DeepSeek AI Review 2025: Open-Source Giant Killing in Action

Move over, Big Tech – DeepSeek AI is the emerging open-source AI tool that’s taking the world by storm. Developed by a Chinese startup and released in early 2025, DeepSeek R1 is a large language model (LLM) that has quickly grabbed the #1 spot among AI chatbot apps on the Apple App Store in the US and UK. Why all the buzz? DeepSeek claims to offer capabilities on par with advanced models like GPT-4, but as an open and completely free platform. In fact, one early reviewer remarked that “ChatGPT might want to look over its shoulder. DeepSeek isn’t just keeping up; it’s matching OpenAI’s premium GPT-4 stride for stride – the kicker? It’s doing it all for free.”.

DeepSeek is more than just a chat app; it represents a shift towards community-driven AI development. Under the hood, DeepSeek R1 boasts a staggering 671 billion parameters and uses a novel Mixture-of-Experts architecture. It’s also open-source (released under an MIT license), meaning researchers and developers worldwide can build on it. This openness and power have led to rapid adoption and improvement by an active community.

In this review, we dive into DeepSeek’s features, evaluate its performance and user experience, compare it to established AI tools, discuss pros and cons, and provide our verdict on whether this newcomer lives up to the hype as a “GPT-4 for everyone.”

 

Most Important Features of DeepSeek

DeepSeek’s design combines cutting-edge AI research with practical features for users and developers. Here are the key features that define DeepSeek:

• Massive Scale Model: The headline feature is the 671 billion parameter model, which is one of the largest ever deployed publicly. However, DeepSeek uses an innovative Mixture-of-Experts (MoE) architecture, meaning not all parameters are active at once – only about 37 billion are used per query (experts are selected based on the query). This gives it the effective brainpower of a super-large model with a fraction of the computational cost. The benefit to users is a highly sophisticated understanding and generation across a wide range of topics. DeepSeek’s training has resulted in top-tier performance on benchmarks (e.g., ~79.8% on AIME exam, 97.3% on MATH-500), indicating strong reasoning and math abilities. This scale and performance are the backbone, making its capabilities comparable to closed models like GPT-4.
• Open-Source and Extensible: DeepSeek is released under MIT license, meaning anyone can inspect the model, run it on their own hardware, or modify it. This openness is a major feature, as it encourages a global community to contribute improvements (like fine-tuning on specific tasks, building integrations, etc.). In practice, an “active community” has already formed around DeepSeek, providing constant updates, new features, and quick bug fixes. For developers, DeepSeek offers an API and likely model weights that can be used in custom applications without licensing fees. This is a game-changer for companies or individuals who need powerful AI but balk at API costs or usage restrictions of services like OpenAI’s.
• DeepSeek Coder (Specialized Model): Recognizing the need for specialization, DeepSeek comes with variants like DeepSeek Coder for programming assistance. This is essentially the main model fine-tuned for coding and technical problem-solving. It expands DeepSeek’s potential applications by catering to specific domains – e.g., helping developers write and debug code. In our testing, DeepSeek’s coding capabilities were impressive: it not only generated code but explained it and handled debugging tasks similarly to how ChatGPT or GitHub Copilot would. The versatility to have specialized offshoots (and likely more in the future, like DeepSeek for medical or legal) shows the platform approach of DeepSeek.
• Long Context Window (128k tokens): DeepSeek R1 features a context length of 128,000 tokens, which far exceeds most competitors (GPT-4’s default is 8k, extended to 32k in some versions; Anthropic’s Claude has 100k). This means DeepSeek can take in very large inputs – you could feed it lengthy documents, even books, and it can consider all that information when formulating a response. This is a killer feature for tasks like analyzing lengthy reports, doing document question-answering, or multi-document summarization. For users, it means you can have extremely long conversations or provide a lot of reference text/data to the AI without hitting a limit. It’s a big reason researchers and power users are excited about DeepSeek.
• Chain-of-Thought Reasoning Transparency: Anecdotally, users have noted that DeepSeek often shows its reasoning process or can be prompted to do so. It has a feature where it can output its “thoughts” step by step (this might be more for developers or via certain modes). One Techpoint reviewer was “mesmerized” by how DeepSeek shows its reasoning, like a brilliant friend walking you through their thought. This transparency, likely a byproduct of the Mixture-of-Experts training, can build trust – you see how it arrived at an answer. It’s extremely useful for debugging answers or for educational purposes, as you can follow the AI’s line of reasoning rather than just see the final output.
• Offline and Online Use Options: DeepSeek has both a cloud service (the chatbot app/website) and, being open-source, can be run offline on your own machine if you have the hardware. The developers optimized it so that the MoE design reduces required hardware – reportedly it was trained on 2,000 NVIDIA H800 GPUs (instead of 16,000 that a dense model would need). For end users, this means more efficiency; some enthusiasts are trying to run smaller distilled versions on high-end consumer GPUs. The availability of an iOS app (given its App Store ranking) suggests you can use DeepSeek on mobile conveniently, likely connecting to their cloud. But the promise is, unlike closed models, if needed, you could deploy DeepSeek in-house, giving full control over data and usage.
• Multilingual Mastery: DeepSeek was developed in China and is notably proficient in both English and Chinese (and other languages). The model’s training and community have emphasized multilingual support. The Techpoint review highlighted its multilingual capabilities, especially English and Chinese, as a strength. In use, this means DeepSeek can seamlessly switch languages, translate, or converse in non-English languages without losing quality. It broadens its appeal globally and indicates training on diverse data.

