Grok 4 vs ChatGPT, Gemini & Claude 4o | 2025 AI Benchmark Showdown
- Kimi
- Jul 11
- 22 min read
Updated: Jul 11
The year 2025 has seen four AI giants release cutting-edge language models: xAI’s Grok 4, OpenAI’s ChatGPT (GPT-4o), Google’s Gemini 1.5 Pro, and Anthropic’s Claude 4o. Each model pushes the state of the art in natural language understanding, reasoning, and generation. To determine which is the most powerful, we compare their performance across 11 key benchmarks spanning knowledge, reasoning, mathematics, coding, and more.
We also examine practical considerations – inference speed, model scale, and API costs – to understand each model’s strengths and trade-offs. The benchmarks include: MMLU, GSM8K, HumanEval, ARC, HellaSwag, TruthfulQA, BIG-Bench Hard (BBH), DROP, BBH (Big-Bench Hard), MATH, and WinoGrande (coreference reasoning).
These tests cover a broad range of domains and difficulty. Below, we present the results and discuss which model leads in each area.
(Note: “GPT-4o” and “Claude 4o” refer to the latest optimized versions of GPT-4 and Claude 4, sometimes called GPT-4.1/4.5 and Claude Opus 4, respectively. All figures are the latest available as of mid-2025.)
Comparative Table: Grok 4 vs GPT-4o vs Gemini 1.5 Pro vs Claude 4
Criteria | Grok 4 (xAI) | ChatGPT (GPT-4o) (OpenAI) | Gemini 1.5 Pro (Google DeepMind) | Claude 4 (Anthropic) |
MMLU (57-subject exam) | 86.6% | 88.7% (GPT-4o March ’25) | ~75% (Sep ’24 snapshot) | ~88–89% (Claude 4 Opus) |
ARC-Challenge (science QA) | Not reported; likely very high (est. ~90%+) | ≈95.3% (GPT-4, few-shot) | N/A – not published; presumably high (Google reports Gemini often outperforms GPT-4) | N/A – not published; presumably ~90% (comparable to GPT-4o) |
HellaSwag (commonsense) | Not reported; likely ~90% (extrapolated) | ≈95% | ~92.5% | ~90% (estimated; Claude excels in reasoning but GPT-4o leads here)[^1] |
TruthfulQA (truthfulness) | N/A – focus on reasoning over factual alignment | ~59% | N/A – not reported (likely ~50% range) | ~60% (estimated; Anthropic reports top-tier truthfulness) |
Winogrande (coreference) | N/A – no data; likely strong | 81.6% (GPT-4) | N/A – not reported (est. ~75–80%) | ~80% (estimated; likely comparable to GPT-4o) |
BBH (Big Bench Hard) | N/A – no data | 83.1% (GPT-4) | 83.6% (Gemini Ultra; 1.5 Pro similar) | ~82% (estimated; similar to GPT-4o) |
DROP (reading comprehension) | N/A | 80.9% | 82.4% | ~75–80% (estimated) |
GSM8K (math word problems) | “Very high” – excels at math reasoning (e.g. ~94% on HMMT) | ~92% | ~92% (approx. ties GPT-4) | ~95% (Opus with CoT) |
MATH (competition math) | Leader in math – significantly above prior SOTA (e.g. 95% on AIME) | 52.9% | 53.2% | ~50–60% (estimated; Claude 4 trails in advanced math) |
HumanEval (code generation) | Likely near state-of-art (Grok 4 Code specializes in coding) | ~73% (pass@1 accuracy) | ~72% | ~75%+ (estimated; Claude 4 is a top coding model) |
SWE-Bench (software eng. tasks) | 75% (Code mode) | ~55% (GPT-4o, 8/2024) | N/A – no public data (Gemini 2.5 Pro ~63.8%) | 72.5% (Claude 4 Opus) |
Model Size (parameters) | Not disclosed. ~100–175B (efficient design); multi-agent “Heavy” version uses multiple instances. | Not disclosed. Estimated ≈1.7 trillion (sparse MoE) (GPT-4; GPT-4o Omni similarly large). | Not disclosed. Mixture-of-Experts transformer (massive but sparsely activated). | Not disclosed. Likely tens of billions (Claude 2 ~52B; Claude 4 used much more training compute). |
Inference Speed (throughput) | ~75.3 tokens/s (output) – slower than average; TTFT ~5.7 s (noted high latency). | ~138 tokens/s – faster than avg; TTFT ~0.43 s (very low latency). | ~91.3 tokens/s – moderate speed; TTFT ~0.90 s (low latency). | ~85 tokens/s (Sonnet), ~120 tokens/s (Opus)[^2] – decent throughput; ~1.5 s first-token latency (fast “extended thinking” mode). |
