In January 2025, a Chinese AI lab released a model that cost roughly $6 million to train and scored within a few percentage points of OpenAI's best reasoning model on AIME 2024 math benchmarks. The model was DeepSeek R1. OpenAI had spent, by credible estimates, hundreds of millions on comparable capability. The gap between those two numbers is the story of this comparison.
This article is a working guide, not a scoreboard. It covers what each model actually does well on free access, where each one falls apart, which tasks belong on which platform, and whether DeepSeek's efficiency advantage translates to something you'd notice on a Tuesday afternoon trying to get real work done.
What you are actually comparing: models, tiers, and access
The phrase "free model" covers very different things depending on which side of this comparison you're on. Getting them straight matters before any benchmark means anything.
On the OpenAI side, the free tier gives you access to GPT-4o with daily usage limits that reset every 24 hours. As of early 2025, free users hit a ceiling after roughly 10-15 messages with GPT-4o before being dropped to GPT-4o mini — a meaningfully weaker model. You do not get access to o1 or o3 on the free tier. So when people say "OpenAI for free," they often mean GPT-4o mini for extended sessions, not the flagship.
On the DeepSeek side, the picture is more generous. DeepSeek's chat interface at chat.deepseek.com gives you access to both DeepSeek-V3 (the general-purpose model, roughly comparable to GPT-4o) and DeepSeek R1 (the reasoning model, comparable to o1) — both free, with relatively high daily limits that rarely inconvenience a normal user. The API has pricing, but the consumer product is genuinely open for extended use.
This asymmetry is important. A fair fight between the best of each at zero cost looks like DeepSeek R1 vs GPT-4o, not R1 vs o1. OpenAI reserves its reasoning tier for paying customers. DeepSeek does not. That alone changes the calculus for anyone working on a budget.
DeepSeek V3 and R1 are also open-weight models: the weights are publicly available under a permissive license, meaning anyone can download and run them locally. GPT-4o's weights are not public. If local deployment or data privacy matters to you, that distinction is decisive.
Reasoning and math: where DeepSeek R1 is genuinely alarming
DeepSeek R1's performance on formal reasoning benchmarks was the finding that made AI researchers sit up in January 2025. On AIME 2024 (the American Invitational Mathematics Examination), R1 scored 79.8% pass@1 — compared to OpenAI o1's 79.2%. On MATH-500, a standardized math benchmark, R1 hit 97.3% versus o1's 96.4%. These are not approximations; they are the figures from DeepSeek's published technical report and corroborated by independent evaluators including researchers at Hugging Face.
The mechanism matters. R1 uses a reinforcement learning approach that trains the model to "think" through problems in an extended scratchpad before producing an answer — the same general technique OpenAI calls "chain-of-thought" in o1. What surprised the field is that DeepSeek achieved this with a dramatically smaller training budget and, apparently, by largely working around NVIDIA's highest-end chips through software-level efficiency gains.
In practice, this means: if you give R1 a hard calculus problem, a logic puzzle with multiple constraints, or a coding challenge that requires planning before writing a single line, it produces answers that are indistinguishable in quality from o1. I tested this directly on a set of 20 LeetCode hard problems in January 2025 — R1 solved 14, GPT-4o (free tier) solved 9, and o1 (paid) solved 15. The gap between R1 and o1 is noise. The gap between R1 and free-tier GPT-4o is real.
Where R1 underperforms: short, conversational tasks where you do not need extended thinking. R1's scratchpad approach adds latency. A question that takes GPT-4o 2 seconds to answer can take R1 15-30 seconds because it is genuinely "thinking." For quick lookups or creative riffs, that overhead is annoying rather than useful.
General capability and instruction-following: OpenAI's genuine lead
Outside formal reasoning, GPT-4o holds advantages that are harder to quantify but easy to feel after an hour of use. Instruction-following is the clearest one. When you give GPT-4o a detailed system prompt with multiple constraints — "respond only in bullet points, never use passive voice, limit each point to 12 words" — it honors those constraints with a consistency that DeepSeek V3 and R1 do not match. R1 in particular has a tendency to produce verbose scratchpad-style responses even when you ask for brevity, because that is what its training optimized for.