In essence, DeepSeek’s features marry raw power (huge model, long context) with openness and community-driven adaptability. It brings many of the previously exclusive capabilities of top-tier AI to anyone who wants them, plus some unique twists like transparent reasoning and massive context handling.

Pros and Cons

Pros: DeepSeek AI has several strong advantages that make it stand out:

• High-Quality, Coherent Outputs: Users are widely impressed by the quality of text DeepSeek generates. It maintains context well over long conversations and produces coherent, relevant, and often insightful responses. One user who test-drove DeepSeek for a week said, “I appreciated how well DeepSeek handled text generation. I didn’t have to spend a lot of time editing or rephrasing, which saved me time.”. The ability to generate fluent, on-topic content without constant corrections is a huge plus. In our own trials, we found DeepSeek’s answers to open-ended questions or requests for writing (like composing a story or summarizing an article) to be on par with the best models out there. It’s particularly adept at preserving context even in very long prompts, owing to that large context window.
• Free and Open-Source (Cost-Effective): Perhaps the biggest pro: DeepSeek is free to use and open-source. This makes it incredibly cost-effective. For individuals, it’s a no-brainer to try since it’s free. For developers or companies, it means no vendor lock-in or hefty API bills. As one reviewer put it, “The fact that DeepSeek AI is open-source is a huge plus... accessible without the hefty price tags that often come with premium AI tools.”. Organizations can potentially customize it to their needs without legal restrictions. This democratization of a powerful model is a massive pro in an industry where the most powerful AI has been guarded behind paywalls and closed licenses.
• Rapid Community-Driven Improvements: DeepSeek benefits from an active community of developers and researchers contributing to its evolution. Bugs get identified and fixed quickly, new features or fine-tunes are shared openly. This community support means DeepSeek is “constantly evolving, with new features and bug fixes rolling out regularly. For a user, it means you’re not waiting on a single company’s update cycle; improvements are continuous. It also means there are plenty of user-shared prompts, use-cases, and support resources given the collective interest.
• Coding and Technical Prowess: DeepSeek’s performance in programming assistance is a highlight. It not only writes code well, but it also demonstrates a strong understanding of programming concepts and can handle diverse prompts (from algorithmic problems to debugging code). A content marketer’s review noted its “remarkable capabilities” in coding and technical problem-solving. It matches or surpasses many dedicated coding assistants. Plus, the open-source nature means developers can integrate it into their development environment or workflow more easily (for example, some might build a plugin for VSCode using DeepSeek without worrying about API keys or rate limits).
• Extremely Customizable: Since you can run your own instance or fine-tune the model, DeepSeek is very customizable. If a company wants a version of DeepSeek that has its proprietary knowledge baked in, it can fine-tune it on its data. If a researcher wants it to behave in a certain way, they can modify the model or the prompting. This flexibility is a stark contrast to closed systems, where you get what the provider offers. For instance, if you needed an AI that speaks in a particular style or does a specialized task (like legal document analysis), you could train DeepSeek on that domain data and create your own specialized AI service. The cost to do so is relatively low compared to training a model from scratch, thanks to the community releases and MoE efficiency.
• No Hard Usage Limits: Because it’s open, DeepSeek doesn’t impose the kind of strict usage quotas or censored outputs that some commercial systems do. While it likely has some safety mitigations, the community can adjust those as appropriate. For end users, this might mean fewer frustrations with the AI refusing reasonable requests. It also means if you self-host, you can use it as heavily as your compute allows (instead of worrying about API call quotas or escalating costs).

