GPT-5, Gemini 2.5 Pro, Grok 4 & Claude Opus 4 — 2025 AI Model Comparison
- Kimi
- Aug 8
- 32 min read
Overview: These four models represent the cutting edge of large language models as of 2025. GPT-5 (OpenAI), Gemini 2.5 Pro (Google DeepMind), Grok 4 (xAI/Elon Musk), and Claude Opus 4 (Anthropic) are all top-tier AI systems. Below is a detailed comparison across five key dimensions: reasoning ability, language generation, real-time/tool use, model architecture/size, and accessibility/pricing.
Quick Comparison Table
Aspect | GPT-5 (OpenAI) | Gemini 2.5 Pro (Google) | Grok 4 (xAI) | Claude Opus 4 (Anthropic) |
Reasoning & Coding | Excellent logic & math; top-tier coding. Achieved 94.6% on a major math test and ~74.9% on a coding benchmark. Uses adaptive “thinking” mode for tough problems. | State-of-the-art reasoning; strong coding. Leads many math/science benchmarks. Excels at handling complex tasks and code generation with chain-of-thought reasoning built-in. | Highly analytical; trained for deep reasoning. Uses massive RL training to solve problems and write code. Real-time web/search integration keeps knowledge up-to-date. Insightful in analysis, often catching details others miss. | Advanced problem-solving; coding specialist. Designed for complex, long-running tasks and agentic coding workflows. Anthropic calls it the best coding model, with sustained reasoning over thousands of steps. |
Language Generation | Creative and coherent. Produces fluent, well-structured text with improved factual accuracy. Demonstrates complex stylistic tricks (e.g. acrostics) and far fewer hallucinations than prior models. | High-quality and context-aware. Writing style is polished (ranked #1 in human preference tests). Natively handles nuance across modalities, though sometimes cautious in tone. Excellent clarity and grammar. | Witty and engaging style. Known for a slightly contrarian, humorous voice while remaining knowledgeable. Outputs are insightful but can be less polished than others. Still maintains strong coherence and correct grammar. | Clear, human-like writing. Outputs feel natural and professional, avoiding robotic tone. Follows instructions precisely and rarely refuses valid requests (safety improved to reduce unwarranted refusals). Consistently high grammar and nuance understanding. |
Tools & Real-Time | Extensive tool integration. Built-in web browsing/search reduces factual errors (~45% fewer mistakes with search on). Supports code execution and plugins/function-calling; can work with images and voice (multimodal input) for analysis. Adapts its “thinking” to use tools when needed. | Multimodal and tool-enabled. Natively accepts text, code, images, audio, video as input (1M-token context). Supports Google Search grounding and code execution functions, enabling real-time info retrieval and running code. Can output spoken responses in a natural voice and use external APIs via function calls. | Designed for autonomy. Trained to decide when to use external tools. It can run a Python interpreter or issue web queries by itself. Has real-time access to the web and X (Twitter) – it actively searches current information, giving it up-to-the-minute knowledge. Also accepts image/voice input (“Voice Mode” lets it see via camera and converse by voice). | Tool-augmented reasoning. Features an “extended thinking” mode where it alternates between reasoning and tool use. Can perform web searches and even execute code (Anthropic’s API provides a code exec tool). Large 200k-token context allows it to ingest lengthy documents, and it can integrate with developer-provided files for long-term memory. (Primarily text-based; image understanding is not a core feature publicly, aside from what can be achieved via its text tools.) |
Architecture & Size | Unified dual-model system. GPT-5 isn’t a single model but an adaptive architecture: a fast lightweight model for simple queries and a “GPT-5 Thinking” model for complex tasks, managed by an intelligent router. This allows dynamic allocation of reasoning effort. It supports an expanded 272k-token input context and is fully multimodal. OpenAI hasn’t disclosed parameter counts, but GPT-5 is their largest and most advanced model, offered alongside smaller variants (gpt-5, -5-mini, -5-nano) for efficiency. | Next-gen Google model, massive context. Gemini 2.5 Pro is built on Google’s latest LLM architecture, succeeding PaLM 2 and LaMDA. It features a 1,048,576-token context window (with 2 million-token support planned), enabling analysis of very large texts. It’s inherently multimodal and uses “thinking” (chain-of-thought) by default. Google hasn’t confirmed param size, but speculation suggests a mix of very large dense and sparse (Mixture-of-Experts) components, possibly on the order of trillions of parameters (not officially verified). Multiple tiers exist (Flash, Pro, etc.), with Gemini 2.5 Pro being the top model for complex reasoning. | Reinforcement learning at scale. Grok 4 builds on a transformer-based foundation extensively refined via reinforcement learning. xAI employed a 200k-GPU supercomputer (“Colossus”) to train Grok 4’s reasoning on an unprecedented scale, incorporating vast data (math, coding, and beyond). It supports a 256k-token context and is inherently a “reasoning model” (no non-thinking mode). Grok 4 comes in standard and a “Heavy” version – the latter likely runs at higher precision or larger scale for even better performance. The model is multimodal (text, image, audio input) and has an expressive voice output. | Hybrid modes with huge context. Claude 4 consists of Opus 4 (the high-power mode) and Sonnet 4 (efficiency mode) in a unified system. Both share a massive 200,000-token context window and can switch between near-instant responses and extended, chain-of-thought reasoning. Anthropic hasn’t revealed parameters, but Opus 4 is described as their most advanced model to date. It’s optimized for complex planning and coding, suggesting a very large model possibly comparable to GPT-4/5 in scale. Architectural innovations (like better long-context handling and safety layers) allow it to maintain coherence over hours-long sessions. |