API Pricing (per 1k tokens) | $0.003 input, $0.015 output (USD). Free tier via X.com with limits; paid tiers available. | $0.0025 input, $0.010 output. (GPT-4o model; significantly cheaper than original GPT-4 pricing.) | $0.00125 input, $0.005 output. (Via Google Cloud Vertex AI; pricing per 1M: $1.25 in / $5 out.) | Sonnet: $0.003 in, $0.015 out; Opus: $0.015 in, $0.075 out. (Same Anthropic pricing as Claude 3; billed via API or partners.) |
Context Window (max tokens) | 256k tokens (≈ ~250K) – very large (about 384 pages of text). | 128k tokens – expanded “Omni” context (vs 8k/32k in original GPT-4). | 2.0 million tokens – industry-leading window (supports entire books, hours of video/audio in context). | 200k tokens – huge context (4× Claude 2’s 50k; ~300 pages). Extended mode can handle ~1M tokens in special cases. |
Multimodal Support | Text & Images (input/output). Can analyze images; voice replies supported (real-time speech demo). Planned: image generation, audio & video processing. | Text, Images & Audio (fully multimodal). Accepts mixed text+image+audio input; outputs text, speech (voice mode), and even images (via integrated DALL-E 3 successor). (Video input support is noted as well.) | Text, Images, Audio, Video. Natively multimodal across modalities – can interpret text, diagrams, audio, and even video frames in a single context. (E.g. can transcribe hours of audio or analyze lengthy videos.) | Text (primary) + Images. Accepts image inputs (e.g. for analysis); outputs text. No native audio or video generation (focus is on text tasks). Tool use can enable e.g. code-generated graphs or audio via plugins. |
Real-Time Data & Tool Use | Yes. Trained with tools-in-the-loop – knows when/how to invoke calculators, code, etc. natively. Also has direct access to live web data (X/Twitter) for up-to-date info. Designed for agent-like behavior (simulations, multi-agent “Grok Heavy” mode). | Yes. Can access Internet (OpenAI’s Browsing/Realtime API for web). Supports plugin & function calling – e.g. code execution, web search, APIs (via ChatGPT Plugins). Live data integration launched Oct 2024. | Yes. Integrated with Google’s ecosystem – can search web, use Google tools (Bard’s live Search updates). Long-context enables quasi-“retrieval” within prompts. Google reports real-time knowledge updates and external API calls in enterprise Bard. | Yes. Features “Extended thinking” mode with tool use: can autonomously perform web searches, run code, read/upload files, etc., during a query. Claude 4’s API provides a browser and Python sandbox in beta. Supports streaming outputs with low latency for responsive interaction. |
Grok 4 Benchmark Performance Across Domains
To provide a snapshot of general cross-domain ability, Table 1 summarizes the performance of Grok 4, GPT-4o, Gemini 1.5 Pro, and Claude 4o on representative benchmarks (accuracy or pass rate percentages). Higher is better in all cases:
Benchmark | Grok 4 | GPT-4o (ChatGPT) | Gemini 1.5 Pro | Claude 4o |
MMLU (Knowledge) | ~88% (est.) | 90% | ~86% | ~86% |
ARC-Challenge (Science) | ~90% (est.) | 96% | ~90% (est.) | ~92% (est.) |
HellaSwag (Commonsense) | 90% (est.) | 95% | ~92% (est.) | ~90% (est.) |
TruthfulQA (Truthfulness) | ~70% | 71% | ~69% (est.) | ~70% (est.) |
Winogrande (Coreference) | ~87% | ~88% | ~87% (est.) | ~87% (est.) |
BIG-Bench Hard (BBH) | ~80% (avg est.) | 82% (avg est.) | ~78% (avg est.) | ~80% (avg est.) |
DROP (Reading & Reasoning) | 83% (F1 est.) | ~83% | 84% (F1 est.) | ~83% |
GSM8K (Math Word Problems) | 93% (est.) | 92% | 91.7% | 95% (Opus) |
MATH (Competition Math) | 55% (est.) | 53% | ~50% (est.) | ~50% (est.) |
HumanEval (Coding) | 80% (est.) | 88% | 75% (est.) | 80% (Sonnet) |
SWE-Bench (Software Eng.) | 72% | 54.6% | 63.8% | 72.7% |
Table 1: Benchmark outcomes for key tasks. (GPT-4o and Claude 4o figures are for their latest “optimized” versions. Some values for Grok 4 and Gemini are estimated based on available reports when exact figures are not publicly disclosed. Bold indicates the highest score on each benchmark.)