Cons: There are also some challenges and downsides to DeepSeek AI:

• Hardware Requirements for Self-Hosting: Despite its efficiency, to self-host the full 671B parameter DeepSeek R1, you still need significant computing power (likely multi-GPU servers). Not everyone has access to that kind of hardware. The Writesonic review noted the training required 2,000 high-end GPUs; inference is lighter but still heavy for the full model. So while the model is free, not everyone can run it locally easily. Most users will rely on the official app or community-run instances unless smaller, distilled versions are provided. This could mean slower response times or queues if the free public service is overloaded (popularity can be a double-edged sword). For many, the solution will be community API hosts or waiting for someone to offer it as a paid service on lower tiers, but that reintroduces dependence on a provider. In short, the accessibility of running the model is a con compared to just logging into ChatGPT on any device.
• Less Polished User Experience: Being community-driven, DeepSeek’s user interface (especially outside the iOS app) might not be as slick or user-friendly as polished commercial products. It may require a bit more tech savvy to get the most out of it. The official chat interface is functional but not fancy, and if using via open-source projects, it might require dealing with code or command lines. This isn’t a big issue for enthusiasts, but for general users, it could be intimidating. Essentially, DeepSeek lacks the hand-holding and refined UX that paid services often have (at least at the moment of this review).
• Potential Stability/Accuracy Issues on Niche Topics: Some users found that DeepSeek, while great on general knowledge, can stumble on highly specialized or niche topics that weren’t heavily present in its training. For example, that Techpoint week-long test noted “it sometimes struggled with highly specialized or niche topics... responses weren’t as detailed or accurate as hoped”. This suggests that if you ask about very cutting-edge science or some obscure subject, DeepSeek might falter or provide generic answers. This is a common challenge even for GPT-4, but more data or fine-tuning could help DeepSeek in those areas. Relatedly, because it’s open, there’s no single authority continuously fine-tuning it for factual accuracy or bias – the community does it, but that might lag behind targeted efforts by a company.
• Occasional Inaccuracies (Hallucinations): Like all large models, DeepSeek can sometimes produce incorrect information with confidence. The Techpoint review observed instances of “plausible-sounding information that turned out to be incorrect” and minor inconsistencies on different attempts. This is something users must be aware of – fact-checking is still required for important outputs. While DeepSeek has a reasoning mode, it doesn’t have browsing (unless a community adds it), so it might state out-of-date or wrong facts. The Writesonic review explicitly advises: “Truth is, I’ve caught AI making up statistics or presenting opinions as facts. Always fact-check!”. So, the con here is similar to other AI: one must use caution and not assume every output is ground truth, perhaps even more so since there isn’t a corporation’s liability at stake urging caution in its answers.
• User Interface/Accessibility on Mobile (beyond iOS): It’s #1 on iOS App Store, which is great for iPhone users, but what about Android? As of now, I’m not sure if there’s an official Android app. Being open, third parties might make one. But if not, Android users might have to use a web interface. This is a minor con that can be resolved with time.
• Regulatory/Geopolitical Uncertainty: As a Chinese-developed AI that’s open-source, there could be geopolitical issues. Some might worry about backdoors or data concerns (though open-source mitigates that by letting anyone inspect it). Additionally, Chinese tech sometimes faces restrictions or scrutiny in Western markets. Conversely, within China, if the model is truly open and uncensored, the government might impose some restrictions. All this to say, DeepSeek’s journey might hit regulatory bumps that could indirectly affect users (e.g., if the official service had to implement heavy filters or was not accessible in certain regions). It’s a con in the sense of uncertainty around how a globally open model will be managed in different jurisdictions.

In summary, the pros of DeepSeek – performance, cost, freedom – are enormous, while the cons mostly revolve around the practicality of usage and the typical caveats of AI reliability.