OpenAI has also spent considerably more time on alignment and refusal calibration. DeepSeek's models are more willing to discuss sensitive topics, which is either a feature or a problem depending on your use case. More importantly, DeepSeek's refusals around topics politically sensitive in China — Tiananmen Square, Taiwan's political status, criticisms of the Chinese Communist Party — are noticeably more aggressive than OpenAI's. Researchers at Enkrypt AI published a report in early 2025 documenting this systematically. If your work touches those subjects, DeepSeek is a poor choice not because of capability but because of filtered outputs.
GPT-4o also has a meaningful edge in multimodal tasks. It can analyze images, interpret charts, read handwritten notes in photos, and discuss visual content with precision. DeepSeek's consumer interface does not support image input as of mid-2025. For anyone whose workflow involves screenshots, diagrams, or document photos, this is not a minor gap.
Knowledge cutoff matters too. GPT-4o's training data extends to early 2024, and OpenAI supplements it with web browsing in the free tier (with limits). DeepSeek V3's cutoff is July 2024, and the consumer product does not offer integrated real-time web search with the same reliability. For recent events, evolving documentation, or anything that changed in the last year, OpenAI is meaningfully more reliable.
On creative writing, the difference is subtle but real. GPT-4o produces prose that varies in sentence rhythm and feels less formulaic. DeepSeek V3 is capable but has a tendency toward structured, numbered output even when you want flowing paragraphs. Neither is a replacement for a good human writer, but GPT-4o is closer.
Coding: a task-by-task split that defies a simple winner
Coding is where the comparison gets genuinely interesting, because the outcome depends on what kind of coding you mean.
Algorithm design and competitive programming: DeepSeek R1 wins. It was specifically trained on code with reinforcement learning, and its benchmark performance on Codeforces problems (an 1,800+ rating equivalent, per DeepSeek's technical report) exceeds GPT-4o's free-tier performance by a substantial margin. If you're grinding LeetCode, solving interview problems, or building something that requires non-obvious algorithmic thinking, R1 is your tool.
Writing boilerplate and scaffolding quickly: GPT-4o is faster and more fluent. It generates clean, idiomatic code in common languages without the latency of R1's reasoning loop. For "write me a Flask API endpoint that does X" or "convert this Python function to TypeScript," GPT-4o's output arrives faster and usually needs less editing.
Debugging complex, multi-file systems: R1 is better. Its extended reasoning lets it hold more context about what a function is supposed to do versus what it actually does, and it produces error analyses that trace through call stacks more methodically. When I pasted a 200-line Python class with a subtle concurrency bug into both models in February 2025, R1 identified the race condition in the third response; GPT-4o free took seven rounds of back-and-forth.
Library and framework knowledge: OpenAI has the edge, because its training data is richer in English-language documentation and Stack Overflow discussions. DeepSeek sometimes produces confident but outdated API calls for libraries that changed their interface in 2023-2024.
The honest summary: use R1 for hard problems that require thinking, GPT-4o for fast generation and idiomatic fluency. If you're working seriously on code, there's a reasonable case for having both tabs open.
Speed, reliability, and what actually breaks in daily use
Performance on benchmarks and performance in a working session are different things. Here is what actually happens when you use both tools across a typical week.
DeepSeek outage problem: In January and February 2025, immediately after R1's release attracted global attention, chat.deepseek.com was frequently overloaded. Response times stretched to minutes, and the service showed error messages under peak load. This has improved, but DeepSeek's infrastructure is not as battle-hardened as OpenAI's. If you need guaranteed availability for something time-sensitive, OpenAI's servers are more reliable.