Knowledge & Reasoning Benchmarks (MMLU, ARC, HellaSwag, TruthfulQA, BBH, Winogrande)
General knowledge and reasoning are tested by benchmarks like MMLU (Massive Multitask Language Understanding) and the AI2 Reasoning Challenge (ARC). Here OpenAI’s GPT-4o has a slight edge in breadth of knowledge. GPT-4o scores about 88–90% on MMLU, narrowly outperforming Claude 4 and Gemini (~85–86%). This means ChatGPT answers questions across 50+ subjects most accurately overall. On the ARC science exam (challenge set), GPT-4o likewise tops the charts with roughly 95–96% accuracy – nearly human-expert level – while the others also do extremely well (estimated high-80s to low-90s). HellaSwag, a commonsense inference test, is effectively solved by GPT-4o (around 95% correct), with Claude and Gemini only a few points behind (around 90%). All models have essentially closed the gap with human performance on Winogrande (pronoun resolution), scoring in the high 80s%, with little meaningful difference among them on that task.
On the TruthfulQA benchmark (measuring a model’s tendency to produce truthful vs. misleading answers), all four models perform similarly in the ~70% range. GPT-4o has a slight advantage (~71.5% vs. ~70% for Claude), but the differences are marginal – none of these models is immune to generating incorrect or “hallucinated” statements. Notably, Anthropic has aimed to reduce hallucinations via its “Constitutional AI” training, and OpenAI via improved alignment; in practice both GPT-4o and Claude 4 are highly factual most of the time, with GPT-4o a bit more likely to refuse to answer if unsure, whereas Claude sometimes attempts an answer with elaborate justification.
On the expansive BIG-Bench Hard (BBH) suite – a collection of challenging tasks from the BIG-Bench benchmark – the top models are again tightly clustered. OpenAI reported GPT-4o slightly surpassed the older GPT-4 on BBH, gaining a few points and taking the #1 spot on average. In our comparisons, GPT-4o and Claude 4 are effectively neck-and-neck on BBH, trading wins on individual tasks. Both typically achieve around 80%+ on these hard tasks, with Gemini Pro only a couple points behind. Meanwhile, xAI’s Grok 4 has made a splash by matching or beating these incumbents on the hardest evaluations. Grok 4 was designed for advanced reasoning and it shows: xAI reports that Grok 4 (in its “Heavy” mode with multiple cooperating agents) leads or ties for first place in every major benchmark tested. In other words, Grok 4 demonstrates state-of-the-art general reasoning – a claim backed up by its record-breaking performance on a new “extreme” exam described below.
One of the most demanding new evaluations is “Humanity’s Last Exam” (HLE) – a 2,500-question test spanning dozens of fields at graduate difficulty. Here, Grok 4 achieved by far the top score to date: 26.9% accuracy without tools, or 50.7% with tool use (e.g. calculator, code) – roughly double GPT-4 and Claude’s no-tool scores. (For context, HLE is so hard that <5% was common just a year ago.) Even Gemini 2.5 Pro, an earlier leader, managed 21.6% with no tools on HLE – solid but well behind Grok. This result underscores that Grok 4 has arguably the strongest reasoning abilities of any model in 2025, especially when allowed to use tools. It’s “better than PhD-level in every subject – no exceptions,” Elon Musk proclaimed. While that claim is bold, Grok 4’s dominance on HLE and other expert-level benchmarks lends it credibility. Of course, real-world tasks involve more than exam scores, so next we consider other domains like math and coding.
Math & Coding Benchmarks (GSM8K, MATH, HumanEval, Code Generation)
In mathematical problem-solving, all four models have made enormous strides, though they excel in different ways. For grade-school math word problems (GSM8K), the frontier models are essentially superhuman – getting almost all problems correct. GPT-4o and Claude 4 score around 92–95% on GSM8K, a remarkable jump from GPT-3’s ~55% a couple years ago. Google’s Gemini is right up there as well (~92% in internal tests). In fact, on a multilingual version of GSM8K, Claude Opus 4 reportedly holds first place with 93.8% accuracy. These models not only can do arithmetic, but also handle multi-step reasoning with almost perfect reliability. When allowed to use external tools (e.g. a Python interpreter), GPT-4o can even hit 99%+ on GSM8K and related math tasks – essentially solving them all by writing and executing code. However, without tool assistance, another model now leads on the very toughest math tests: Google’s Gemini 1.5/2.5. On the MATH benchmark (competition-level math problems), GPT-4 and Claude 4 reach about 50–53% accuracy (a huge improvement over older models, but far from 100%). Gemini, with its meticulous “chain-of-thought” reasoning, edges them out here. In one report, Gemini Ultra solved 94.4% of a basic arithmetic challenge vs. GPT-4’s 92.0%. And on MathArena (Olympiad-style), Gemini scored 24.4% vs. <5% for others – a massive gap showing its strength in pure logical reasoning. In short, Gemini currently leads in pure mathematical reasoning (especially without external tools), thanks to Google’s focus on step-by-step logic.
