DeepSeek R1 vs GPT-4 : Which One Should You Use?

AI has moved beyond just chatting—now we care about reasoning quality, cost, and openness. Two names that keep coming up are DeepSeek R1 and OpenAI’s GPT-4. Both are powerful, but they’re built for slightly different goals.

This article compares DeepSeek R1 vs GPT-4 on:

• Architecture & openness

• Reasoning performance

• Multimodality (text vs images/audio)

• Context window

• Pricing & deployment

• Best use cases

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1. Quick Overview

DeepSeek R1

• A family of “open reasoning” models (up to 70B and larger), trained with heavy reinforcement learning to improve chain-of-thought reasoning.

• Released with open weights (MIT licence), plus distilled smaller models (Llama/Qwen based) and APIs.

• Focused on math, code, and multi-step reasoning, with performance comparable to OpenAI’s o1-style reasoning models.

GPT-4

• OpenAI’s 2023 large multimodal model: accepts text and images, outputs text; used widely in ChatGPT and APIs.

• Closed-weight, proprietary; delivered only through OpenAI / Azure APIs.

• Known for strong general performance across exams and professional benchmarks (bar exam, Olympiad-style tasks, etc.).

High-level takeaway:
DeepSeek R1 is an open, reasoning-first model; GPT-4 is a closed, general-purpose multimodal model.

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2. Performance & Benchmarks

Exact numbers vary by benchmark and version, but several independent comparisons show:

• DeepSeek R1 achieves performance comparable to OpenAI o1 (a reasoning-optimized successor to GPT-4) on math, coding, and reasoning tasks.

• On standard benchmarks like MMLU (57 subjects), DeepSeek R1 scores around 90.8% vs ~85% for GPT-4 Turbo in one public comparison.

• Analyses of RAG and reasoning note that R1’s Mixture-of-Experts design gives competitive reasoning at lower cost than GPT-4o/4-class models.

GPT-4, meanwhile:

• Still performs at or near human level on many standardized exams and is extremely reliable for general knowledge, writing, and instruction following.

Interpretation

• For pure reasoning (math proofs, complex coding, step-by-step logic), DeepSeek R1 is at least in the same league—and sometimes ahead of GPT-4/Turbo-class models on public benchmarks.

• For broad, everyday tasks (writing, tutoring, brainstorming, multimodal input), GPT-4 remains extremely strong and more battle-tested.

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3. Modality & Capabilities

If you need image+text in one prompt (e.g., reading diagrams, screenshots, UI mockups), GPT-4 wins easily. If you only care about text reasoning and want open weights, R1 is more attractive.

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4. Context Window & Long Documents

• Many DeepSeek R1 deployments support up to 128k tokens of context.

• Original GPT-4 models shipped with 8k and 32k token windows.

(Recent GPT-4.1 models go much higher—up to 1M tokens—but strictly speaking that’s a successor to GPT-4. )

For this comparison:

• If you’re on older GPT-4 endpoints, DeepSeek R1 may give you a larger context window out of the box.

• If you’re allowed to use GPT-4.1 instead of plain GPT-4, OpenAI regains the lead for extreme long-context use.

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5. Architecture, Openness & Deployment

DeepSeek R1

• Uses a Mixture-of-Experts (MoE) architecture that activates only a small fraction of parameters per token, giving better cost-efficiency for reasoning workloads.

• Released as open-weight models under MIT, meaning:

  • You can self-host on your own GPUs/cloud.

  • You can fine-tune and even distil the model commercially.

• Available via multiple providers (DeepSeek API, Hugging Face, Ollama, and cloud GPU platforms).

GPT-4

• Closed architecture; OpenAI hasn’t published parameter counts or training details.

• Can only be used via:

  • OpenAI API

  • Azure OpenAI Service

• You can’t download weights or run fully offline; fine-tuning is limited to certain variants and use cases.

So if you need:

• Maximum control, self-hosting, or on-prem compliance → DeepSeek R1

• Managed, “no-ops” SaaS with strong SLAs and ecosystem tools → GPT-4

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6. Pricing & Cost Efficiency

Exact pricing changes often, but general patterns:

GPT-4 API

• GPT-4 Turbo (128k context) is around $10 per 1M input tokens, $30 per 1M output according to mid-2024 docs.

• Higher-end successors like GPT-4.5 are significantly more expensive.

DeepSeek R1

• DeepSeek positions R1 as cheaper per token than GPT-4o/4-class models due to the MoE design—one analysis claims roughly 4.6× cost savings vs GPT-4o for similar reasoning tasks.

• Because weights are open, you can also self-host and tune cost based on your hardware and throughput needs.

Net effect:
For heavy, reasoning-centric workloads, R1 is generally more cost-efficient per “unit of intelligence”, especially if you’re willing to manage infrastructure yourself. GPT-4 is simpler if you just want to pay an API bill and not think about GPUs.

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7. When to Choose DeepSeek R1 vs GPT-4

Choose DeepSeek R1 if you:

• Care most about math, code, and step-by-step reasoning.

• Want open weights for self-hosting, fine-tuning, or regulatory reasons.

• Need a large context window without paying top-tier proprietary prices.

• Are building internal tools/agents where text-only interaction is enough.

Choose GPT-4 if you:

• Need multimodal input (images + text) in the same conversation.

• Value mature tooling and ecosystem (ChatGPT plugins, Assistants API, Azure integrations).

• Prefer not to manage infrastructure—just call a stable, production-grade API.

• Already use other OpenAI services and want a single vendor.

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8. Simple Decision Cheat-Sheet

• Startup building an AI coding assistant you want to self-host → DeepSeek R1.

• Product team adding image-understanding and chat into a SaaS app → GPT-4 (or its successors).

• Research lab or enterprise that needs to inspect / customize the model → DeepSeek R1.

• Solo creator who just wants reliable, polished answers and tools → GPT-4 via ChatGPT / API.