The year 2025 has seen rapid advancements in large language models, with industry leaders rolling out new versions and features at a blistering pace.
OpenAI’s GPT-4 has evolved (with a “Turbo” version and possibly successors like an O3 model), Google’s Gemini project has introduced multi-modal capabilities and massive context windows, and Anthropic’s Claude continues to push safer, longer conversations.
In this landscape of AI titans, DeepSeek – the open-source upstart that made headlines by matching GPT-4-level performance – is not sitting idle either.
DeepSeek’s latest updates (such as DeepSeek R1-0528) have improved it further, seeking to keep up with or even surpass the giants in certain areas.
In this article, we provide a comprehensive overview of how DeepSeek compares to the major AI models as of 2025, incorporating the latest developments.
We’ll look at OpenAI’s newest offerings, Google’s Gemini 2.5 series, Anthropic’s long-context Claude, and others – examining where DeepSeek stands strong and where it might lag behind.
OpenAI’s Evolving Models vs DeepSeek
OpenAI’s GPT-4 set the standard in early 2023, and by late 2024 they introduced GPT-4 Turbo, an enhanced version boasting 128K tokens context window and significantly reduced costs.
GPT-4 Turbo essentially allowed handling book-length inputs (128k tokens is about 96,000 words) and was optimized for faster, cheaper inference while maintaining similar quality. This was a clear response to competition (Claude’s 100k context and others).
Additionally, OpenAI hinted at or launched an intermediate model often dubbed “O3” (perhaps an internal codename). By 2025, there are references to an OpenAI O3 or GPT-4.1 series, which reportedly feature major improvements in coding, instruction following, and further long-context support.
Some sources describe an “O3-mini” model that OpenAI introduced – a smaller, cost-efficient model that still has strong performance and was used to undercut open-source offerings in price.
How does DeepSeek measure up? In early 2025, when DeepSeek R1 launched, it matched GPT-4 (OpenAI’s O1) on many benchmarks.
For example, R1 slightly beat OpenAI’s GPT-4 (1217 version) on the AIME math test and essentially tied on MATH-500. This was a shocking accomplishment for an open model.
However, OpenAI’s ongoing improvements mean the bar is rising. The rumored OpenAI O3 model (which might be an internal successor to GPT-4) appears to have reclaimed some lead. One comparative assessment noted that OpenAI’s O3-mini outperforms DeepSeek-R1 in most benchmarks, including reasoning, coding, and general tasks.
If accurate, this suggests OpenAI has fine-tuned its models to surpass R1’s level by mid-2025. O3-mini is also said to be highly efficient and much cheaper, potentially 15× cheaper than GPT-4 (O1) while offering comparable performance.
OpenAI also continuously works on safety; one comparison showed O3-mini had a far lower rate of unsafe responses than DeepSeek (which reflects OpenAI’s strength in fine-tuning alignment).
DeepSeek has not stood still either – the R1-0528 update (May 2025) brought notable improvements in reasoning and math.
It raised DeepSeek’s accuracy on the AIME 2025 exam from about 70% to 87.5%, a huge jump.
This indicates the DeepSeek team is fine-tuning and updating their model architecture to keep pace.
In coding, R1-0528 was evaluated with a LiveCodeBench score of 73.3%, which is extremely high; OpenAI’s GPT-4 (original) was around the 60-70% on similar coding metrics, so DeepSeek may still be on par in coding prowess.
OpenAI’s GPT-4 Turbo and any O3 variants also expanded multi-modal capabilities (GPT-4 with vision, etc.). DeepSeek R1 is currently text-only.
If a user needs image analysis or a combination of modalities, OpenAI holds an advantage thanks to the vision-enabled GPT-4.
DeepSeek might introduce such features in the future (perhaps in R2), but as of now it competes primarily on pure language tasks.
Context Window: GPT-4 Turbo’s 128k context versus DeepSeek’s 128k (R1) is roughly equal at face value.
However, OpenAI’s model has a lot of optimization in retrieving relevant info from that large context, whereas DeepSeek’s effective use of 128k context hasn’t been as publicly demonstrated.
Both can ingest very large texts, but users have reported that beyond a certain point, models might struggle to “remember” earlier content without specialized prompting. Here, OpenAI’s extensive testing might give GPT-4 Turbo a reliability edge in ultra-long prompts.