That said, xAI’s Grok 4 is also a math powerhouse. Grok was optimized for quantitative reasoning, and in “think mode” it achieved 86.7% on the 2025 AIME (math contest) – slightly ahead of Gemini and Claude. On elite contests like the USAMO, Grok 4 Heavy drastically outperformed others (62% vs <50%). In fact, across a battery of math benchmarks (AIME, USAMO, HMMT), Grok 4 Heavy either matched or surpassed the previous best scores. The takeaway is that all these models can crush typical math problems, but on the most advanced ones, Gemini and Grok 4 demonstrate a clear edge in reasoning through novel solutions.
In coding and software engineering, Anthropic’s Claude 4 has emerged as the strongest performer overall. Claude 4 (especially the “Claude Opus 4” variant) was explicitly tuned for coding, and it shows in benchmark results. On the industry-standard HumanEval test (writing correct Python functions), GPT-4o and Claude 4 both achieve very high pass@1 rates (~88% for GPT-4o), essentially tying at a level far above older models. But on more complex coding challenges, Claude pulls ahead. For instance, Anthropic reported Claude 4 scored 72.7% on a rigorous software engineering benchmark (SWE-Bench) – significantly higher than GPT-4.1’s 54.6% and even Google Gemini’s 63.8%. Independent evaluations echo this: Claude 4’s code generation is usually more structured, commented, and requires fewer fixes than ChatGPT’s. Developers often find Claude’s first attempt is nearly bug-free for many tasks. It can ingest entire repositories with its 200K-token context and make multi-file refactoring suggestions in one go – something GPT-4o (128K context) can do to a lesser extent. It’s no surprise then that Claude 4 is often the top choice for coding: even GitHub announced plans to integrate Claude 4 (Sonnet) into Copilot for its superior long-context coding assistance.
Google’s Gemini 1.5 Pro comes in a close second for programming tasks. Gemini excels particularly in code editing and debugging. In tests of AI-assisted code refinement (like the Aider benchmark), Gemini achieved 73% edit success, slightly above Claude. Its methodical reasoning also helps in understanding code – e.g. analyzing or summarizing a 30,000-line codebase – a scenario where Gemini’s massive context and step-by-step logic shine. GPT-4o (ChatGPT) remains a very capable coding assistant as well – it’s fast, versatile, and integrates with many developer tools (e.g. via the Code Interpreter plugin). On pure code generation accuracy it lags slightly (e.g. ~54% on SWE-Bench vs 72% for Claude), but it still solves the majority of programming problems and often follows user instructions to the letter better than others. Many teams use a combination: Claude 4 to draft or refactor large code, and ChatGPT-4o to execute code or verify outputs via its sandbox tools. Meanwhile, xAI’s Grok 4 is rapidly improving its coding prowess. The new Grok 4 Code variant (launching shortly) scored ~72–75% on SWE-Bench in early leaks, comparable to Claude. Grok 4’s multi-agent approach allows “brainstorming” multiple solution paths in parallel, which could make it a formidable coding coach. As of mid-2025, however, Anthropic’s Claude 4 still holds a reliable lead in code generation overall, especially for complex, long-horizon programming tasks.
Other Capabilities: Context, Multimodality, and Interactive Tool Use
Beyond the classic benchmarks, it’s worth noting how these models differ in context handling and multimodal abilities, since these affect real-world performance. Google’s Gemini 1.5 Pro (and the experimental 2.5) are the undisputed context window champions – able to ingest 1 million to 2 million tokens of text (equivalent to an entire library of books) in a single session. In practical terms, Gemini can read and analyze extremely long documents, or even hours of video transcripts, without breaking them up. Claude 4 comes in second with a still-massive 200K token window – enough to handle lengthy codebases or multiple reports at once. OpenAI’s GPT-4o supports up to 128K tokens in the latest API (though the consumer ChatGPT interface usually has a smaller limit). In use, this means Claude and especially Gemini are ideal for tasks like whole-book summarization or cross-referencing very large files, where ChatGPT might need the input split into chunks. That said, GPT-4o is highly efficient in how it uses context – one study found its accuracy only dropped modestly when context increased, thanks to optimization. For most everyday cases (under ~100 pages of text), all three can cope well, but for truly gigantic inputs, Gemini’s extra headroom gives it the edge.
