How do I convert a GitHub repo to a text file?

Paste the repository URL above and the file tree appears as a checkbox list. Untick what the model does not need, generate, and you get one text file: directory structure at the top, then each selected file under a header with its path. Nothing to clone, nothing to install, free and no sign-up.

How do I convert a private GitHub repository?

Paste a personal access token alongside the URL. Create a fine-grained token scoped to just that one repository with read-only Contents permission and a short expiry — that is the least privilege the API will accept for this. The token is used to call GitHub from your browser and is not stored on our side.

How many tokens is my codebase, and will it fit?

The counter updates as you tick files, so you can see the number before you paste anything. As a rough guide, source code runs around 3 to 4 characters per token, so a 500 KB selection lands near 140,000 tokens. That fits a 200K context window and will not fit into a single chat message on most interfaces.

Which files should I exclude before feeding a repo to an LLM?

node_modules, vendor, and any dependency directory first — they are usually the overwhelming majority of the bytes and contribute nothing about your code. Then lockfiles, build output, minified bundles, test fixtures, and binary assets. On a typical JavaScript project that pass alone often removes over ninety percent of the tokens.

How does this compare to gitingest, repomix, or code2prompt?

Those are strong tools and the honest difference is workflow, not capability. They are command-line first, scriptable, and the right answer inside CI or a batch job. This runs in a browser tab with a checkbox tree, so you can see the repo, deselect what you do not want, and watch the token count move — better for the one-off case where you are still deciding what to include.

Is it safe to put my code through this?

Public repositories are read through the GitHub API from your browser, so the code goes from GitHub to you. For anything confidential the stronger option is the local folder converter, which never uploads at all. Either way, scan your selection for API keys and credentials before pasting it into a model — that is the step people skip.

Top-Performing LLMs of April 2025: A Comprehensive Review

The Latest Top-Performing Large Language Models (April 2025)

The landscape of large language models (LLMs) has evolved explosively in early 2025. In just the past few months, both open-source communities and AI industry leaders have released state-of-the-art LLMs that push the boundaries of what AI can do. These models are achieving unprecedented scores on key benchmarks – from knowledge tests like MMLU to coding challenges like HumanEval – and introducing novel architectural tweaks. In this article, we'll highlight the top-performing LLMs as of April 14, 2025, including both open-source breakthroughs and cutting-edge commercial models. We'll compare their benchmark performance (MMLU, MT-Bench, HumanEval, GSM8K), discuss innovative features (like huge context windows and reinforcement learning training), and explore real-world applications in a conversational yet technical tone that developers and AI enthusiasts will appreciate.

Key Benchmarks for LLM Performance in 2025

Before diving into the models, it's important to understand the benchmarks commonly used to evaluate LLMs:

MMLU (Massive Multitask Language Understanding): A rigorous test of a model's broad knowledge and reasoning, consisting of questions from 57 subjects at high school and college level. It's usually evaluated in a few-shot setting (e.g. 5-shot) to measure how well models can recall and reason across domains. Top models like GPT-4 have scored in the mid-80s percentage on MMLU, essentially acing what amounts to a comprehensive exam.
MT-Bench: A multi-turn dialogue benchmark that evaluates how coherent, helpful, and consistent an LLM is in conversation. It simulates chat interactions requiring the model to handle follow-up questions and context over multiple turns. Scores are often on a scale up to 10. For example, GPT-4 scored about 8.99/10 on MT-Bench, setting a high bar for chatbot quality.
HumanEval: A coding benchmark from OpenAI that tests the model's ability to generate correct solutions to programming problems (typically in Python). It consists of 164 problems, and the metric is usually pass@1 (success rate without needing multiple tries). Earlier LLMs like GPT-3.5 had modest performance here, but newer models are dramatically better – for instance, Claude 3.5 Sonnet achieved about 92% on HumanEval, slightly edging out GPT-4's ~90% in one recent evaluation. This indicates that the latest models can write correct code for the majority of standard problems, a huge leap from a year ago.
GSM8K: A benchmark of grade-school math word problems (8,000 problems). It tests arithmetic, logic, and problem-solving with chain-of-thought reasoning. Top models approach human-level performance with careful reasoning. For example, some state-of-the-art models in 2025 can solve GSM8K problems with over 90% accuracy by generating step-by-step solutions.

These benchmarks cover a spectrum: MMLU for world knowledge and reasoning, MT-Bench for conversational ability, HumanEval for coding, and GSM8K for math reasoning. Of course, many other evaluations exist (from mathematical contests like AIME to multi-modal tests), but we'll refer to these key four to illustrate how the latest models stack up.

Open-Source Breakthroughs in Reasoning and Efficiency

One of the most exciting developments has been the rise of open-source LLMs that rival or even surpass some proprietary models on these benchmarks. Two notable examples released recently are DeepSeek-R1 and QwQ-32B – both focus on enhanced reasoning via reinforcement learning, and they've achieved top-tier performance.