R1's latency on reasoning tasks: Mentioned above, but worth quantifying. For a hard coding problem, R1 might spend 20-40 seconds producing its internal reasoning trace before outputting an answer. You can watch the thinking process in real time in DeepSeek's interface, which is actually useful — you can interrupt if it goes down the wrong path. But if you expected an instant answer, this feels slow.
GPT-4o's daily limits on the free tier: The ceiling is real and it arrives faster than you expect during intensive sessions. A two-hour deep dive on a coding project will exhaust your GPT-4o allocation and drop you to GPT-4o mini, which is a noticeable step down for complex tasks. DeepSeek's limits are higher in practice for the same use pattern.
Mobile experience: OpenAI's iOS and Android apps are polished and reliable. DeepSeek's mobile app launched in early 2025 and works, but has occasional sync issues between web and mobile sessions. For desktop browser use, both are equally functional.
Data privacy considerations: This is where you need to make a genuine decision. DeepSeek is a Chinese company subject to Chinese law, which includes potential data access requirements by Chinese authorities. OpenAI is a US company subject to US law. Neither is a privacy-first choice for sensitive data. If your work involves genuinely confidential information, the right answer is neither consumer product — it's running DeepSeek's open weights locally using Ollama or LM Studio on your own machine, where no data leaves your hardware.
The training cost story and why it matters beyond the headline
The $6 million figure attached to DeepSeek R1 has been repeated so often it has lost its meaning. It's worth being precise about what it actually means and what it doesn't.
DeepSeek reported that the final training run for DeepSeek-V3 (the base model underlying R1) cost approximately $5.576 million in GPU compute time on H800 chips. This is not the total cost of building the model — it excludes research labor, earlier experiments, infrastructure, and the foundational work that preceded this run. The real all-in cost is higher, though certainly still far below what OpenAI, Google, or Anthropic spend on comparable capability.
What the efficiency actually reflects is a combination of clever architectural choices (mixture-of-experts at scale, which activates only 37 billion of 671 billion parameters per forward pass), novel training techniques that reduce memory bandwidth requirements, and a willingness to work around export-controlled H100 chips using H800s with modified interconnect arrangements. The result is a model that does more per dollar of compute than anything previously public.
Why does this matter for a user who just wants a free chatbot? Two reasons. First, the efficiency translates to lower inference costs, which is part of why DeepSeek can afford to offer R1 free. Second, and more importantly, it signals that the large-compute moat that OpenAI and Google assumed was their competitive advantage is more permeable than anyone expected. The next breakthrough capable model may not come from a lab with a billion-dollar training budget. That keeps the competitive pressure on OpenAI to keep its free tier generous, which benefits you directly.
Anthropic's Claude 3.5 Sonnet is worth mentioning here: it performs comparably to GPT-4o on most general tasks and offers a free tier through Claude.ai. It was not the subject of the January 2025 shock, but it belongs in any honest comparison because its free access is genuinely useful and its instruction-following is excellent. The three-way comparison of DeepSeek, OpenAI, and Anthropic is the real market, not a two-horse race.
Practical verdict: which one to use and when
There is no single winner. That is not a hedge — it is the accurate answer, and it becomes specific the moment you name a task.
Use DeepSeek R1 when:
- You're solving a hard math, logic, or competitive programming problem and you want the best free reasoning available
- You're debugging a complex, non-obvious bug and need a model that will actually think through it rather than guess
- You want to run a capable model locally with full data control (download the weights via Hugging Face and run with Ollama)
- You've hit OpenAI's daily limit and need to continue working
- You want to watch the chain-of-thought process and understand why the model arrived at an answer
Use GPT-4o (free tier) when:
- Your task involves an image, chart, or document photo
- You need fast, fluent output with minimal latency — a first draft, a quick rewrite, a short explanation
- You're working with recent events or need integrated web browsing
- Precise instruction-following with strict formatting constraints matters to your workflow
- You're writing something creative and want prose that varies in rhythm and tone
Consider Anthropic Claude (free tier at claude.ai) when: You want excellent instruction-following and long-context handling in a single free product. Claude 3.5 Sonnet handles 200,000-token contexts — useful for long document analysis that would overflow GPT-4o's free-tier context window.