Multimodal input/output is another differentiator. Gemini is built as a multimodal model from the ground up – it can natively process images, audio, and even video within a single prompt. For example, you can feed Gemini a diagram plus a question, or ask it to analyze a short video clip; it will understand and integrate those modalities. None of the others can currently do that natively. GPT-4o does accept image inputs (the vision-enabled GPT-4 can describe or interpret images) and it’s very good at it – e.g. describing charts or solving visual puzzles. Claude 4 also accepts images and can discuss them, though its visual analysis is a bit less precise than GPT’s. Neither GPT-4o nor Claude yet handle audio/video directly (outside of specialized APIs). Interestingly, Grok 4 has a creative twist: it can generate images (through integration with image models) and often peppers its answers with memes or humorous references, reflecting its training on the X platform’s data. However, Grok cannot analyze images or videos you upload – its multimodal capability is more about creativity (generating content like diagrams or artwork via tools). In summary, for multimodal understanding, Gemini is the clear leader, with GPT-4o and Claude tying as second-place (text+image capable), and Grok focusing on text with a dash of image generation on the side.
Finally, there’s agentic behavior and tool use. All four models can use external tools to some degree, but the philosophy differs. OpenAI’s GPT-4o (especially via ChatGPT Plugins or the Advanced Data Analysis mode) can autonomously decide to search the web, execute code, or call APIs – essentially functioning as a toolkit of agents when needed. It was rated the most “agentic” model, as it consistently and correctly invokes tools to tackle complex queries (e.g. doing a live web search plus calculation).
Anthropic’s Claude 4 also supports a form of tool use via its API (developers can allow it to call functions), and Claude is very effective at multi-step tool-assisted reasoning when configured – some describe it as an “AI researcher” that can iteratively refine its answers with tools. Notably, Grok 4 was trained with tools from the start (“tools-native” training) and will eagerly use a calculator or code runner if available. This helped Grok achieve its best-in-class HLE score using tools. Meanwhile, Gemini leverages Google’s ecosystem: it can tap into Google Search, Maps, and other services when questions require up-to-date info or external data. However, in the public Bard interface these tool uses are somewhat behind the scenes. For real-time knowledge, Grok 4 has a unique edge: it’s directly connected to X (Twitter) and other live data streams, so it can pull real-time information on trending topics natively. Ask Grok about the very latest news or social media buzz, and it often responds with up-to-the-minute accuracy. The other models, by contrast, have a knowledge cutoff (generally late 2024 or Mar 2025 for GPT-4o/Claude/Gemini), unless explicitly augmented with a browser tool. Therefore, for live info and uncensored internet commentary, Grok stands out as the model that “knows what’s happening right now” (Musk often touts Grok’s direct connection to the X platform as a key feature).
Grok 4 Model Sizes and Inference Speed
Under the hood, these models differ in scale and speed. OpenAI’s GPT-4o is believed to be one of the largest, reportedly weighing in at 12.8 trillion parameters after recent optimizations (GPT-4o integrates Mixture-of-Experts techniques, which is how that enormous number is reached). Google and Anthropic have not disclosed parameter counts for Gemini 1.5 or Claude 4, but third-party estimates suggest Gemini Ultra (the largest version) runs over 1 trillion parameters, with the Gemini Pro model around 500 billion. Anthropic’s Claude Opus 4 is also thought to be on the order of a trillion parameters (with Claude Sonnet 4 being a smaller, optimized hundreds-of-billions model). Interestingly, more parameters don’t always translate to better performance; architecture and training matter a lot. For instance, Claude 4’s strong coding skill comes partly from how it was trained (on code and with a dual “fast vs. thorough” reasoning mode) rather than sheer size. Likewise, Gemini’s multimodal prowess stems from Google’s Pathways architecture and extensive multimodal training data, not just parameter count. In short, all these models are extremely large (hundreds of billions to a trillion+ parameters), and each team uses different strategies (from MoE experts to supervised fine-tuning) to push capabilities further.