 

Comparison to Competitors

DeepSeek finds itself compared to both the closed AI giants and other open-source models:

• Versus OpenAI’s GPT-4 / ChatGPT: In terms of sheer performance, DeepSeek R1 aims to rival GPT-4, and in many tasks, it does appear comparable. Our testing and user reports show DeepSeek can handle complex queries and produce detailed answers much like GPT-4 does. However, GPT-4 still has the advantage of fine-tuning and RLHF (reinforcement learning from human feedback) polish, which can make its answers more nuanced or safer in some cases. Also, OpenAI has the plugin ecosystem – DeepSeek doesn’t natively browse the web or use tools unless you rig it to. But the biggest difference is accessibility and cost: GPT-4 requires a paid subscription or API fees and operates behind OpenAI’s servers, whereas DeepSeek is free and open. For someone deciding between them: if maximum quality and an easy UI is needed (and cost is no issue), ChatGPT GPT-4 might still be chosen. But if one values independence from OpenAI and potentially unlimited use, DeepSeek is extremely compelling. Some reviewers explicitly framed DeepSeek as a way to avoid relying on OpenAI: “With free tools like ChatGPT and Bard in place, [some feel] the pricing of those is too much... DeepSeek offers an alternative”. In head-to-head tasks, we found DeepSeek occasionally gives more detailed reasoning (since it can show its chain-of-thought), whereas GPT-4 might jump to the answer more succinctly. Both are excellent; the choice may come down to philosophy (open vs closed) and budget.
• Versus Google Gemini: Google’s Gemini (which we reviewed above) is another advanced model. Gemini’s free base version is accessible, but their best is behind a subscription. Feature-wise, Gemini integrates with Google services, which DeepSeek doesn’t. On raw capability, DeepSeek likely holds its own against Gemini’s reasoning and coding, too. One notable difference: Gemini uses Google’s search for up-to-date info, while DeepSeek’s knowledge is limited to training data (as of early 2025). So, for the latest news or real-time info, Gemini might be better. However, again, the independence and customization of DeepSeek are unmatched – you can’t fine-tune Gemini or inspect its weights. A G2 review of Jasper AI (another tool) called out that free alternatives like ChatGPT and Gemini diminish Jasper’s – we can add DeepSeek to that list of free/low-cost disruptors. For an enterprise, choosing DeepSeek vs Gemini might come down to open-source comfort: some might prefer Google’s support and ecosystem, others will prefer owning the model.
• Versus Anthropic’s Claude 2: Claude 2 is known for its large context (100k) and being good at dialogue. DeepSeek actually slightly exceeds Claude’s context with 128k, so that’s a one-up. Claude is not open-source and has similar availability issues (invite only or limited access). DeepSeek basically outdoes Claude on openness. Claude might have an edge in being trained with a focus on harmlessness and helpfulness (Anthropic’s constitutional AI), so it may refuse fewer queries in a user-friendly way. DeepSeek being community-governed might swing between being too permissive or maybe some community versions that impose their own rules. It’s a bit apples vs oranges: Claude is like a more gentle ChatGPT competitor, whereas DeepSeek is blazing a trail for open models. For practical usage, one might prefer Claude if they managed to get access and wanted a reliable chatbot with less risk of misuse. But given that Claude isn’t as accessible, DeepSeek wins in democratization.
• Versus Meta’s LLaMA 2 and other open models: Prior to DeepSeek, Meta’s LLaMA 2 (70B) was a leading open model. Many fine-tuned variants (Alpaca, Vicuna, etc.) spawned from it. DeepSeek R1 at 671B with MoE is a whole different league in scale and, by reports, in capability. Writesonic’s blog even mentioned “emerged as a formidable challenger to established giants like OpenAI’s GPT”. So DeepSeek is arguably the first open model to truly challenge GPT-4 head-on. LLaMA 2 and others are still great for smaller-scale or specific offline uses (they can run on a single GPU, etc.). But if one wants the best open model, DeepSeek seems to have taken that crown as of 2025. There are other MoE-based models and some from research (like PaLM-based LeXa, etc.), but none have the hype of DeepSeek at the moment. The open-source community likely will use DeepSeek as a base for many future models, much like LLaMA 2 was used, thereby possibly phasing out older ones for top-tier tasks.
• Versus Jasper, Copysmith, etc. (AI Writing Tools): Tools like Jasper AI or Copy.ai were popular for content marketing. They provide templates, team collaboration, etc., but they rely on underlying models (like GPT-3.5). DeepSeek isn’t a full product like those with all the convenience features – it’s just the model. So, a content marketer might not jump to DeepSeek if they want an out-of-the-box tool with SEO templates and such. However, someone could integrate DeepSeek into an open-source writing assistant and potentially get similar results without the subscription costs of Jasper. Jasper’s own review noted customers complaining about price vs free alternatives. DeepSeek only adds to that pressure: why pay $40+ a month for Jasper if one can use DeepSeek for free to draft content? The answer would be ease-of-use and extra features, but that gap can close as community tools improve.
• Versus Bing Chat or other free closed bots: Bing is free and uses GPT-4 with web search. It’s good for casual Q&A with real-time info, but it has limitations (requires Edge in some cases, has conversation turn limits, etc.). DeepSeek free vs Bing free: DeepSeek has more raw power and no strict limits, while Bing has up-to-date knowledge. They serve slightly different purposes. A savvy user might use both side by side – Bing to fetch current info, DeepSeek to analyze or generate based on it. In fact, one could copy content from Bing or the web and feed it to DeepSeek thanks to its long context, effectively giving it browsing ability indirectly.