DeepSeek-R1: Massive Mixture-of-Experts with Top Reasoning Performance

DeepSeek-R1 made waves in early 2025 by demonstrating that an open model could reach GPT-4 level performance on complex reasoning tasks. Developed by the Chinese AI company DeepSeek, R1 is a 671-billion-parameter model employing a Mixture-of-Experts (MoE) architecture (though only about 37B parameters are "activated" per query). This design allows it to be extremely large in knowledge capacity while keeping inference efficient – effectively leveraging sparse computation so that only a subset of the model's expertise is used for any given question.

DeepSeek-R1 underwent a unique training pipeline heavily reliant on reinforcement learning (RL) for reasoning. In fact, an earlier variant DeepSeek-R1-Zero showed that strong reasoning behaviors (like self-checking answers and generating detailed chains-of-thought) can emerge purely from RL fine-tuning without any supervised stage. The final DeepSeek-R1 combined this with some supervised "cold start" data and multi-stage RL, resulting in exceptional performance. On English MMLU (a broad knowledge test), it scores about 90.8% – essentially at the top of the chart for any model its time. It's also extremely proficient at coding and math: on evaluations like Codeforces (competitive programming) DeepSeek-R1's performance was comparable to expert human coders (a Codeforces rating over 2000, in the range of a master-level competitor). For GSM8K math problems, it similarly achieved ~97% on one math benchmark, indicating near-perfect multi-step math reasoning.

Importantly, DeepSeek-R1 is open source (MIT licensed) and available for use, even being deployed on platforms like AWS Bedrock as a fully managed model. This openness means organizations can fine-tune it on their data or integrate it directly. Its massive scale and training approach make it especially good at tasks requiring step-by-step reasoning. It's currently ranked as the #1 open-source LLM on the Chatbot Arena leaderboard (and fourth overall among all models). For enterprises, R1 opens up possibilities to have GPT-4-level reasoning on private data – for instance, companies are already using retrieval-augmented setups where DeepSeek-R1 is connected to internal knowledge bases to give domain-specific answers.

However, running DeepSeek-R1 can be challenging due to its sheer size. Full inference requires significant hardware (it was reported to need ~1500 GB of VRAM if fully loaded, e.g. 16×A100 GPUs). To address this, the developers also released distilled "dense" models (without MoE) based on smaller architectures like LLaMA and Qwen. One distilled variant, DeepSeek-R1-Distill-Qwen-32B, was shown to outperform OpenAI's o1-mini model on various benchmarks and set new state-of-the-art results for non-MoE models. This distilled 32B model gives a taste of R1's power in a package that can run on a single high-end GPU (around 24GB memory required).

In summary, DeepSeek-R1 proves that the open-source community can keep up with the OpenAI's and Googles of the world, at least in reasoning benchmarks. Its combination of scale and sophisticated RL training makes it a reasoning powerhouse, excelling in benchmarks like MMLU (90+%) while also handling coding (HumanEval and beyond) at near state-of-the-art levels. If your application needs deep reasoning and you have the resources, R1 or its distilled versions are game-changers.

Alibaba's Qwen QwQ-32B: Efficient Reasoning with RL at 32B Parameters

Another headline model is QwQ-32B, launched in March 2025 by Alibaba's Qwen team. QwQ-32B is remarkable because it achieves performance comparable to the likes of DeepSeek-R1 with a model of only 32 billion parameters. In other words, it's packing GPT-4-level reasoning skills into a model small enough to run on a single modern GPU. This model is part of Alibaba's open LLM initiative (the same family as Qwen-7B/14B from 2023), and it specifically focuses on reasoning and problem-solving abilities.

Like DeepSeek, QwQ-32B uses extensive reinforcement learning fine-tuning to boost its performance on hard tasks. The Qwen team describes QwQ as a "reasoning model" that is capable of "thinking and reasoning" beyond what conventional instruction-tuned models can do. Under the hood, QwQ-32B's architecture is a transformer with modern enhancements – it uses RoPE rotary positional embeddings for long context, SwiGLU activation, RMSNorm, and some custom attention biases. It has 64 layers and 40 attention heads (with some grouped-query attention technique), and notably supports an extremely large context window of up to 131,072 tokens (about 100k tokens by default, extendable to 131k with special settings). That means QwQ can handle long documents (hundreds of pages) in a single prompt, an increasingly important feature for real-world applications.

Despite being only 32B in size, QwQ's reinforcement learning regimen was multi-stage and quite intensive. According to Alibaba, it was trained in two main RL phases:

Math & Coding Phase: QwQ was trained with specialized feedback – a math accuracy verifier to reward correct solutions to math problems, and a code execution tester to reward code that passes unit tests. This ensured the model learned to double-check its answers (much like a person would verify a calculation or run a snippet of code). This led to big gains in benchmarks like GSM8K (math) and HumanEval (coding). In fact, QwQ-32B's performance on coding problems is on par with much larger models. On the LiveCodeBench coding challenge, it scored ~63.4%, not far behind DeepSeek-R1's 65.9%.
General Reasoning & Alignment Phase: A second stage of RL fine-tuning rewarded the model for general capabilities – using a mix of reward models and rule-based checks to improve its instruction following, coherence, and "agentic" reasoning (the ability to use tools or take actions). This stage improved its performance on tasks like following user instructions and staying aligned with human preferences, without sacrificing the math/coding skills gained in the first stage.