Still, the fact that DeepSeek can even play in this 100k+ token league is significant – it means for tasks like feeding in a lengthy research paper or multiple documents, DeepSeek can go head-to-head with GPT-4 Turbo in principle.
Cost: OpenAI did aggressive price cuts – they reduced GPT-4’s cost and introduced GPT-3.5 Turbo fine-tuned variants at lower prices in late 2023, and GPT-4 Turbo came with much lower pricing than the original GPT-4.
This was partly a reaction to open-source – making their API more attractive. DeepSeek’s API remains extremely affordable (claimed 90-95% cheaper than OpenAI’s original GPT-4 pricing)0
If OpenAI’s O3-mini offers near-DeepSeek performance at a low price, that directly challenges one of DeepSeek’s selling points (affordability). For now, though, DeepSeek is still likely cheaper in raw token cost.
Also, DeepSeek being self-hostable means if you have the hardware, you can run it without paying per token at all – something not possible with GPT-4 which is only via API.
Knowledge Cutoff and Updates: GPT-4’s original knowledge cutoff was 2021; GPT-4.1 (if that’s O3) might have a later cutoff (maybe late 2023 or 2024). DeepSeek R1’s knowledge cutoff isn’t explicitly stated, but since it launched in Jan 2025, it likely trained on data up to mid/late 2024.
This means both are relatively up-to-date, but OpenAI has the edge of plug-ins/tools and live retrieval integration in their ecosystem. DeepSeek doesn’t have a built-in browsing plugin, though users can supply information to it.
Verdict vs OpenAI: At the start of 2025, DeepSeek shocked by matching OpenAI’s best, but OpenAI’s subsequent releases (GPT-4 Turbo, O3 series) have pushed the bar higher.
DeepSeek R1-0528 keeps it competitive – for instance, on complex reasoning, it’s reported to be “near top-tier” with that 87.5% AIME score, which is probably in the vicinity of OpenAI’s model performance. It’s a leapfrog game: OpenAI introduces enhancements, DeepSeek updates to catch up. As of mid-2025, OpenAI still holds an edge in overall polish, safety, and multi-modality, and likely in some benchmark averages (per those O3 vs R1 reports).
Yet, DeepSeek remains very close in pure capability – close enough that for many tasks you’d get comparable results, especially in coding or math where it excels.
OpenAI’s strength is a more consistent and integrated platform, whereas DeepSeek’s strength is openness and cost. The competition is ongoing, and importantly, DeepSeek’s presence has forced OpenAI to improve and cut prices – a win for users.
Google’s Gemini: A New Challenger and How DeepSeek Compares
Google entered the fray in late 2024 with Gemini, a suite of models developed by DeepMind.
By 2025, the Gemini 2.5 series is the talk of the town, especially Gemini 2.5 Pro and Gemini 2.5 Flash.
These models are notable for a few reasons:
- Multimodal capabilities: Gemini is designed from the ground up to be multimodal. Gemini 2.5 Pro can accept not just text, but images, audio, and even video inputs. This means it can answer questions about images or perform tasks like transcribing and analyzing audio, etc. DeepSeek, in contrast, is currently text-only (focused on language tasks). In any scenario requiring multimodal understanding – e.g., “Describe what’s happening in this video and answer questions about it” – Gemini has a functionality advantage.
- “Thinking” and Tool Usage: Gemini models introduced a feature sometimes called “Dynamic Reasoning” or “chain-of-thought with tools”. For example, Gemini 2.5 Flash can show its step-by-step thought process for complex queries and adjust its reasoning depth on the fly. This is similar to giving the model an internal scratchpad or to how AutoGPT-like agents work. DeepSeek R1 also uses an internal reasoning (it “thinks before answering” by design), but Google has really emphasized this “deliberate reasoning” aspect in marketing Gemini. In practical terms, Gemini is very strong at multi-step logical reasoning and planning, arguably matching or exceeding GPT-4 in some logic puzzles. In one comparison, Gemini 2.5 Pro was said to excel in multi-step reasoning, often rivalling or surpassing GPT-4 and Claude. DeepSeek R1-0528 is itself great at reasoning (as seen in math/logic benchmarks), so it’s likely on par here. In fact, DeepSeek’s team cited that R1-0528’s complex reasoning was top-tier, which implies it could handle those tasks similarly. But with Gemini’s added ability to break down problems (and perhaps even use tools via Google’s ecosystem), it’s a tight race. A noteworthy claim from DeepSeek’s side: DeepSeek R1-0528’s performance in complex reasoning tasks is comparable to Google’s Gemini 2.5 Pro, as per internal testing. For instance, DeepSeek’s AIME score vs Gemini’s might be similar (Gemini 2.5 Pro reportedly scores ~71.0% on AIME 2024, according to one blog, whereas DeepSeek V3 scored 94% on AIME 2024 – though that difference is huge, it could be that the sources vary, or DeepSeek is simply much better at math contests). On other reasoning benchmarks like logical deduction or the ARC exam, both are at the frontier of performance.