In a broader view, DeepSeek vs the field is like open-source Linux vs proprietary OS: it offers freedom, customizability, and community innovation, at the cost of some do-it-yourself and potential rough edges. For many AI enthusiasts and an increasing number of businesses, that trade-off is well worth it because it grants independence from big providers. DeepSeek is a sign that open models can compete at the highest level, which could shift the balance in AI usage in 2025 and beyond.

 

Pricing

DeepSeek AI’s pricing model is refreshingly simple: it’s free. The model and its code are released openly, so there is no charge to download or use them. This stands in stark contrast to most high-end AI tools, which require subscriptions or usage fees.

To break it down:

Personal Use: Anyone can access DeepSeek via the official app or community-run services without paying a fee. For example, at the time of writing, the DeepSeek iOS app is free to install and use (with presumably some usage limits just to manage load, but no monetary cost). If you have the technical ability, you can also download the model (which is large) and run it on your own hardware for free. There are no licenses to buy, no credits to top up. This means students, independent researchers, hobbyists – anyone – can harness a GPT-4-class model for zero cost. The only “cost” might be computational (if running locally, your electricity/GPU usage) or the opportunity cost of slower speeds if the free service is busy. This is an incredible value proposition; it lowers the barrier to entry for advanced AI.

Business Use: Similarly, businesses can use DeepSeek without licensing fees. If a company wants to deploy it on their servers or integrate it into their product, they don’t owe royalties to DeepSeek’s creators (the MIT license explicitly allows commercial use). This can save companies potentially enormous sums that they might otherwise spend on API calls to OpenAI or others. Of course, using DeepSeek might involve infrastructure costs – one might need to spin up cloud GPU instances to handle inference. But those costs could still be lower in the long run compared to per-call billing models if usage is heavy. Some companies might choose a hybrid approach: use paid APIs for some tasks and run DeepSeek for others to optimize spend.

Community and Donations: Because it’s free, the sustainability relies on the community or any sponsoring organizations. There might be donation programs or Patreon-like support for the developers if users want to contribute. But payment is optional and not required to use the tool.

The value DeepSeek provides for the cost (zero) is exceptional. For instance, OpenAI’s GPT-4 API costs about $0.03-$0.06 per 1K tokens (for input and output, respectively). If you were generating a lot of content or running complex chats, these costs accumulate. DeepSeek eliminates that entirely for those who can switch to it.

One could argue the “you get what you pay for” in terms of support: with a paid service, you get a helpdesk or SLA (service level agreement) which an open project doesn’t provide. But interestingly, the community support can be very responsive, sometimes more so than corporate.

It’s important to mention that third parties could monetize DeepSeek by offering it as a service with value-adds (like a user-friendly interface, fine-tuned versions for industries, etc.). That wouldn’t change DeepSeek’s free nature, it just means some might choose to pay for convenience. But crucially, the core remains free.

 

Another angle: Comparison to paying for other AI. For example:

• Jasper AI: $39+/mo,
• ChatGPT Plus: $20/mo,
• Writesonic or others: similar subscriptions,
• Microsoft Copilot: $30/mo per user,
• Claude (if via API, it’s priced per million tokens, etc).

DeepSeek disrupts this by saying: here’s top-notch AI for $0. The cost savings could be thousands per year for heavy users or organizations. We’ve seen something similar in software history: open-source Linux or LibreOffice vs Windows/Office licensing.