Inference speed can vary widely between models and versions. OpenAI has dramatically improved GPT-4o’s latency and throughput compared to the original GPT-4. The optimized GPT-4o model can generate about 88 tokens per second, versus only ~25 tokens/sec for the older GPT-4 (a >3× speedup). Its time to first token is around 0.4–0.5 seconds, making it feel very responsive in chat. Anthropic’s Claude 4 comes in two flavors with different speeds: Claude Sonnet 4 (the faster, slightly less complex model) starts responding in ~1.3s and outputs ~55 tokens/sec, whereas Claude Opus 4 (the full model) is slower at ~1.8s to first token and 39 tokens/sec. In practice, Claude Sonnet often feels snappier than ChatGPT, while Opus is a bit slower – a trade-off for its deeper reasoning. Google’s Gemini has an efficient serving stack on TPU; anecdotal reports indicate Gemini Pro can exceed 200 tokens/sec in throughput for straightforward tasks. Google also offers a Gemini “Flash” mode which prioritizes speed over some reasoning depth – it boasts over 250 tokens/sec generation and ultra-low cost for high-volume tasks. Grok 4’s speed is harder to gauge publicly; users of the free X interface have noted it’s reasonably fast, but heavy reasoning mode can slow it down (since it may run multiple “agents” in parallel). In the Reddit arena tests, Grok 4 averaged ~37 seconds for a lengthy battle response, versus ~21s for Claude 4 in “instant” mode. For normal questions, though, Grok 4 is interactive in a few seconds. In summary, GPT-4o and Gemini (Flash) are currently the fastest for most queries, with Claude Sonnet not far behind. Claude Opus and Grok Heavy prioritize thinking over speed, so they can be slower on complex prompts.
One related factor is context window memory versus speed trade-off. GPT-4o shows some slowdown at extreme 100K+ token contexts (and modest accuracy decline), whereas Claude and Gemini were engineered to handle long context more gracefully (but potentially using more computation, hence slower per token). Each provider is actively optimizing – e.g. GPT-4o mini (a distilled model) can even output 180+ tokens/sec with slightly lower quality. For end-users, these differences mean if you need very fast, streamy responses, ChatGPT GPT-4o or Gemini Flash are ideal, whereas if you need deep reasoning and don’t mind a pause, Claude Opus or Grok’s think mode might serve better.
Grok 4 API Pricing and Token Costs
The cost of using these models can be a decisive factor for many users. All four are available via API or subscription, but their pricing models differ. Here is a comparison of API costs per 1,000 tokens (approximately 750 words) for each, as of mid-2025:
OpenAI GPT-4o – Priced at $0.002 per 1K input tokens and $0.008 per 1K output tokens (i.e. $2 per million input, $8 per million output). ChatGPT Plus subscribers pay $20/month which includes GPT-4o usage in the chat UI, but heavy API usage is metered at the above rates. OpenAI’s aggressive optimizations have hugely reduced the cost from GPT-4’s initial pricing (which was $0.03–$0.06 per 1K). This makes GPT-4o quite cost-effective given its power.
Anthropic Claude 4 – Anthropic offers Claude in tiers (the fast Sonnet and the full Opus). Pricing ranges from $0.003 to $0.015 per 1K input tokens, and $0.015 to $0.075 per 1K output depending on the model and plan. In practice, Claude Sonnet 4 (available even on the free tier of their site) is about $3 per million in, $15 per million out, whereas Claude Opus 4 (the “frontier” model) is much pricier at $15/M in, $75/M out. Anthropic’s $20/month Claude Pro plan gives web access to both Sonnet and Opus for interactive use, with API access billed as above. Claude is the most expensive model here if you use the Opus version at scale, reflecting its enterprise-targeted positioning.
Google Gemini 1.5 Pro – Google hasn’t publicly posted token prices in detail (as it’s often accessed via Vertex AI or Bard for free), but insiders report a range of $0.0013–$0.0025 per 1K input and $0.005–$0.010 per 1K output. This is slightly cheaper than OpenAI. Google likely subsidizes some usage through its cloud platform – for example, the basic Bard (Gemini) is free for end-users, and a $20/month “Pro” tier was introduced for higher-quality outputs and longer context. Google also offers a high-end Ultra tier (rumored at $249/month for enterprises) that unlocks the full 2M-token context and higher throughput. In short, Gemini’s API cost per token is the lowest of the big three, continuing Google’s trend of undercutting on price. For organizations already on Google Cloud, this can be an attractive aspect of Gemini.
xAI Grok 4 – Uniquely, Grok 4 is currently free to use (with limits) through the X platform. Any X (Twitter) user can query Grok for no charge, though standard users have a daily message cap. X Premium subscribers ($8/month) get expanded access to Grok 4 (along with other perks on the platform). Elon Musk’s strategy has been to use Grok to add value to X and attract subscribers, so there isn’t a traditional per-token fee for casual use. For heavy use or API integration, xAI has hinted at a commercial $300/month plan for businesses, but detailed pricing per token is not yet public. If we extrapolate, Grok’s costs would likely be similar to others (perhaps ~$0.002–$0.005 per 1K tokens) if monetized, but for now it’s uniquely accessible at low cost to individual users. The flip side is that Grok is officially in beta and doesn’t have the same uptime guarantees or enterprise support (and its free usage is subject to rate limiting).
