University of Kwazulu-Natal

Deepseek V3 Vs Gpt 4 Vs Claude 3 5 I Tested All Solutions For Model

Bonisiwe Shabane
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deepseek v3 vs gpt 4 vs claude 3 5 i tested all solutions for model

Let me tell you about my late nights wrestling with AI integrations. After trying every combination of DeepSeek v3, GPT-4, and Claude 3.5 across real projects, I’ve got some surprises to share. The differences aren’t always where you’d expect – especially when you see the price tags. I clocked hours testing API tweaks, custom setups, and third-party tools. What worked? What made me want to throw my keyboard?

Here’s exactly what I found through hands-on testing. Across 100 coding tasks, here’s how they stacked up: Those percentages might look close on paper, but day-to-day? DeepSeek’s consistency on medium tasks saved me more time than I expected. When I ran the code through security scanners, DeepSeek had 15% fewer red flags than GPT-4. Claude surprised me though – its comments and docs were actually the clearest of the three.

The global AI race has entered a new chapter in 2025. Just months after the splashy launches of OpenAI’s GPT-5 and Anthropic’s Claude 4.1, Chinese startup DeepSeek quietly introduced V3.1, a massive open-weight model boasting frontier-level capabilities at a fraction of the cost. With all three models making headlines, the question for businesses and developers isn’t simply “which is the most powerful?” — it’s which delivers the best value. That means carefully balancing performance, cost, licensing, and ecosystem fit. Many organizations look to an experienced OpenAI development company to guide them in evaluating these trade-offs and implementing the right solution. In this article, we’ll break down how DeepSeek V3.1, GPT-5, and Claude 4.1 compare — and which one delivers the strongest return on investment.

Save time and money with this quick checklist to match AI models to your needs. Top AI Reasoning Model Cost Comparison 2025 Get a detailed comparison of AI language models DeepSeek's DeepSeek-V3 and OpenAI's GPT-4, including model features, token pricing, API costs, performance benchmarks, and real-world capabilities to help you choose the right LLM for your... DeepSeek-V3 is a Open-Source 671B parameter Mixture-of-Experts (MoE) model with 37B activated parameters per token. It features innovative load balancing and multi-token prediction, trained on 14.8T tokens. The model achieves state-of-the-art performance across benchmarks while maintaining efficient training costs of only 2.788M H800 GPU hours.

It incorporates reasoning capabilities distilled from DeepSeek-R1 and supports a 128K context window. The latest GPT-4, developed by OpenAI, features a context window of 8192 tokens. The model costs 3.0 cents per thousand tokens for input and 6.0 cents per thousand tokens for output. It was released on March 14, 2023, and has achieved impressive scores in benchmarks like HellaSwag with a score of 95.3 in a 10-shot scenario and MMLU with a score of 86.4 in a... GPT-4 is 18 months older than DeepSeek-V3. GPT-4 has a smaller context window (8,192 vs 128K tokens).

Unlike DeepSeek-V3, GPT-4 supports image processing. Compare costs for input and output tokens between DeepSeek-V3 and GPT-4. Architecture & Specs: DeepSeek-V3 is a 671B-parameter Mixture-of-Experts (MoE) transformer (37B activated per token) with a novel Multi-head Latent Attention and multi-token-prediction objective​. It’s pretrained on ~14.8 trillion tokens, then fine-tuned (SFT) and RL-tuned. DeepSeek-R1 uses the same 671B MoE base but is further refined via large-scale RL to enhance reasoning​. Both support very long context (128K tokens)​ and text modality.

By contrast, Anthropic’s Claude 3.7 Sonnet is a dense transformer (parameters undisclosed, estimated at “100+B”​) built on Claude 3 architecture. Claude 3.7 introduces a hybrid “thinking” mode: a unified model that can output quick answers or engage in visible chain‑of‑thought for extended reasoning​. Claude Sonnet 3.7 supports text, vision, and audio inputs with an extremely long (200K‑token) context window​. Performance & Benchmarks: DeepSeek-V3 already matches top models on many tasks (e.g. 88.5% on MMLU, 89.0% on DROP​), and DeepSeek-R1 improves further via RL. For example, DeepSeek-R1 scores 90.8% on English MMLU (vs.

88.3% by Claude 3.5 Sonnet) and 97.3% on MATH-500 (vs. 96.4% by GPT-4o)​. In coding and reasoning, R1 rivals GPT-4-level models: its AlpacaEval win-rate is 87.6% vs. ~57% for GPT-4o-mini and ~52% for Claude 3.5​, and on complex code problems it outperforms OpenAI’s o1-mini​.​ Capabilities & Features: DeepSeek-V3/R1 specialize in chain-of-thought reasoning with long contexts. R1 in particular generates visible multi-step “thought” before answering (much like Claude’s extended mode), trading speed for accuracy​.