- Context Window: Perhaps the most jaw-dropping spec: Gemini 2.5 Pro supports up to 1 million tokens context, with plans for 2 million. This dwarfs DeepSeek’s 128k and everyone else. Essentially, 1M tokens is like the model could ingest an entire book series or a large codebase repository in one go. Now, whether that’s practical or just theoretical is another matter – feeding 1M tokens is very expensive computationally. But Google is clearly pushing the boundary of “how much can an AI hold in its working memory.” In comparison, DeepSeek’s 128k is large but not unique (Claude and GPT-4 Turbo are in that range too). So Google currently leads in context length by an order of magnitude. For use cases like reviewing massive documents (think: an entire Wikipedia dump or a multi-hundred-thousand-line codebase), Gemini could be a game-changer. DeepSeek cannot compete at that extreme scale presently. However, for most realistic tasks, 128k is already sufficient (that’s ~100 pages of text). So unless one specifically needs that million-token ability (some enterprise might, for huge datasets analysis in one prompt), this is more about Google’s forward-looking tech showcase. It does signal that Google invested heavily in memory and may have special retrieval techniques to utilize it effectively.
- Performance Benchmarks: On standard NLP benchmarks, how does Gemini stack up? There are mixed reports. Some leaderboards (like lmsys Chatbot Arena) put DeepSeek V3 slightly ahead of Gemini 1.5 and around the level of Gemini 2 in head-to-head chat battles. But with Gemini 2.5 Pro, Google regained a top spot in many areas. According to one comparison (PromptHackers), DeepSeek R1 achieved 90.8% on MMLU, whereas Gemini 2.5 Pro scored 81.7%. This implies DeepSeek actually had higher knowledge test performance (MMLU) than that version of Gemini. It’s plausible because DeepSeek’s sheer size might give it an edge in knowledge recall. On coding, the Codersera analysis showed Gemini 2.5 Pro and DeepSeek R1-0528 are close: LiveCodeBench Pass@1 was 73.3% for DeepSeek vs “comparable, often slightly higher” for Gemini (meaning Gemini sometimes a bit above 73% in their tests). So both are excellent coding models. It was noted that Gemini excels in generating clean, correct code with fewer errors, and DeepSeek was “nearly on par” in real-world coding feedback. Essentially, Gemini 2.5 Pro and DeepSeek R1 are neck-and-neck in many technical tasks – each might win some, lose some. DeepSeek might have a slight advantage in pure math (given its fine-tuning) and perhaps knowledge recall, while Gemini might be better at structured outputs and using context/tooling, given Google’s focus.
- Enterprise Integration: Google is integrating Gemini into its cloud (Vertex AI, etc.), meaning businesses can use Gemini with relative ease if they are in Google’s ecosystem. This includes features like Generative AI Studio, APIs on GCP, etc. DeepSeek is offered via its own platform and API, but it’s not as deeply integrated into enterprise software stacks yet (understandably, as Google has that advantage). Google also has fine-tuned versions (like domain-specific Gemini models, and their Gemma smaller models for different scales). For a company already using Google’s services, adopting Gemini might be a no-brainer over trying an open-source model, unless cost or data privacy (self-hosting) is a priority.
Verdict vs Google: Google’s Gemini 2.5 Pro is arguably the most feature-rich and versatile model as of 2025 – with unmatched context length and multimodal input, it has capabilities DeepSeek doesn’t. In terms of raw NLP performance, DeepSeek R1 is highly competitive with Gemini. Some metrics favor DeepSeek (e.g., MMLU knowledge, possibly certain coding/test scores), while others might favor Gemini (e.g., maybe some reasoning or interactive tasks, and anything involving multi-modal or extremely long input).