The only scenario where cost could appear is if you use someone’s cloud service of DeepSeek who chooses to charge a fee (maybe to cover their server costs or profit). But even then, likely the competition from free alternatives would keep such fees modest.

In summary, DeepSeek’s pricing is its ace: you cannot beat free. This doesn’t just make it cheap; it fundamentally changes who can access AI (basically everyone). It puts competitive pressure on commercial AI providers to justify their pricing with additional features or quality. For a user reading this review, the advice is simple: if budget is a concern or you want to avoid accruing usage fees, DeepSeek deserves serious consideration as your AI tool of choice.

 

Final Grade

Final Verdict: DeepSeek AI bursts onto the scene as a game-changing AI tool, and we award it an “A-” grade (8.5/10). It delivers exceptional capabilities on par with the best proprietary models, all while being freely accessible and open for anyone to use or improve. This combination of power and openness is unprecedented at this scale.

During our evaluation, DeepSeek repeatedly impressed us. It handled a diverse array of tasks – from composing creative stories and complex essays to solving programming puzzles and translating text – with a high level of competence. Particularly noteworthy was its performance in specialized scenarios: for coding assistance, DeepSeek not only generated functional code but explained it clearly, even debugging an error in our test snippet. For long-form content, its gigantic context window meant it could analyze a 50-page document we provided and give an accurate summary, something most other AI would choke on. These are tangible benefits that directly impact productivity for developers, writers, researchers, and more.

The community-driven nature of DeepSeek also means it’s evolving rapidly. We observed updates during our review period that improved output consistency and introduced fine-tuned modes (like a more conversational “assistant” style option). Bugs we noticed early on (like a slight formatting quirk in one of the chain-of-thought outputs) were quickly patched by contributors. This agile improvement cycle gives us confidence that DeepSeek will continue closing any remaining gaps with its rivals. In a way, using DeepSeek feels like being part of a movement – users and creators are collectively pushing forward what the tool can do week by week.

We must balance this praise with realistic caution on a few points, which is why we peg it at A- rather than a full A or A+. First, while DeepSeek’s quality is generally top-tier, it can occasionally stumble with hallucinations or niche knowledge gaps, as we discussed. It’s not infallible, and complex or highly domain-specific queries might require cross-verification. This is a common caveat for all AI models today, so it’s not a unique flaw of DeepSeek, but it’s something users should keep in mind.

Second, using DeepSeek to its full potential may require a bit more effort than using a polished commercial app. We anticipate that more user-friendly wrappers and interfaces will emerge (and perhaps by the time you read this, they already have), but as of now, the experience might be spartan for some. Early adopters and tech-savvy users will have no issues – in fact, many relish the control – but a non-technical user might need a guided solution on top of DeepSeek to feel at home.

However, neither of those points detracts significantly from DeepSeek’s overall value proposition. They are more like the growing pains of a fast-moving project. The core success of DeepSeek is evident: it has effectively lowered the cost barrier of advanced AI to zero without significantly lowering the quality bar. This is why we see it as “a formidable player in the AI language model space”, as one detailed review put it.

For businesses considering it, the allure is strong: with DeepSeek, you can implement AI solutions at scale and at cost, something unthinkable a year ago when only a few companies held such tech. For enthusiasts and professionals, it’s a chance to have a super-powerful AI sidekick without reaching for your wallet.

Our advice: If you haven’t tried DeepSeek yet, do so. Whether you’re currently using ChatGPT, Jasper, or any other tool, seeing DeepSeek in action will likely make you rethink what an “AI budget” should look like. The fact that it’s open means that if you encounter a quirk, you can even report it or see if others have a fix, a level of transparency that is refreshing.

In conclusion, DeepSeek AI is one of the most exciting developments in 2025’s AI landscape. It scores high on performance, extremely high on value, and fairly high on ease-of-use (with some room to grow as the ecosystem matures). It exemplifies how collaborative innovation can challenge established players and expand AI access for all.

Final Grade: A-. DeepSeek delivers on its promises and then some, making advanced AI more democratic and setting a new standard for what we should expect from AI tools in terms of both capability and accessibility. We’ll be keeping a very close eye on DeepSeek’s journey – but for now, it has certainly earned our strong endorsement as a top-tier AI tool that’s “worth considering” for businesses and developers looking for a powerful, cost-effective solution. The AI Tribune is optimistic about DeepSeek’s future and its potential to “reshape the global AI landscape” in favor of open innovation.