Access & Pricing | Widely available. ChatGPT now uses GPT-5 as the default model. Free users can access GPT-5 (with some limits), while paid Plus users get higher message limits, and Pro subscribers unlock GPT-5 Pro (the most rigorous reasoning mode). Via API, GPT-5 is priced at $1.25 per 1M input tokens and $10 per 1M output tokens – notably cheaper per-token than GPT-4 was. This makes GPT-5 accessible to developers for fine-tuning and integration. | Subscription-based access. Google offers Gemini 2.5 through its services: for consumers, the Gemini app and integrations in Google products (Docs, Search, etc.) provide limited free use of 2.5 (Flash/Pro) and full access for subscribers. The Google AI Pro plan ( | Premium and API model. Grok 4 is offered through xAI’s Grok chatbot platform and API. It requires an X (Twitter) Premium+ or SuperGrok subscription – Elon Musk has bundled AI access with Twitter’s paid tiers. The exact consumer pricing of Premium+ is moderate (e.g. in the tens of dollars per month), whereas SuperGrok Heavy (which grants access to the strongest “Grok 4 Heavy” model) costs $300/month. Developers can also access Grok 4 via the xAI API with usage-based fees (approximately $3 per 1M input tokens and $15 per 1M output tokens, matching GPT-4’s former pricing). There is no fully free version of Grok 4, though earlier Grok 3 models had limited trials. | Enterprise and cloud access. Anthropic provides Claude 4 through its API and partners. Claude Opus 4 is generally reserved for paying customers: it’s available to organizations via the Anthropic API, AWS Bedrock, and Google Cloud Vertex AI. Anthropic’s own platform (claude.ai) offers the lighter Claude Sonnet 4 to free users, but Opus 4 (and the extended “thinking” mode) are included only in premium plans (Claude Pro, Enterprise, etc.). Pricing for Opus 4 is the highest among these models – around $15 per 1M input tokens and $75 per 1M output tokens on the API – reflecting its specialized coding prowess. (Sonnet 4 is cheaper at $3/$15 per 1M tokens.) |
1. Reasoning Ability (Math, Logic, Coding)
GPT-5: Built as a “reasoning-first” system, GPT-5 excels at complex logic, mathematics, and programming tasks. It introduces an adaptive reasoning mode – difficult queries automatically invoke a deeper “thinking” process. This leads to superior performance on benchmarks: GPT-5 scores 94.6% on the 2025 AIME math competition (no tools) and sets a new standard on coding tests, achieving ~74.9% on SWE-bench (a software engineering benchmark). These scores slightly surpass Claude Opus 4’s coding results (Anthropic reported Opus 4 at ~72.5% on SWE-bench), indicating GPT-5’s coding logic is state-of-the-art. It also significantly improved scientific reasoning, with GPT-5 Pro scoring 88.4% on the extremely hard GPQA science questions. In practice, GPT-5 can handle multi-step problems in math and logic with ease, using chain-of-thought internally to break down tasks. Users can even explicitly prompt it to “think carefully,” though it often does so automatically. Overall, GPT-5’s reasoning is robust – a leap forward from GPT-4 – making it adept at everything from formal proofs to debugging code.
Gemini 2.5 Pro: Google’s Gemini 2.5 Pro is likewise at the cutting edge of reasoning and coding. It was introduced as “our most intelligent AI model… showcasing strong reasoning and code capabilities”. In fact, the experimental Gemini 2.5 Pro debuted at #1 on human preference leaderboards and “leads common coding, math and science benchmarks.”. Google DeepMind enhanced Gemini with a “thinking” approach: the model internally reasons through problems (via techniques like chain-of-thought) before responding. This yields excellent logical consistency and problem-solving. For coding, Gemini 2.5 made a “big leap over 2.0” – it excels at generating and editing code, even for complex web apps and “agentic” coding tasks. On SWE-Bench (agentic coding benchmark), Gemini 2.5 Pro scores ~63.8% with its own agent setup. While that is a bit behind GPT-5 and Claude’s latest, it’s still among the top scores, and Gemini’s strength lies in leveraging its huge context – it can, for example, ingest an entire codebase or lengthy technical paper (up to 1M tokens) and reason about it holistically. In practical terms, Gemini’s advantage is handling very large or complex inputs without losing track. One reviewer found it could answer questions about page 180 of a document by recalling details from page 15 – a “game-changer for large documents.” Its math and logic abilities are also top-tier (on par with GPT-5 in many evaluations), making Gemini 2.5 Pro a powerhouse for complex analytical tasks.
Grok 4: xAI’s Grok 4 has rapidly advanced in reasoning due to its unique training focus. Grok 4 was refined with massive-scale reinforcement learning specifically to improve multi-step thinking. As a result, its problem-solving skills are excellent – xAI claims it is “the most intelligent model in the world,” having “unparalleled world knowledge and performance.” On challenging reasoning benchmarks, Grok 4 is positioned as at least on par with OpenAI and Google’s models. Early tests praised its ability to break down problems and even push back with correct logic, rather than just agreeing, demonstrating strong logical faculties. In coding, Grok 4 is very capable as well: it was trained on extensive coding data and can not only generate code but also use a built-in code interpreter to verify or debug its outputs. Some independent comparisons note that Grok 4 is extremely strong in code – one analysis found it generally on par with Claude Opus 4 in coding tasks and clearly ahead of Gemini 2.5 in that domain. Its reinforcement learning on coding tasks means it can iteratively improve its solutions. One caveat: Grok sometimes adopts a distinctive style in reasoning – described as “a slightly sarcastic research assistant” – which can be insightful but also willing to question premises. This can actually lead to deeper reasoning on open-ended problems. In summary, Grok 4’s math, logic, and coding prowess are cutting-edge, bolstered by real-time data access (it learns from live information) and a training regimen explicitly aimed at thinking through tough problems rather than just predicting the next word.