A direct side-by-side (as done in Codersera) basically concluded that DeepSeek R1-0528 and Gemini 2.5 Pro are both cutting-edge, each with strengths: DeepSeek is open, efficient on smaller hardware (relative to its size) and stellar in math/coding, whereas Gemini is more versatile (handles text+images+audio) and has that massive context and tight integration with tools.
One telling line from that comparison: DeepSeek is nearly closing the gap with Western frontier models, while Gemini sets the standard for versatility and scale.
We can expect Google to keep iterating (a “Gemini 3” or further improved versions later in 2025). DeepSeek will likely aim to incorporate more of those features (maybe multimodal in a future release, etc.) to not be left behind.
But as of now, if a client said “I have a huge dataset of mixed media and I need an AI to analyze it in one go”, Gemini would be the answer.
If they said “I need the absolute best coding/math AI and want to host it myself”, DeepSeek would be a prime answer.
Anthropic’s Claude and Others: Long Context and Safety
We covered DeepSeek vs Claude earlier in detail, but focusing on the 2025 landscape: Anthropic released Claude 2 in July 2023 with the 100k context window and improved performance.
By 2025, they have experimented with Claude 2.1 and beyond, and there are references to Claude 3 (with variants named Claude 3.5, Claude 3.7 in some benchmarks).
Anthropic also introduced Claude Instant 100k (a faster, cheaper but somewhat less capable version). Their niche is very long conversations and safety.
Claude can handle 100k tokens reliably, enabling workflows like analyzing lengthy financial reports or even multiple documents together.
DeepSeek’s standing: DeepSeek also offers very long context (again, ~128k), so it meets Claude on that front.
And as previously discussed, DeepSeek generally outperforms Claude 2 in raw task performance – especially in coding and specialist benchmarks.
In 2025, unless Anthropic made a big leap with a Claude 3, DeepSeek likely still has an edge on things like math and code accuracy.
In fact, internal tests from late 2024 indicated DeepSeek’s models beat Claude v3 in certain coding tests, and anecdotally users note Claude’s weakness in code compared to GPT-4 or DeepSeek.
Claude’s big advantages are safety and reliability: Anthropic maintains a low hallucination and high harmlessness focus.
For organizations worried about AI going off-script or producing disallowed content, Claude is often seen as the safest bet (Anthropic markets it that way, and tests confirm it’s reluctant to violate guidelines).
DeepSeek, being open, doesn’t have those guardrails unless a user adds them.
One should mention Meta’s contributions here too: While not a paid competitor like OpenAI/Google/Anthropic, Meta did release LLaMA 3 in late 2024 (hypothetically; there are signs of some LLaMA 3 or improvements).
And recently, Microsoft (with OpenAI partnership) might have exclusive models like GPT-4.5 or specialized ones via Azure.
But on the public stage, the main “giants” are OpenAI, Google, Anthropic – with DeepSeek as the open outsider punching above its weight.
In summary for Anthropic/others: DeepSeek stands above Anthropic’s Claude in many technical aspects (accuracy, coding, etc.), but below it in things like aligned safe behavior (Claude is less likely to output something problematic).
Depending on the application, that could tilt preference either way.
The New Landscape: Competition Driving Innovation and DeepSeek’s Role
By mid-2025, the competition among AI giants has clearly intensified:
- OpenAI is not resting on GPT-4 laurels – iterative GPT-4 Turbo, GPT-4.1/O3 models show improved performance and larger context.
- Google’s Gemini has leapfrogged in context and multimodality, setting new benchmarks for what AI can do in a single model.
- Anthropic continues to push the envelope on context length and trustworthiness with Claude, ensuring a niche of users who need high safety and long documents are covered.
- Meta and others are likely preparing their next moves, possibly integrating their models (like LLaMA) into more products or releasing bigger ones.
DeepSeek’s impact on this race is evident. Its emergence as an open alternative forced incumbents to respond.
OpenAI’s policy and pricing changes (e.g., making their models cheaper and more accessible) came as models like DeepSeek started threatening their dominance. Google, seeing a viable open-source challenger, might have accelerated Gemini’s public release or scaled up context to differentiate further.