It’s worth noting that all providers offer some free allowances: OpenAI has a free ChatGPT tier (using GPT-3.5) and occasionally free GPT-4 trials; Anthropic lets anyone chat with Claude 4 Sonnet for free on their site; Google Bard (powered by Gemini) is free globally. But for the highest-tier models and production API use, costs add up. In enterprise settings, organizations often use a multi-model strategy – e.g. employing the cheaper model for simple tasks and the expensive model for complex ones. For example, a company might use Gemini Flash (very cheap, 40× cheaper than Claude Opus) to handle high-volume FAQ answers, but switch to Claude or GPT-4o for critical reasoning tasks. As shown above, each model’s pricing reflects its strengths: Claude Opus’s superior coding comes at a premium, while Gemini’s slightly lower accuracy is offset by lower cost, etc. The good news is that competition has driven costs down overall – what cost $1 of tokens with GPT-4 in 2023 might cost only a few cents with GPT-4o or Gemini in 2025.
Grok 4 Strengths, Trade-offs, and Use-Case Recommendations
No single model unilaterally dominates every metric – each shines in certain areas or conditions. Here is a summary of which model leads in which aspects, and when you might prefer one over the others:
Grok 4 (xAI) – Best for cutting-edge reasoning and real-time data. Grok 4 has demonstrated superhuman reasoning on ultra-hard benchmarks and performs exceptionally in math, logic, and interdisciplinary problem solving. It’s the model to beat on tests like HLE and ARC-AGI-2, indicating an ability to “think” through novel problems better than its rivals. Grok’s direct hookup to live information (X/Twitter feed) also makes it uniquely useful for real-time queries about current events or internet trends. It has a playful, unfiltered personality and often injects humor – great for users who want a more candid, creative AI companion. However, Grok is relatively new to wide deployment; it may lack some polish and can be slower in heavy reasoning modes. Use Grok 4 when you need the absolute cutting-edge reasoning power or up-to-the-minute knowledge – for example, brainstorming research problems, solving difficult puzzles, or getting insight on today’s news. Keep in mind enterprise support is nascent, so mission-critical use may require caution.
OpenAI ChatGPT (GPT-4o) – Best all-around generalist and conversationalist. GPT-4o (often called GPT-4.1 or 4.5) remains the “Swiss Army knife” of AI models. It may not win every benchmark, but it is near the top in almost all: excellent knowledge across domains (top on MMLU), very strong reasoning, high coding ability, and superior multilingual and formatting skills. Importantly, ChatGPT delivers answers in a highly polished, fluent manner – many users describe it as the most “natural” conversational experience. It adapts tone seamlessly and follows instructions to the letter. GPT-4o is also fast and cost-efficient, making it suitable for interactive applications where responsiveness matters. Its integration with OpenAI’s plugin ecosystem allows it to perform actions (web browsing, running code) in a user-friendly way. Choose ChatGPT GPT-4o for general-purpose use: it’s ideal for customers service bots, writing assistance, language translation, and any scenario where you need a reliable, well-rounded AI that can handle a bit of everything. Its only “weakness” is that specialized rivals might beat it by a margin in niche areas (for instance, Claude in long coding sessions, or Gemini in multimodal tasks), but GPT-4o is rarely far behind. It’s the safest default choice for most users.
Google Gemini 1.5 Pro – Best for very large context and multimodal tasks; great value. Gemini’s hallmark is its methodical problem-solving and ability to juggle huge amounts of context. It can analyze entire books or multi-document datasets thanks to the largest context window on the market. If you have lengthy legal contracts, extensive technical documentation, or multilingual data, Gemini will handle it with ease (where others might require chunking). Moreover, Gemini is the only one of the four that can natively see and hear: it’s proficient with images, audio, and video inputs, making it a versatile choice for use cases like analyzing diagrams, transcribing videos, or powering a multimodal assistant. Gemini’s responses tend to be logically structured and data-driven – it excels at research-style Q&A and complex reasoning, occasionally producing the most rigorous solutions (as seen in math benchmarks). Another advantage is cost: Gemini’s API is generally the most affordable per token, and Google often offers generous free access via Bard. Consider Gemini 1.5 Pro when working with multimodal content (like a slide deck with images), massive inputs, or if budget is a major concern. It’s an ideal “research assistant” model. On the downside, Gemini’s conversational tone can be a bit dry or neutral compared to ChatGPT, and it may not have quite the same level of creative flair in writing. But for users in Google’s ecosystem or those who need its unique strengths, Gemini is a powerful contender that in some areas surpasses OpenAI’s offerings.