Claude Opus 4: Anthropic’s Claude Opus 4 is renowned for reasoning and especially for programming tasks. It was introduced as “the world’s best coding model” with “advanced reasoning, and AI agents” in mind. Opus 4 can sustain very lengthy chains of thought, working for hours if needed to tackle complex, multi-step problems. Its reasoning ability is reflected in benchmark wins: for example, it leads on the SWE-bench coding challenge (72.5% pass rate) and achieves state-of-the-art results on other agentic reasoning tests like Terminal-Bench. Claude 4 was built to reason in a more “agent-like” way, meaning it plans and monitors its progress on tasks. It has two modes – instant and extended thinking – and in the extended mode it can iteratively analyze, use tools, and refine answers for complex logic puzzles or difficult queries. This makes its logical reasoning very robust; it handles tricky tasks that stump many models. In mathematics, Claude’s performance is strong (though perhaps a notch below GPT-5 on the very latest math benchmarks – e.g., Claude Opus scored around 75.5% on AIME 2025 single-shot, versus ~88-92% for GPT models). However, Claude particularly shines at structured reasoning such as legal or scientific analysis, where it needs to maintain accuracy and consistency over long documents or dialogues. Its coding capabilities deserve special mention: Opus 4 can manage entire codebases, perform step-by-step debugging, and even improve code quality autonomously. Partners like GitHub and Replit reported that Claude 4 significantly improved at handling multi-file projects and making coherent code edits across many files. In essence, Claude Opus 4’s reasoning and coding are at the very top tier – it approaches tasks like a “thoughtful senior engineer,” reasoning carefully and methodically through every step.
2. Language Generation (Creativity, Coherence, Grammar)
GPT-5: OpenAI’s GPT-5 is widely regarded as a highly creative and coherent text generator. It can produce everything from succinct answers to elaborate stories or technical documents with excellent grammar and fluency. Thanks to reduced hallucination rates and a new “Safe Completions” system, GPT-5’s outputs are not only well-formed but also more factually accurate and on-topic than previous models. In terms of creativity, GPT-5 has shown remarkable ability to follow complex stylistic instructions. For instance, it was demonstrated writing a clever paragraph where each sentence grew by one word and the initials spelled out a hidden message – all while remaining readable and in a consistent voice. This indicates a mastery of nuanced literary tricks and structure. GPT-5 tends to maintain a neutral, helpful tone by default (less “sycophantic” than before, per OpenAI’s tests), but it’s easily steerable into various personalities or styles when prompted (OpenAI even offers preset “personas” like Cynic or Nerd to showcase its style range). Its coherence in long outputs is excellent – it can stay on narrative or argument threads over very lengthy essays, thanks in part to its large context and internal chain-of-thought management. Overall, GPT-5’s language generation is creative, coherent, and correct. It is adept at producing imaginative content (stories, poems, brainstorming ideas) and usually employs perfect grammar and a natural flow. If anything, GPT-5 can sometimes be too verbose or too eager to be helpful, but OpenAI mitigated this by allowing more user control over verbosity and tone. In summary, GPT-5 sets a high bar for generating text that is both creative and reliable.
Gemini 2.5 Pro: Gemini 2.5 Pro is also a superb language generator. One hallmark of Gemini’s output is “high-quality style” – Google notes that human evaluators preferred its responses, indicating they found them well-written and clear. Indeed, Gemini 2.5 topped the LMArena human preference leaderboard, reflecting strong coherence and naturalness in its replies. The model is capable of multilingual and multimodal context handling (24 languages in voice, and understanding images, etc.), which can enrich its responses with broader context. For pure text tasks, users report that Gemini’s writing is polished and organized. It’s very good at maintaining context over long discussions (thanks to the huge context window) – meaning the narrative or explanation it produces won’t easily drift or contradict itself even across many paragraphs. In creative writing, Gemini can produce stories, dialogues, or scripts effectively; some users have experimented with it for novel-writing and found it capable of autonomously structuring chapters and scenes in a logical flow. Its grammar and diction are typically excellent. If instructed, it can emulate styles or formats (technical report, casual blog, etc.) accurately. One minor observation is that Gemini sometimes adopts a cautious or neutral tone, likely due to Google’s guardrails – it may hedge statements or include polite caveats more than, say, GPT-5. Anecdotally, this is described as a “corporate” or formal streak in its voice. However, this also means it’s less likely to output something inappropriate. In terms of creativity, Gemini is certainly capable (it can brainstorm and invent content well), but some reviewers feel models like GPT or Claude have a slight edge in raw creativity/“spark.” On the other hand, Gemini’s strength is synthesizing information – for example, writing a summary that elegantly weaves together data from a 100-page report is a task it handles gracefully. Summing up, Gemini 2.5 Pro produces very coherent, well-structured, and contextually rich text. It meets professional standards for grammar and clarity, and with the right prompts, it can be quite imaginative as well.
Grok 4: Grok 4’s language generation has a bit more personality than the others. Elon Musk’s xAI designed Grok to have a witty, somewhat irreverent style – reportedly inspired by the snark of Douglas Adams’ character tone – while still delivering accurate content. In usage, Grok often comes across as a “brilliant contrarian” or a highly analytical friend with a dry sense of humor. For example, when asked to critique something, it might not shy away from pointing out fundamental issues (even if not directly asked), which can be refreshing. This style can enhance creative tasks; Grok might produce a more novel or bold answer rather than a generic one. Importantly, Grok 4’s responses remain coherent and on-topic – the humor is usually balanced and it doesn’t derail the conversation. Its grammar and fluency are on par with top models; it produces natural-sounding, well-punctuated sentences. Grok is also capable of creative writing, and thanks to its training on social media and web content, it has a broad sense of modern lingo, cultural references, and idioms. This can make its storytelling or dialogue feel very current. One thing to note: Grok’s outputs might occasionally be a bit less filtered, as Musk’s philosophy was to allow more controversial or edgy content (within limits). The built-in result is that Grok might make jokes or take angles that other corporate models avoid, which can be either a perk or a downside depending on the user. However, it does follow user instructions and can adopt formal tones if asked. In terms of pure creativity, Grok 4 can certainly generate imaginative content – it even has a “voice mode” that can sing or role-play characters with distinct voices, adding a literal creative dimension. Its longer outputs maintain structure well, though some users have found it slightly less structured than Gemini or Claude for very long essays (possibly because Grok may prioritize interesting content over strict formality). All told, Grok 4 provides engaging, coherent, and often entertaining language generation, making it stand out with a bit of attitude while still delivering quality writing.