They support rich SFT instructions but no built‑in tool use or web browsing (DeepSeek’s system is closed-loop chat). Claude 3.7 supports standard and extended modes: users can toggle “think longer” or even set a token budget for reasoning​. Claude Sonnet also offers specialized safety and alignment (Anthropic’s Responsible Scaling, guardrails against harmful outputs) and multimodal input (it can analyze images, audio, PDFs, etc.)​. By contrast, DeepSeek’s published model only handles text; no public vision or tool-API is announced. Availability & Access: DeepSeek-V3 and R1 are open-source (MIT license) and downloadable via Hugging Face​. The chat/API is currently private to DeepSeek’s Chinese app and API, but the code/model weights are public.

In contrast, Claude 3.7 is closed-source; access is via Anthropic’s hosted service (Claude.ai and API) or through partners (AWS Bedrock, Google Vertex AI). Claude’s pricing is $3/million input and $15/million output tokens​ (same as Claude 3.5). DeepSeek has no known usage fees (models are self-hosted); hardware costs are borne by the user. DeepSeek-R1 offers no commercial API yet (unless DeepSeek builds one), whereas Claude has enterprise plans. Fine-tuning: DeepSeek models (being open) can be fine-tuned or distilled by anyone; Claude weights are proprietary, so only Anthropic can fine-tune it or offer it as a service. Documentation & Research: DeepSeek publishes technical reports and code.

An arXiv paper and HF README detail the V3 and R1 designs (MoE, MLA, RL pipeline). The Hugging Face repos include evaluation tables and instructions​. Anthropic has released a blog and a system card for Claude 3.7​【23†(pages 1–2)} describing its philosophy and safety. Notable claims: Anthropic emphasizes Claude 3.7 as their first “hybrid reasoning” model【20†L21-L29】; DeepSeek claims R1 rivals GPT-4 (“OpenAI-o1”) on math/code​. Independent analyses (e.g. industry benchmarks and leaderboards) generally corroborate that Claude 3.7 Sonnet and Google’s Gemini/DeepMind models currently lead closed-source performance, while DeepSeek-V3/R1 set new open-source marks on math and code tasks​.

We’re in the first month of 2025 and already have a few benchmark-breaking AI models for coding: Mistral’s Codestral 25.01 and the recently released DeepSeek R1 model. But since we’ve already covered Codestral 25.01, this article is all about DeepSeek R1. We compare OpenAI’s GPT-o1 and Claude 3.5 Sonnet for coding tasks and give a technical overview and pricing for each model. But before we get into that, first let’s overview DeepSeek R1 and its model variants. DeepSeek R1 (where R stands for reasoning) is a newly released class of LLM models developed by the Chinese AI lab DeepSeek, designed specifically for tasks requiring complex reasoning and programming assistance. Currently, DeepSeek has released two variants of its model: DeepSeek-R1-Zero and DeepSeek-R1.

They employ a Mixture-of-Experts (MoE) and large-scale reinforcement learning (RL) architecture, allowing them to activate only a subset of its parameters for each token processed. This new design enhances their computational efficiency while maintaining high performance in generating and debugging code. For our comparison, we’ll be focusing on the main ‘R1’ model. OpenAI o1 is known for its advanced reasoning capabilities and has demonstrated solid performance in coding tasks, achieving a Codeforces rating of 2061, which places it in the 89th percentile among competitive programmers. Its architecture allows it to generate coherent code snippets and provide explanations, making it a popular choice among developers. However, its pricing is significantly higher, costing $60 per million output tokens compared to DeepSeek R1, which offers similar coding capabilities at about $4.40 per million output tokens.

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Choosing between DeepSeek V3.1, GPT-5, and Claude 4.1 in 2025 isn’t just about raw performance—it’s about scalability, cost, and business automation. Each model brings unique strengths: DeepSeek’s open-weight affordability, GPT-5’s enterprise reliability, and Claude’s safety-first reasoning. This guide delivers a clear head-to-head comparison of benchmarks, context limits, pricing, and deployment options, so whether you’re a startup founder, enterprise CTO, or researcher, you’ll know which AI model fits your workflow and... We’ll also explore real-world business use cases, cost savings, and how these models stack up against each other in coding, reasoning, compliance, and automation tasks. The star of deep-learning AI in 2025, DeepSeek V3.1 comes packed with 685 billion parameters—one of the largest open-weight models. Instead of firing all those neurons, though, it uses a Mixture-of-Experts (MoE) design, activating just 37 billion per token.

It’s smart and friendly on your compute bills, so startups and research teams can save big. DeepSeek recently announced DeepSeek V3.1 as a big leap towards the agent era, bringing together two inference modes—”Think” and “Non-Think”—within one model. This hybrid design lets developers toggle between fast, non-thinking chats and more deliberate, complex reasoning tasks.

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