There’s even a geopolitical angle: DeepSeek, being from a Chinese startup and open-sourced globally, spurred discussions in the US about competitiveness and export controls.
In late 2024, OpenAI’s policy head specifically cited companies like High Flyer (DeepSeek’s parent) as a reason to support U.S. AI efforts lest Chinese models catch up.
This competitive pressure is benefiting end users with faster model improvements and lower costs.
Where DeepSeek Stands (Strengths & Weaknesses in 2025):
Strengths:
- Top-tier reasoning and coding: DeepSeek R1 is at or very near the level of the best (GPT-4/Gemini) in complex reasoning, math, and coding tasks. It’s essentially proven that with enough training, an open model can reach those heights.
- Openness and Community: DeepSeek remains the only model of that caliber that’s open-source (MIT license). This means researchers and companies can fine-tune it, examine it, and deploy it without fear of usage limits. It also means community-driven enhancements can feed back into it – we’ve seen many derivatives and fine-tunes already.
- Rapid iteration: The DeepSeek team showed they can update the model quickly (R1-0528 came just a few months after R1’s release), giving notable gains. This agility in open-source development means DeepSeek can integrate cutting-edge techniques (like any new optimization or training method) without corporate red tape.
- Cost disruption: DeepSeek drastically undercuts the giants in price for similar performance, which pressures everyone to not overcharge. For many, trying DeepSeek via its free chat or low-cost API is a no-brainer, when GPT-4 might be gated or expensive.
Weaknesses / Gaps:
- No multimodal ability (yet): Unlike GPT-4’s vision or Gemini’s full multimedia input, DeepSeek can’t natively process images or audio. This is a growing part of AI use-cases (like analyzing a screenshot or listening to a meeting recording for summary). DeepSeek will need to address this, potentially in a future release or through integration with other tools.
- Higher resource requirements: Using DeepSeek at full capacity isn’t trivial for the average user. GPT-4 and Claude are accessible via simple API calls – all the heavy lifting is on OpenAI/Anthropic’s side. To use DeepSeek beyond the provided API, one might need to spin up multi-GPU servers or be content with slower distilled versions. This limits some adoption; not every company wants to manage GPU clusters. The giants have the advantage of offering a turnkey solution (cloud APIs). That said, companies that do have resources may prefer self-hosting for privacy.
- Fine-tuned specialties: The big companies are tailoring models for specific domains (OpenAI fine-tuned GPT-3.5 for certain functions, Google has code-focused or dialog-focused variants of Gemini, etc.). DeepSeek is a generalist (with perhaps a coder variant). It lacks the array of specialized models that a large org can maintain (e.g., OpenAI’s new “nano” model for quick tasks, or different sized GPT-4 for real-time applications). DeepSeek might rely on the community to fill this gap (people fine-tune R1 for medicine, for law, etc., and share those).
- Safety and support: As mentioned, DeepSeek doesn’t come with a dedicated support team or an alignment guarantee. Enterprises might be wary of deploying it in customer-facing roles without thorough testing, whereas Claude or Azure OpenAI come with some assurances and documentation about behavior. DeepSeek’s origin in China also means the default model has some content restrictions (e.g., it avoids certain political topics), which could be a pro or con depending on the user – but it’s something to be aware of.
The Road Ahead and Trends:
It’s clear that as of 2025, no single model “dominates” absolutely.
We’re in a scenario where for any given task, one might have a slight edge, but there’s healthy competition:
- For extreme context length and multimodal – Google Gemini 2.5 is setting the pace.
- For strict safety and reliability – Anthropic Claude leads (with OpenAI also strong due to RLHF).
- For overall reasoning prowess (especially closed models) – GPT-4 and its successors remain incredibly strong, arguably still the benchmark in many minds, but Gemini and others are very close.
- For open-source and cost-effective deployment – DeepSeek R1 is the leader.
We might soon see GPT-5 or Gemini 3 – and possibly DeepSeek R2. OpenAI’s Sam Altman had suggested GPT-5 isn’t immediately in the works, but rather incremental upgrades.
However, given how things are advancing, a big architecture jump might come late 2025 or 2026.
Google will likely push further on multimodality and integration (their models could be inside all Google products).
Anthropic might release a Claude with even more reasoning or a larger model (Claude has been rumored to run on upwards of 100B+ params, but they focus on efficient training).