Claude Opus 4: Claude Opus 4 is often praised for producing exceptionally clear and human-like prose. Anthropic has always tuned Claude models to be helpful, harmless, and honest, and with Opus 4 they also greatly improved its “taste” and adherence to instructions. The result is that Claude’s writing sounds like it was composed by a thoughtful human expert. It excels at capturing nuances of tone – for instance, it can draft a sensitive email or a persuasive essay that feels polished and polite. In fact, one analysis called Claude Opus 4 “the most tasteful coder and writer” among frontier models. It avoids rambling and stays on point. Grammar and vocabulary are top-notch; it rarely makes basic errors. Claude 4’s coherence is superb even in very long documents – it uses its large context to remember details and maintain consistency (e.g. using the same terminology or style throughout a 20-page report). This makes it especially good for long-form writing like proposals, technical documentation, or fiction with complex continuity. Creativity-wise, Claude can be very imaginative when prompted (it can write stories, poems, etc.), though it tends toward a measured, earnest style unless instructed otherwise. It might not crack jokes as readily as Grok, but it often provides more detailed and structured creative content. For example, if asked to write a short story, Claude will likely produce a well-organized narrative with a clear beginning, middle, and end, and rich descriptions – perhaps more traditional in style compared to GPT-5’s sometimes offbeat creativity. An area Claude truly shines is instruction following and refinement: if you say “write this paragraph more formally” or “elaborate on point 2,” Claude does so with precision, adjusting tone and detail gracefully. Also, Anthropic reduced refusal/hallucination issues in Opus 4, so it very rarely gives an irrelevant answer or unjustified “I can’t do that” – it tries to comply in a safe way. In summary, Claude Opus 4 produces language that is professional, coherent, and nuanced. It’s like an extremely competent writer/editor that you can rely on for high-quality output across creative and analytical tasks.
3. Real-Time and Tool-Using Capabilities
GPT-5: OpenAI endowed GPT-5 with robust tool-use and real-time capabilities. It has a built-in web browsing/search function (in the ChatGPT interface, this appears as the ability to search the web when answering questions). With search enabled, GPT-5 is 45% less likely to make factual errors compared to its predecessor, since it can retrieve up-to-date information. This means for questions about current events or specific data, GPT-5 can fetch answers in real time. In addition, GPT-5’s API supports a flexible function calling mechanism: developers can define tools (for example, a calculator, a database lookup, or an image generator) and GPT-5 can invoke these tools by outputting a JSON or even plain text command. OpenAI reports that custom tools can now be triggered with free-form text (not just strict JSON), making integration more natural. For coding, ChatGPT with GPT-5 integrates an execution environment (successor to the GPT-4 “Code Interpreter” / Advanced Data Analysis). This lets GPT-5 write and run code to solve problems – essentially a sandboxed Python environment it can use. It greatly enhances math, data analysis, and code debugging tasks, as GPT-5 can iterate by actually executing code and seeing results. Multimodal abilities are also present: GPT-5 accepts image inputs (e.g. you can upload a picture and ask questions about it) and can handle audio (it can transcribe or analyze sound, and it can output synthesized speech in some clients). These were introduced in GPT-4 and further improved – e.g., GPT-5 scored ~84% on a multimodal understanding benchmark (MMMU). GPT-5’s large context (272k tokens input) also plays a role in tool use: it can digest entire books or long web pages as “tools” for knowledge. In the ChatGPT application, GPT-5 feels very agentic – it will often suggest actions (like “Shall I search for more info on X?”) and can carry out multi-step tool use plans automatically. Overall, GPT-5 is equipped to use the internet, run code, and integrate with plugins, making it a versatile AI assistant that can act on the world’s information in real time.
Gemini 2.5 Pro: Google’s Gemini is inherently designed to be tool- and data-aware. In fact, Gemini 2.5 Pro lists “Grounding with Google Search” and “Code execution” among its supported capabilities. In practical terms, this means Gemini can perform live web searches when needed – for example, if asked about today’s news or a stat that isn’t in its training data, it can query Google. This feature (called Gemini Live/Deep Search) is integrated in products like the Gemini app and Search Labs, allowing the model to pull in real-time information. Additionally, Gemini can call APIs and functions. Google’s function calling interface lets developers pass the model a set of tools; Gemini 2.5 will autonomously decide to invoke tools if the query requires it. It also has a coding sandbox in Google’s AI Studio: as showcased in Google’s demos, Gemini can generate executable code (JavaScript, Python, etc.) to create interactive outputs. One example from Google’s presentation: with a one-line prompt, Gemini 2.5 Pro wrote code for a simple video game and produced a playable result. This shows it can not only write code but effectively use a runtime to deliver outcomes (similar to ChatGPT’s code interpreter). On the multimodal side, Gemini is natively multimodal – it can take in images (e.g., you can show it a chart and ask for analysis), audio, and even video frames as part of its input. For instance, it could analyze an image and then perform a relevant tool action (like reading text in the image or searching for objects it identified). Gemini’s outputs are primarily text, but it also offers text-to-speech in the Gemini app: it can respond with synthesized voice in 24 languages, with options for style and accent. And while Gemini itself doesn’t directly generate images in the chat, it’s tightly integrated with Google’s generative image model (Imagen) and video model (Veo). As part of Google’s AI ecosystem, Gemini Pro users can ask for image or video creation and the request will route to those models (for example, through the Gemini app’s “Image Generation” or “Flow” video tools). In summary, Gemini 2.5 Pro is deeply connected to tools – it can browse the web, execute code, retrieve information from various Google services, and handle multiple input modalities. This makes it extremely powerful for tasks like research (using search), data analysis, and creative content creation with media.
