OpenAI provides explicit rate-limit framework documentation and publishes multiple processing tiers (Flex / Standard / Priority) that trade off price and speed, plus Batch for non-time-sensitive high-volume runs. 

Use cases, migration, and decision framework

Typical and niche use cases

DeepSeek tends to fit best when:

• You need very low token costs at scale, especially for high-output workloads, and can exploit prefix caching. 
• You need open weights (e.g., for on-premise deployment, offline workflows, or heavy customization) and can choose checkpoints whose licenses permit your intended use (e.g., DeepSeek‑R1 MIT; DeepSeek‑V3‑0324 MIT). 
• You are building your own agent platform and want a tool-calling-capable model, accepting you must host the tools/services yourself (DeepSeek tool calls are “call external tools”—the model doesn’t execute functions on its own). 

ChatGPT (and the OpenAI platform) tends to fit best when:

• You want an integrated knowledge-work environment: voice, vision, images, deep research, projects, and collaborative workflows backed by higher-tier models. 
• You need Business/Enterprise governance: SAML SSO, admin console, admin roles, analytics/management for GPTs, SOC 2 Type 2 and ISO certifications, and “content not used for training” assurances. 
• You are building agentic applications and want built-in tools such as web search and file search integrated into the same API object model (Responses API). 

Migration and integration considerations

API integration: switching between OpenAI and DeepSeek

DeepSeek’s docs show using the OpenAI SDK with a different base_url, implying easiest migration paths for apps built around OpenAI-compatible chat-completions tooling. 

Key friction points when migrating:

• Endpoints and “agentic primitives”: OpenAI’s recommended direction is /v1/responses with built-in tools; DeepSeek’s public docs emphasize chat completions plus tool call schemas you execute yourself. If your application depends on built-in web/file search, migration is not a simple base URL swap—you must re-implement those tool layers (or remove them). 
• Context window differences: DeepSeek API documents 128K context for V3.2; ChatGPT plan limits vary by subscription tier. If you are migrating a ChatGPT-based workflow, verify whether your effective context/memory requirements fit inside DeepSeek’s actual API context limits and output limits (e.g., max output defaults and maxima differ by model mode). 
• Governance and auditability: If your organization relies on ChatGPT Business/Enterprise admin console, analytics, SOC/ISO, and “no training” commitments, DeepSeek’s equivalent enterprise posture is largely unspecified in the public sources reviewed; you may need compensating controls (self-hosting, strict data handling, vendor risk reviews). 

Enterprise adoption risks and geopolitical constraints

Reuters reports that OpenAI has accused DeepSeek of model distillation from U.S. systems and that governments have increased scrutiny and restrictions on DeepSeek due to privacy/security concerns. These factors can influence procurement decisions even when technical fit is strong, particularly for public sector and regulated environments. 

Decision matrix

This matrix is intentionally pragmatic: it focuses on criteria that usually decide real deployments. Scores are qualitative (High/Medium/Low) and grounded in documented capabilities.