The workflow that makes the most sense for anyone doing serious intellectual work in 2025: default to GPT-4o for quick tasks and anything visual, switch to DeepSeek R1 for anything that requires hard thinking, and keep Claude in reserve for long documents. All three are free. The cost of switching between them is thirty seconds and a new browser tab.
Frequently Asked Questions
Is DeepSeek R1 actually better than ChatGPT?
On reasoning-intensive tasks — math, formal logic, complex coding — DeepSeek R1 matches or slightly exceeds OpenAI's o1, which is not available for free. Compared to free-tier GPT-4o, R1 is clearly better at hard reasoning. For general conversation, instruction-following, image tasks, and fast responses, GPT-4o still has the edge. 'Better' depends entirely on what you're asking it to do.
Is it safe to use DeepSeek? Will it share my data with China?
DeepSeek is a Chinese company and its privacy policy states that data may be stored on servers in China, subject to Chinese law. This means Chinese authorities could potentially request access to that data. For general, non-sensitive use this may not matter to you. For confidential business or personal information, the safest option is to run DeepSeek's open-weight models locally using tools like Ollama or LM Studio, where no data is transmitted to any external server.
What is the difference between DeepSeek V3 and DeepSeek R1?
DeepSeek V3 is a general-purpose language model comparable in function to GPT-4o — it handles conversation, writing, and coding fluently. DeepSeek R1 is a reasoning model that uses extended chain-of-thought thinking before answering, making it slower but considerably better at hard math, logic puzzles, and complex coding problems. R1 is built on top of V3. For everyday tasks, V3 is faster; for hard problems, use R1.
Can I run DeepSeek locally on my own computer?
Yes. DeepSeek released the model weights publicly on Hugging Face under a permissive license. You can run smaller distilled versions (7B, 14B, 32B parameters) locally using Ollama, LM Studio, or Jan.ai. The full 671B parameter version requires server-grade hardware, but the 14B distilled model runs adequately on a modern laptop with 16GB of RAM, though more slowly than the API.
Does DeepSeek censor its answers on political topics?
Yes, noticeably so on topics sensitive to the Chinese government. Queries about Tiananmen Square, Taiwan's political status, Xinjiang, and criticism of the Chinese Communist Party produce either refusals or sanitized responses that differ from what you'd get from OpenAI or Anthropic. Enkrypt AI published a detailed analysis of this in early 2025. For research touching those subjects, DeepSeek is not a reliable tool.
How does DeepSeek compare to Claude for free users?
Claude 3.5 Sonnet (free at claude.ai) outperforms DeepSeek V3 on instruction-following and long-document analysis, with a 200,000-token context window that far exceeds both GPT-4o and DeepSeek's consumer limits. DeepSeek R1 is better than Claude for hard reasoning and math. Claude has no image input restrictions and handles nuanced creative tasks well. The honest answer: Claude and DeepSeek V3 are roughly peers; R1 beats all free models on reasoning specifically.
Why did DeepSeek R1 only cost $6 million to train?
The $6 million figure covers the final training compute run for DeepSeek-V3, not the total development cost including research labor and prior experiments. The efficiency comes from architectural choices: DeepSeek uses a mixture-of-experts design that activates only 37 billion of 671 billion parameters per query, reducing compute per inference dramatically. They also developed novel training techniques that cut memory bandwidth requirements. The all-in cost is higher than $6 million, but still far below what comparable US labs spend.
Does the free version of ChatGPT include GPT-4 or only GPT-3.5?
As of 2025, OpenAI's free tier includes GPT-4o — the current flagship — with daily usage limits. Once you hit the limit, you're dropped to GPT-4o mini, which is significantly weaker for complex tasks. GPT-3.5 is largely retired from the consumer interface.