Grok 4: Grok 4 was built with autonomy and tool use at its core. Unlike others that added tools as a bonus, xAI trained Grok 4 explicitly to use tools during its RL training. This means Grok doesn’t just have the capability; it has the learned skill to know when and how to use a tool in order to get the best answer. Two major tools are integrated: web search and a code interpreter. Grok’s web search is especially notable – it has real-time access not just to the open web via search engines, but also to the X platform (Twitter). According to xAI, Grok can search “deep within X,” using advanced filters to find specific posts or information on the social network. This gives it a unique edge for up-to-the-minute trends, social sentiment, or content that isn’t indexed on Google. Grok will actively run multiple searches, click results, and read them to compile an answer – essentially performing a research task autonomously (the browsing traces show it iterating search queries until it’s satisfied). Its other tool, the code interpreter, allows Grok to write and execute code (often Python) for calculations, data processing, or any logic that benefits from actual computation. For example, if asked to analyze a dataset or solve a programming puzzle, Grok can run code in the background and return the result. Grok’s multimodal abilities are also significant: it accepts images and videos as inputs. xAI introduced a “Voice Mode” where you can speak to Grok and it will respond vocally, and you can even show it what your phone camera sees in real time. It can analyze photos or screenshots and incorporate that into its reasoning (e.g., identifying objects or reading text in an image). Grok 4 even has an expressive text-to-speech voice that can convey tone (whispering, laughing, etc.) or sing, making interactions more dynamic. Essentially, Grok 4 functions like an AI agent: if a question requires fresh data, it goes online; if it needs calculation, it runs code; if it needs to see, it looks through your camera. This autonomy was highlighted by testers – for example, ask Grok to investigate a current event, and it will seamlessly pull recent info from news sites or social media. These capabilities justify the claim that Grok is “always up to date with current events” and can handle dynamic tasks that pure offline models cannot.
Claude Opus 4: Claude 4 introduced “extended thinking with tool use” in beta, reflecting Anthropic’s focus on making Claude more agentic. In extended thinking mode, Claude can interweave calls to external tools while it reasons – much like how a human might switch between brainstorming and using a calculator or browser. One of the primary tools is a web browsing/search function. Anthropic’s documentation notes that both Opus 4 and Sonnet 4 can use a web search API to gather real-time information. This means Claude can answer questions about recent events or fetch specific data not in its training set. It will cite sources or incorporate facts from the live web (Anthropic provides a safe-search mechanism to the model). Another tool integrated is a code execution tool: Anthropic’s API recently added a feature where developers can allow the model to execute code snippets (e.g., run Python) and use the output in its answer. This greatly helps with tasks requiring calculations, data analysis, or format conversions – Claude can ensure its answers are correct by actually computing results. Moreover, Claude 4 can interface with a memory/files system when permitted: developers noted that Opus 4 will create and refer to “memory files” to store key facts during a long session. For instance, if you give it a lengthy project with multiple steps, it can save interim information in a file and recall it later, effectively expanding its working memory beyond the 200k token window. In terms of multimodality, Claude at launch did not have native image input in its public API (it’s primarily text-focused). However, through partnerships (like being on Google’s Vertex AI), Claude can be paired with image tools externally. The AWS Bedrock announcement emphasizes Claude 4’s role in building “autonomous AI agents” and notes its improvements in “task planning, tool use, and agent steerability.” This indicates that Anthropic expects Claude to be embedded in agent systems that handle files, search, and other actions. Indeed, developers are using Claude in IDEs (Integrated Development Environments) for coding: Claude Code integrations allow it to edit code directly in tools like VS Code, essentially acting as a pair-programming agent that can execute git commands or read documentation as needed. In summary, Claude Opus 4 is highly capable of using tools, especially for coding and research. It may not have as many built-in multimodal gadgets as Gemini or Grok’s voice, but it compensates with very strategic tool usage during extended reasoning. When confronted with a hard query, Claude can seamlessly pivot: search for facts, run code to verify a hypothesis, or store findings for later use – all in service of producing a more accurate and thorough answer.
4. Model Architecture and Size
GPT-5: OpenAI’s GPT-5 introduces a novel unified architecture that combines multiple model components. Instead of a single gigantic model handling everything, GPT-5 uses a two-tier system: a fast, efficient base model (sometimes referred to as gpt-5-main) for most queries, and a heavier gpt-5-thinking model that kicks in for particularly complex tasks. A smart routing algorithm decides on the fly which model (or how much “thinking”) a query needs. This design allows GPT-5 to be both speedy and deep – simple questions don’t incur huge latency, while tough questions get the benefit of a more powerful reasoning process. On top of that, OpenAI has an even more potent version called GPT-5 Pro for Pro subscribers, which essentially pushes the reasoning to the maximum (taking longer and using more compute per answer). In terms of model size, OpenAI hasn’t publicly shared the parameter count. However, given GPT-4 was estimated to be on the order of hundreds of billions of parameters (with some speculating about 1 trillion with mixture-of-experts), GPT-5 is likely in that ultra-large range as well – possibly with architecture changes that make it effectively larger (it “unifies every previous model line,” possibly meaning it combines GPT-4, code-davinci, GPT-4V, etc. into one system). GPT-5 supports an extremely large context: 272k input tokens and 128k output tokens in the API, which is roughly 3-4 times GPT-4’s context length. Achieving this might involve optimized attention mechanisms or hierarchical processing (OpenAI likely uses techniques like sliding windows or segment retrieval to handle 270k tokens efficiently). The model is fully multimodal – inheriting GPT-4’s ability to accept images and audio. OpenAI also refined the training with “reasoning traces” (from the GPT-4.5 stage) and extensive fine-tuning to improve chain-of-thought quality. We can say GPT-5 is OpenAI’s largest & most advanced model to date, with a cutting-edge architecture focused on adaptive reasoning. Additionally, OpenAI offers GPT-5 in smaller variants: gpt-5-mini and gpt-5-nano, which likely correspond to reduced parameter versions (for cost/speed) that still share the core capabilities. Overall, GPT-5’s architecture balances scale with flexibility, aiming for reliability and efficiency in a single system.