Where does this leave DeepSeek? DeepSeek has carved out a seat at the table of AI giants by virtue of performance, even if it doesn’t have the big corporation backing.
Its existence ensures that the frontier of AI knowledge isn’t confined behind corporate walls. It also acts as a check – for example, if OpenAI decided not to increase GPT-4’s context due to cost, Claude and DeepSeek doing so forced them to match it with GPT-4 Turbo.
If giants neglect certain languages or markets, an open model can step in (DeepSeek is multilingual and accessible globally).
The DeepSeek team will likely focus on:
- Closing any remaining quality gaps (to match the very best of OpenAI/Google on all benchmarks).
- Possibly reducing model size or increasing efficiency (to make it more accessible).
- Adding features like multimodality or tool use (so it’s not left behind in those dimensions).
- Continued open releases (maybe an R2 with even more capabilities, or smaller models that are still very strong, etc.).
One intriguing point: an analysis on global impact noted that DeepSeek’s efficient training (reportedly only ~$6 million cost) and open release caused some ripples (like even affecting Nvidia’s stock temporarily).
This highlights how a relatively small player can cause outsized reactions when it challenges assumptions (e.g., that only hundreds of millions of dollars and secret data can produce a top model).
It democratizes AI development and could lead to more innovation outside Big Tech.
Conclusion
In the 2025 AI arena, DeepSeek holds its own among giants. It may not have the vast resources of OpenAI or Google, but through ingenuity and openness it has reached a performance tier that commands respect.
DeepSeek is not the single best at everything – if you compare it to each rival:
- OpenAI’s latest might edge it out in some benchmarks and has more bells and whistles (tools, plugins, etc.).
- Google’s Gemini surpasses it in context capacity and multimodal inputs.
- Anthropic’s Claude offers a safer, more controlled AI for sensitive deployments.
However, DeepSeek is within striking distance on core capabilities of each: it’s as smart a problem-solver as any, with a context window larger than most, and an unmatched open usage model.
A tech commentator might say, “DeepSeek is effectively an open GPT-4-level model” – and that was a near unthinkable idea a couple of years ago. Its presence ensures that the major AI labs cannot be complacent.
Users, whether individual tinkerers or enterprises, now have an alternative path if the closed providers falter or overcharge.
For a user deciding “Which AI model should I use in 2025?”, the answer can be nuanced:
- Use DeepSeek if you value transparency, control, and top-tier reasoning/coding ability without relying on a vendor. It’s perfect for research, for customizing to your domain, or for deployment where you want full control (and to save costs).
- Use GPT-4/GPT-4 Turbo (OpenAI) if you need a highly reliable general model with broad knowledge, and especially if you need things like vision input or guaranteed support. It’s still arguably the gold standard for many general tasks, with a slight quality edge kept through continuous tuning.
- Use Google Gemini 2.5 if your task involves huge data or multiple data types, or if you’re already on Google Cloud and want easy integration. It’s the most “ambitious” in scope with its million-token context and multimodal knack.
- Use Anthropic Claude if your priority is a model that can read very long text and remain super aligned/safe. It’s a workhorse for long documents and sensitive use cases, albeit slightly weaker in raw performance.
In practice, many organizations might employ a mix. For instance, a company could use DeepSeek internally for data analysis (leveraging its power without sharing data externally), but use OpenAI’s or Google’s service for customer-facing chat where uptime and support are crucial.
This multi-model strategy is becoming feasible as more models reach high capability levels.
One thing is certain: the fierce competition in 2025 means rapid advancements. Every few months, we see improvements or new versions (like GPT-4 Turbo, Claude 100k, Gemini Flash, DeepSeek updates, etc.).
The question “Where does DeepSeek stand?” is almost a moving target – but as of now, it stands tall as a peer to the best models out there, with its unique open-source twist.
By maintaining its momentum and possibly addressing its current weaknesses, DeepSeek could either become the de facto open platform that others build on or push the boundaries even further in R2.
Meanwhile, OpenAI, Google, and Anthropic will surely keep raising the bar.
Ultimately, the biggest winner in this competition is the end-user – enjoying better AI capabilities, more choice, and declining costs.
And DeepSeek has been a catalyst in that positive trend, ensuring the giants never ease up.