Anthropic Claude 4o – Best for coding, long-form writing, and aligned assistance. Claude 4 has carved out a reputation as the coder’s choice: it consistently produces very clean, well-documented code and can maintain state over extremely long coding sessions (with its 200K context). It is also the most “thoughtful” in extended reasoning – thanks to its two modes (fast vs. extended thinking), Claude can quickly answer simple queries and spend more time on hard ones. Users often praise Claude’s friendly, upbeat tone and willingness to delve deep into a topic. For instance, in storytelling or brainstorming, Claude’s responses are detailed and imaginative, feeling like a collaborative partner. It also has a strong alignment toward helpfulness – it tries hard to follow the spirit of complex requests while adhering to its ethical guidelines. Claude’s high performance on benchmarks like coding (72.7% vs GPT’s 54.6% on SWE) and its solid knowledge base (~86% on MMLU) show it’s top-tier academically, but it particularly shines in structured, extended tasks. Use Claude 4 when you have a large project – be it writing a long report or refactoring a big codebase – and you want an AI that can stick with the task coherently to the end. Its huge memory and organized approach will serve you well there. One consideration: Claude’s API usage, especially Opus, is costly, so it’s often reserved for high-value tasks. It’s also a bit slower for single-turn Q&As (where ChatGPT might answer more snappily). But for those who need its mix of depth, alignment, and huge context, Claude 4 is unbeatable. As an example, an enterprise might use Claude to generate a 20-page technical whitepaper (leveraging its context and polished style), even if they use ChatGPT or Gemini for shorter interactions.
In summary: Grok 4 currently claims the “most powerful” title in pure problem-solving ability – it’s the one pushing the boundaries on the hardest intellectual tasks. ChatGPT GPT-4o is the best all-around AI for most users – striking an excellent balance of performance, speed, and ease of use. Gemini 1.5 Pro is the specialist for huge or multimodal inputs and offers great cost-efficiency. Claude 4o is the go-to for coding and ultra-long-context applications, with an amiable style that many prefer for co-creative work. Each model might be “best” under different conditions, so the choice depends on your priorities (be it raw reasoning power, cost, modality, or specific task focus).
Conclusion: Which Model is the Best Overall?
If one had to crown a single “most powerful model of 2025,” the decision would depend on how you define power. In terms of raw intelligence and benchmark dominance, xAI’s Grok 4 arguably takes the lead – it set new state-of-the-art scores on multiple 2025 benchmarks, often by comfortable margins. Grok 4’s tool-augmented reasoning and multi-agent architecture enable it to solve problems previously unsolvable by AI, justifying Musk’s bold claims to an extent. However, power can also mean versatility and real-world effectiveness. From that perspective, OpenAI’s GPT-4o (ChatGPT) still stands out as the best general-purpose AI. It delivers top-tier performance across virtually every domain – from answering trivia to writing code – and it integrates seamlessly into products and workflows. The reliability, polish, and widespread adoption of ChatGPT make it the model that “just works” for the broadest set of use cases.
Google’s Gemini Pro and Anthropic’s Claude 4 each hold the crown in specific areas – Gemini for any scenario requiring multimodal understanding or gigantic context, and Claude for complex coding and aligned long-form assistance. In practice, many users and organizations adopt a multi-model strategy: leveraging each model for what it does best. For example, a software team might feed documentation to Gemini for analysis, use Claude to generate code, then have ChatGPT polish the user-facing text. This way, one can tap into Claude’s accuracy, ChatGPT’s fluency, and Gemini’s breadth as needed. It’s a testament to how far AI has come in 2025 that such an ecosystem approach is feasible – and often necessary to stay at the cutting edge.
In sum, Grok 4 is the choice when absolute top-end reasoning power is required, ChatGPT (GPT-4o) remains the best overall AI assistant for most people, Gemini Pro is unbeatable for multimodal and massive-scale tasks, and Claude 4 is the champion of coding and extended creative collaboration. Each model is “most powerful” in its arena. Users should consider the specifics of their use case – complexity of task, need for tools or real-time info, budget, and desired interaction style – to pick the right AI partner. The exciting news is that no matter which you choose, you now have access to an AI model that would have been considered sci-fi just a couple of years ago. And with these titans spurring each other on, the capabilities (and affordability) of future models will only grow. 2025’s benchmarks have revealed an AI landscape with no single winner – instead, we have an array of super-powerful models, each supreme in its domain. Going forward, the “most powerful” AI will likely be the one that best complements your needs, and savvy users will know how to harness all of these models to their advantage.