Gemini 2.5 Pro: Gemini is the product of Google DeepMind’s combined research, and it leverages some distinctive architectural choices. Google has not disclosed exact parameter counts, but there are hints: earlier Gemini versions (e.g. “Ultra” and “Pro” tiers) possibly use Mixture-of-Experts (MoE) layers, a technique allowing models to have a very high total parameter count with only a subset active per query. One community rumor suggested Gemini’s largest configuration (“Ultra/Behemoth”) might involve trillions of parameters (with ~2 trillion total, ~>☃), but actively use around 200-300B per inference via MoE – however, these figures are speculative and not confirmed by Google. What we do know is Gemini 2.5 Pro is the flagship reasoning model in the Gemini 2.x family. It likely builds on the Pathways system Google developed (which can train models with multiple sub-networks). Gemini models are also multimodal from the ground up – unlike GPT-4 which added vision later, Gemini was designed to handle text, images, audio, and more in one model. This could mean it has specialized input encoders for each modality feeding into a unified transformer backbone. The context window is extraordinarily large: 1,048,576 tokens (1M) input, and 65k+ output. Google achieved this by innovations in memory management, possibly segmenting context or using retrieval-based augmentation. For example, they might embed chunks of the input and attend only to the most relevant chunks at each layer, allowing quasi-infinite context. It’s noted that a 2M-token context is in the works, so scalability is a key feature of Gemini’s architecture. Gemini 2.5 also has “Flash” variants, which are faster/cheaper models (likely smaller or with truncated context) and a “Nano”, which suggests a much smaller param model for lightweight applications. As part of architecture, Gemini employs chain-of-thought natively: it was trained or fine-tuned to generate intermediate reasoning steps, which improves its logical responses. It might use a technique akin to “Recurrent GPT” or self-reflection to manage these thoughts internally within the model’s layers. In summary, Gemini 2.5 Pro’s architecture is cutting-edge, emphasizing extreme context length, multimodal fusion, and powerful reasoning. It sits in Google’s lineup above PaLM 2; for comparison, PaLM 2 was ~540B dense parameters – Gemini 2.5 Pro could be of similar or greater effective size, especially if MoE is involved (which can multiply the parameter count into the trillions with sparse activation).
Grok 4: Grok 4’s architecture is rooted in transformer models but with xAI’s unique twist on training. The base model is likely comparable in scale to other top models (e.g., in the 100B+ parameter range, though xAI hasn’t published a number). However, what distinguishes Grok 4 is the way it was trained with reinforcement learning at scale. After the initial pretraining (predicting next tokens on a vast dataset, similar to GPT’s approach), Grok 3 and 4 underwent an extensive Reasoning RL phase. xAI used their 200k GPU supercomputer to run many iterations of model self-play and tool-use training. Essentially, the model was presented with challenging problems (especially in domains like math and coding) and rewarded for showing its work, using tools correctly, and reaching correct answers. This likely means the architecture incorporates some form of internal monologue or scratchpad: an internal text stream where it can perform step-by-step reasoning (possibly not emitted unless needed). In terms of structure, Grok 4 is a single model (unlike GPT-5’s dual model setup). xAI even mentions that “there are no non-reasoning modes” – Grok 4 is always thinking deeply by default. This suggests the model might always operate in a chain-of-thought paradigm, which could slow it slightly but yield more accurate results. The context window of Grok 4 is very large at 256k tokens, enabling it to handle long conversations or documents. Achieving 256k likely required special positional encoding schemes (perhaps similar to what Claude uses) or chunking strategies. Another architectural aspect is multimodality: Grok 4 accepts multiple input types. It likely uses a combination of encoders – for instance, a vision transformer for image inputs whose output embeddings are concatenated with text token embeddings. The “voice mode” implies it has a built-in speech recognition and text-to-speech pipeline, though those might be separate components wrapping the language model rather than part of the core model weights. Grok 4 also comes in a “Heavy” version, which suggests either a larger parameter count model or the same model run with more computational steps (e.g., a higher precision or more inference-time augmentation). Given the $300/month price for Heavy, it might be a version with, say, more fine-grained expertise (possibly analogous to GPT-5 Pro vs normal). Another notable thing: Grok’s architecture likely integrates with X data – being an in-house product, xAI might have given it special access or training on the real-time firehose of Twitter data (within ethical boundaries). In conclusion, Grok 4’s architecture can be summarized as a large transformer-based LLM, augmented heavily via RL training, with a design emphasizing tool use and multimodal inputs, and a very long context. It might not be drastically different architecture-wise from GPT-4/5 at the core (no explicit mention of MoE or retrieval modules, for instance), but its training approach yields a model that behaves differently – more like an autonomous reasoner than a straightforward predictor.
