Deepseek V3 Vs Gpt 4o Vs Claude 3 5 I Tested All Models For Coding

Bonisiwe Shabane

-Jan 15, 2026, 6:56 AM

deepseek v3 vs gpt 4o vs claude 3 5 i tested all models for coding

I spent weeks putting DeepSeek v3, GPT-4o, and Claude 3.5 through their paces for coding tasks. If you’re like me—tired of overpriced AI models that don’t deliver—here’s what actually works in real development scenarios. After 200+ test cases (everything from debugging to full feature implementation), here’s how they performed: DeepSeek surprised me—it responded 20-30% faster than the others. When you’re in the zone and waiting for AI suggestions, that speed difference feels huge. This is where things get interesting.

Compare these numbers: Translation? DeepSeek costs less than your morning coffee for what others charge as much as a fancy dinner. 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. Artificial intelligence is evolving faster than ever, and with each iteration, we’re seeing groundbreaking advancements. Models like DeepSeek V3, Qwen2.5, Llama3.1, Claude-3.5, and GPT-4o are at the forefront of this innovation. But how do they stack up against each other? In this mega guide, we’ll dive deep into these models, comparing their architecture, performance, and specialized features to help you understand which one suits your needs best. Each model brings unique strengths to the table.

If you need a powerhouse for coding and complex computations, DeepSeek V3 is unbeatable. For versatile applications, GPT-4o offers exceptional performance. Ultimately, the best model depends on your specific needs—whether it’s efficiency, multilingual capability, or enterprise-level reasoning. Got a favorite AI model? Let us know in the comments below! WhiteX AI is the AI-focused division of WhiteX Design, a web design and development agency based in Europe.

Our dedicated team of experts leverages the latest advancements in AI technology to deliver innovative solutions that help small to medium-sized businesses thrive in today's fast-paced digital world. With new AI models popping up almost daily see which LLMs fit best - ChatGPT vs DeepSeek vs Claude With new AI models popping up almost daily, development teams often find themselves asking, "Which one should we actually use?" It's a fair question – each model comes with its own set of strengths,... In this guide, we'll cut through the noise and take a practical look at three popular players: DeepSeek, ChatGPT (GPT-4 series), and Claude. Before diving into specific comparisons, let's take a quick tour of what makes each of these models tick. If you're working with complex reasoning tasks, DeepSeek might catch your attention.

It's good at pulling in relevant information to support its responses. If you like well-structured answers and like to give detailed instructions, you'll probably find DeepSeek's approach refreshing. ChatGPT, powered by OpenAI’s GPT-4 models, is widely adopted due to its versatility, strong instruction-following capabilities, and extensive fine-tuning for conversational tasks. It balances creativity with factual accuracy and is optimized for a variety of use cases, including coding, writing, and customer support. I spent weeks putting the top AI coding assistants through their paces – DeepSeek v3, GPT-4o, and Claude 3.5 – and the results surprised me. If you’re tired of burning through your AI budget, I’ve got some eye-opening findings you’ll want to see.

This wasn’t just casual testing – I ran all three models through identical coding challenges to get fair results. Here’s what mattered most in my evaluation: Every test ran in Cursor IDE using the same prompts. For API testing, I used this basic config: // Sample API configuration const deepseekConfig = { apiKey: 'YOUR_KEY', model: 'deepseek-v3', temperature: 0.7 }; When I saw the cost differences, my jaw dropped:

After burning through three pots of coffee and putting DeepSeek v3, GPT-4o, and Claude 3.5 through real coding challenges, I can finally tell you which integration methods actually work – and which will drive... Forget the marketing fluff. Here’s what happened when I tested every approach across 150+ coding tasks. Let’s talk numbers first. The price differences made my eyes water: That’s not just saving pennies – on large projects, DeepSeek consistently cost about 1/10th of what I’d pay for GPT-4o.

But does cheaper mean worse? I tested all three models on actual development work: Here’s where things get interesting. For everyday tasks like generating CRUD operations, DeepSeek kept up with GPT-4o about two-thirds of the time. But when I threw complex problems at it – like optimizing database queries across microservices – it only delivered useful solutions 4 times out of 10. I spent a week putting DeepSeek v3, GPT-4, and Claude 3.5 through their paces in Cursor IDE.

Here are my honest findings – no fluff, just what developers actually need to know. Let’s talk money first, because wow – the cost gaps are massive: For context: my test project chewing through 500k tokens daily would cost: If you’re bootstrapping or working solo, DeepSeek saves you enough for a nice dinner every week. But cheaper doesn’t always mean better – here’s how they actually perform. I ran 100 coding challenges across three categories:

After spending weeks testing DeepSeek v3, GPT-4o, and Claude 3.5 Sonnet on real coding projects, I found some eye-opening differences that might change how you work in Cursor IDE. Let me break down what actually works—and where each model falls short. I put all three models through 50 common developer tasks—from fixing broken Python scripts to generating API docs. The results surprised me: While GPT-4o technically won, the gap was smaller than I expected. The real shocker?

How much more you pay for that extra 2-4% success rate. Here’s where your wallet comes into play. Check out these token costs: DeepSeek costs 17x less than Claude for output. That’s not a small difference—that’s “hire-an-intern” level savings. Let me tell you about my late nights wrestling with AI integrations.

Deepseek V3 Vs Gpt 4o Vs Claude 3 5 I Tested All Models For Coding

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I Spent Weeks Putting DeepSeek V3, GPT-4o, And Claude 3.5

Compare These Numbers: Translation? DeepSeek Costs Less Than Your Morning

Both Support Very Long Context (128K Tokens) And Text Modality.

For Example, DeepSeek-R1 Scores 90.8% On English MMLU (vs. 88.3%

R1 In Particular Generates Visible Multi-step “thought” Before Answering (much

Deepseek V3 Vs Gpt 4o Vs Claude 3 5 I Tested All Models For Coding

People Also Search

I Spent Weeks Putting DeepSeek V3, GPT-4o, And Claude 3.5

Compare These Numbers: Translation? DeepSeek Costs Less Than Your Morning

Both Support Very Long Context (128K Tokens)​ And Text Modality.

For Example, DeepSeek-R1 Scores 90.8% On English MMLU (vs. 88.3%

R1 In Particular Generates Visible Multi-step “thought” Before Answering (much

Both Support Very Long Context (128K Tokens) And Text Modality.