Claude Opus 4: Claude 4’s architecture follows Anthropic’s “Claude” series approach, which is grounded in transformer models with some special enhancements for safety and context length. Both Claude Opus 4 and Claude Sonnet 4 support up to 200k tokens of context, which is among the largest in the industry (similar to Gemini’s 1M, but far above most others). Anthropic achieved this already with Claude 2 (100k tokens) by using an approach akin to embedding-based retrieval within the context: the model doesn’t attend fully to all 100k tokens at every layer, rather it can pick relevant parts. They likely improved that for 200k. The architecture is also optimized for long, coherent outputs – Opus 4 can generate up to 32k tokens in a single response, which is why they price output tokens higher. Internally, Claude uses what Anthropic calls “constitutional AI” techniques: there’s a subsystem or secondary model that provides feedback to the main model to ensure outputs are helpful and harmless. By Opus 4, these safety architectures are well-integrated (and they moved from rigid refusals to more context-aware compliance). The Claude 4 models are hybrid fast/slow like GPT-5: Anthropic explicitly mentions “two modes: near-instant and extended thinking”. Likely, it’s one model that can operate at different “thought lengths” – perhaps via a special token or API parameter that lets it know how long it can spend. If extended, it might iterative internally or use more computation per token (e.g., do a tree of thought or loop over a reasoning process – this could be facilitated by the model’s ability to write to a scratchpad file as mentioned). Parameter count for Claude 4 isn’t stated, but given Claude 2 was rumored ~70B or so (just speculation), Claude 4 is likely much larger – possibly comparable to GPT-4’s scale or beyond, since Anthropic had scaled up infrastructure with ~$5B funding to train frontier models. The model card might hint that it’s their largest to date, which logically it is. Another aspect: Claude 4 is very much geared to be an agentic model – the AWS blog explicitly says Opus 4 is “designed for building sophisticated AI agents that can reason, plan, and execute complex tasks”. This implies architectural support for things like function calling, which we discussed, and perhaps a more modular output (it can output structured plans or code as needed). It might also involve self-evaluation steps: Anthropic has experimented with models that critique or refine their outputs (there’s mention of “thinking summaries” where a smaller model condenses the larger model’s lengthy thoughts to keep it efficient). Overall, Claude Opus 4’s architecture can be viewed as a very large, long-context transformer, fine-tuned heavily for coding and safe, coherent interactions, with dual operation modes and an emphasis on reliability in extended tasks.
5. Accessibility and Pricing
GPT-5 (OpenAI): One of GPT-5’s defining features is that it has been made relatively accessible to the public. Upon launch (Aug 2025), OpenAI rolled GPT-5 into its flagship product, ChatGPT. Free users of ChatGPT get to use GPT-5 (with some limits on usage speed or quantity) – this is notable, as it’s the first time the latest model is on free tier. Free access is likely rate-limited (e.g. a certain number of messages per day), but it means anyone can experiment with GPT-5’s reasoning capabilities. For power users, OpenAI has the ChatGPT Plus subscription at $20/month (which previously gave GPT-4). Plus users now enjoy GPT-5 with higher rate limits than free. Additionally, OpenAI introduced a Pro tier (pricing not publicly disclosed, but presumably higher) for professional or heavy users – Pro grants “unlimited access to GPT-5” and exclusive access to the GPT-5 Pro mode for even better reasoning. So, individuals or small businesses can choose free, $20, or higher plans depending on their needs. For developers and enterprises, GPT-5 is available via the OpenAI API (and through Microsoft’s Azure OpenAI service). The API is pay-as-you-go, with significant price reductions compared to GPT-4. As of release, the usage cost is $1.25 per 1,000,000 input tokens and $10.00 per 1,000,000 output tokens. In more familiar terms, that’s $0.00125 per thousand input tokens, which is 4× cheaper than GPT-4’s input, and $0.01 per thousand output tokens (GPT-4 was $0.06 for same). This lower pricing is likely to encourage widespread adoption and fine-tuning of GPT-5 in applications. OpenAI also provides gpt-5-mini and nano at even lower cost (for example, GPT-5-mini might cost only a fraction of that, suitable for real-time or high-volume use with some quality trade-off). In terms of platform support, GPT-5 is integrated into various interfaces: ChatGPT web and mobile apps, Azure’s services, and it can be plugged into many third-party apps (e.g., via plugins, or in Microsoft’s Copilot products). Summing up, GPT-5 is broadly accessible, with a free entry point and affordable scaling via API, which is in line with OpenAI’s strategy to maintain dominance by getting their model into as many hands as possible.
Gemini 2.5 Pro (Google): Google has taken a somewhat different approach, blending consumer offerings with cloud services. For consumer-level access, Google created the Gemini app (part of Google’s AI products, possibly integrated with Google Assistant in the future). There is a Free tier: anyone with a Google Account can use the Gemini app with access to Gemini 2.5 Flash (the fast model) and limited access to 2.5 Pro. “Limited” likely means you can ask a few Pro-powered questions per day or the model might switch to Pro only for certain tasks. To unlock full capabilities, Google offers Google AI Pro – a subscription at $19.99/month. This subscription (which is part of Google One offerings) gives you unlimited access to Gemini 2.5 Pro in the app, as well as other perks: e.g., “Deep Research” mode (more powerful reasoning, likely using Pro), “Veo 3 Fast” video generation, and integration of Gemini across Gmail, Docs, etc.. There’s also a higher Google AI Ultra plan at $249.99/month, targeting professionals/enterprise, which provides access to Gemini 2.5 Deep Think (an even more advanced reasoning model, perhaps an experimental or larger version beyond Pro) and the highest limits on all AI features. Ultra also bundles things like a huge 30 TB Drive storage and YouTube Premium, signaling it’s for power users. For developers and companies, Vertex AI on Google Cloud is the main channel to use Gemini. As of mid-2025, Gemini 2.5 Pro is available in Vertex’s Model Garden (in Preview). Pricing on Vertex AI is usage-based: Google hasn’t publicly posted final prices at this writing, but documentation and third-party sources indicate it’s in the same ballpark as OpenAI’s (around $1.25 per million input tokens, $10 per million output) and possibly lower for smaller models. Google might also charge for specialized features like long context usage or image analysis by units of consumption. Another way to access Gemini is through Google Search: Search Generative Experience (SGE) uses Gemini models to answer queries in search results. Pro and Ultra subscribers actually get an upgraded SGE (“AI Mode Deep Search”) that uses Gemini 2.5 Pro for more detailed, up-to-date answers. In terms of platform support: Gemini is (or will be) integrated into Gmail (for drafting), Google Docs (for writing assistance), Sheets (for analysis), and even Chrome (as a browsing assistant). So Google is weaving Gemini into its ecosystem. To summarize, Gemini 2.5 Pro is accessible to general users via the Gemini app and Google’s services (with a freemium model), and to developers via Google Cloud (paid per use). The pricing strategy is clearly to entice users into Google’s subscription bundles for AI, while offering enterprises a competitive API option.
Grok 4 (xAI): Grok 4’s access is a bit unique, tied to Elon Musk’s X platform. Upon its launch, Grok was made available through the Grok chatbot interface on X – essentially a chat UI accessible to users who subscribe to certain tiers of X Premium. Specifically, Premium+ and SuperGrok subscribers have access to Grok 4 within the X app. It appears that Premium+ (which might be priced around $16/month, double the standard $8 Twitter Blue) gives access to Grok’s standard model, while SuperGrok (a higher tier) gives more usage or priority. Then they introduced SuperGrok Heavy at $300/month for those who want the absolute best version (Grok 4 Heavy) and presumably higher limits. This Heavy plan is quite expensive, aimed at enthusiasts or professionals who need top performance and possibly faster responses or more context. For developers, xAI launched the xAI API which allows direct use of Grok 4 in applications. The API pricing, per xAI’s documentation, is $3.00 per million input tokens and $15.00 per million output tokens. This mirrors older GPT-4 pricing and is higher than GPT-5/Gemini, perhaps reflecting the additional costs of the integrated live search (note: these prices align with what a third-party comparison noted: Grok 4 has “the lowest API cost” among some models when considering certain contexts, but in absolute terms $3/$15 per 1M is not the lowest; it’s equal to GPT-4’s former cost). It’s possible xAI will adjust prices over time to stay competitive. As for free usage: currently, there isn’t a truly free public option for Grok 4. Musk’s philosophy has been subscription-centric (to avoid bot abuse and monetize the platform), so unlike GPT or Claude, you can’t just go to a website and use Grok for free. xAI might occasionally offer limited trials or have some demo, but generally it’s paywalled. Platform support: aside from the X app/web, Grok 4 doesn’t have widespread integrations yet (since xAI is a new company). However, Musk has hinted at using Grok in X for things like improving search or helping moderate content. Also, being accessible via API means third-party services (perhaps some browser extensions or productivity apps) could incorporate Grok if they prefer its style or capabilities (especially the real-time web insight). In short, Grok 4 is accessible through X’s subscription model and via API for developers, with a pricing scheme that targets serious users (the heavy tier for maximal power, and a relatively affordable developer rate for the standard model). This strategy somewhat mirrors OpenAI’s (offering both consumer and API access), but with the twist of being bundled into a social media subscription.
Claude Opus 4 (Anthropic): Anthropic has positioned Claude 4 more for business and enterprise, with a limited consumer-facing presence. For individual users, Anthropic’s Claude.ai web interface offers a free chatbot, but it traditionally ran Claude 2 or Claude Instant (earlier models). With the advent of Claude 4, Anthropic announced that Claude Sonnet 4 (the lighter, faster model) is available even to free users on claude.ai. However, Claude Opus 4 (the full-strength model) is not freely available; it’s included in the paid plans. Anthropic has a Claude Pro subscription (recently introduced, costing $20/month) which might grant some access to Opus 4 or at least higher usage of Sonnet 4 – but details are a bit unclear publicly. They also have higher tiers (Claude Team, Enterprise) which definitely include Opus 4 usage. In enterprise contexts, Claude 4 is accessible through partnerships: it’s integrated in AWS Bedrock (Amazon’s AI platform) so any AWS customer can use Opus 4 via an API call on Bedrock. It’s also available on Google Cloud Vertex AI as one of the third-party models. Through these channels, pricing is usage-based. Anthropic’s direct API pricing for Opus 4 is quite premium: $15.00 per million input tokens and $75.00 per million output tokens. This is 5× more expensive than GPT-5 on input and 7.5× on output. The rationale is that Opus 4 likely uses much more compute (especially in extended mode) and is targeted for applications where its superior coding ability or long-context is worth the cost. Sonnet 4, for comparison, is priced $3/$15 per million (which is similar to GPT-4’s pricing). Many business users might use Sonnet for general tasks and invoke Opus for heavy-duty ones. It’s worth noting that Anthropic sometimes negotiates custom deals or offers volume discounts for enterprise contracts, so listed prices may be high watermark. In terms of availability, aside from API, Claude is being integrated into products like Slack (Claude powers the Slack AI assistant for summarizing channels, etc.) and some knowledge management tools. But unlike OpenAI or Google, Anthropic does not have its own consumer app ecosystem (beyond the basic claude.ai chat). So individuals mostly encounter Claude through third-party apps that use it under the hood or by signing up on claude.ai (which, again, for free uses the slightly smaller model). Another aspect: Anthropic has been amenable to open-access via collaborations – for instance, some developers on the Discord and programming communities got early access tokens for Claude 4 API to test it out. But broad free access to Opus 4 isn’t currently a thing. In summary, Claude Opus 4 is mainly a premium offering for businesses and advanced users, obtainable via cloud platforms and paid plans. Its cost is the highest of the four models here, reflecting its high-end positioning as an “AI specialist” (especially in coding and lengthy tasks). That said, Anthropic’s tiering with Sonnet 4 allows a gradient so that casual users can still benefit from much of Claude’s power at lower cost or free, switching to Opus 4 when maximum capability is needed.