The result of this training is that QwQ-32B can "think critically while utilizing tools and adapting its reasoning based on feedback". In practical terms, it means QwQ is better at multi-step logical problems and can even interface with external tools (e.g., calculators, search engines) if designed to do so. Alibaba has essentially given it some agent-like abilities on top of raw language modeling.

On benchmarks, QwQ-32B proves its mettle. While official numbers are still being confirmed, the Qwen team stated that QwQ-32B's performance is on par with DeepSeek-R1 (671B) and OpenAI's o1-mini on hard tasks. Internal evaluations showed QwQ-32B solving ~79.5% of problems on a challenging math test (AIME24) and matching DeepSeek-R1 on complex multi-step reasoning benchmarks. In the Chatbot Arena leaderboard of open models, QwQ-32B sits comfortably among the top, with an Elo rating around 1315 and high scores on automated "Arena Hard" dialogue tests. It's worth noting that QwQ doesn't necessarily beat larger open models on every single benchmark – for example, its MMLU (general knowledge) score is likely a bit lower than DeepSeek-R1's 90.8%, simply due to having fewer parameters to store world knowledge. But on the hardest reasoning-centric evaluations, QwQ often comes very close to the best, making it one of the most efficient high-performance LLMs available.

QwQ-32B is released under an Apache 2.0 license, completely open for commercial use. This openness means developers can fine-tune or integrate QwQ freely. Running it requires roughly 24 GB of GPU memory for 16-bit precision (and even less with 8-bit quantization), which puts it within reach of a single high-end GPU. Many in the community have already started running QwQ-32B on local hardware, amazed that they can get near-GPT-4 reasoning abilities on a desktop workstation. If you need to deploy an LLM on-premises or at the edge, QwQ-32B is a compelling option.

In short, Alibaba's QwQ-32B demonstrates that smart training (especially RL on reasoning tasks) can make a 32B model punch far above its weight class. It excels at problem solving and tool use, has a massive context window for long documents, and is open and efficient. Together, DeepSeek-R1 and QwQ-32B indicate a trend: the open-source LLM community is rapidly innovating to close the gap with proprietary models, using techniques like mixture-of-experts and reinforcement learning to achieve superior performance-to-compute ratios. This bodes well for wider access to AI, as organizations won't always need a giant budget to leverage top-tier LLM capabilities.

Meta's LLaMA 3.1: Pushing Open-Source Scale to 400B+ Parameters

The LLaMA 3 family includes smaller distilled versions (8B and 70B) that benefit from the larger model's knowledge via techniques like cross-model training and reward distillation. Meta reported that LLaMA 3's 70B model outperforms previous open models like Mistral 7B and Google's Gemma 7B on benchmarks including MMLU, coding, and math. In fact, LLaMA 3 (70B) is often used as a strong base model for fine-tuning because of its balanced capabilities. Meanwhile, the 405B version is used as a research reference – it's available on platforms like IBM's watsonx.ai for those who have the hardware to utilize it. Even if running the full 405B model is impractical for most (it requires multi-GPU setups with at least 8×80GB A100s, and even then uses sharding to fit), the advancements in training it have trickled down to better mid-size models.

In terms of benchmarks, while LLaMA 3.1 405B hasn't been publicly pitted against GPT-4 in a paper, we can infer it's very strong in knowledge and language understanding (likely north of 85% on MMLU and competitive on MT-Bench conversation). Its coding and math skills improved over LLaMA 2, though without the specialized RL training, it may not reach the extreme reasoning heights of DeepSeek or QwQ on those niche tests. Still, LLaMA 3.1's importance is in being an open foundation – researchers and companies can build on it with their own fine-tuning to create domain-specific top performers. And since it's open, there's no restriction on using it for any purpose (some models in the family are under Meta's community license, which is fairly permissive).

Meta's effort, combined with projects like DeepSeek and Qwen, means that as of 2025, the open-source community has LLMs that are competitive with (and sometimes superior to) closed models on many benchmarks. This diversity of models is healthy for the ecosystem – developers can choose an open model and tailor it, rather than relying only on API access. It also means enthusiasts can run powerful models locally. For instance, someone could deploy a local AI coding assistant by fine-tuning LLaMA 3.1 70B on coding data and get performance approaching Codex/GPT-4 on programming tasks, without sending code to an external service. Tools like repo-to-text tools can help in this process by converting entire codebases into plain text for training or prompting (one can easily turn a GitHub repository into text using a service like repo2txt to create a dataset for an LLM – essentially going from a repository to text format for LLM consumption). This ability to feed custom data is incredibly valuable, and indeed many developers use solutions to get GitHub repo to text for fine-tuning LLMs on their codebase.

(Internal link example above: converting a github repo to txt is a common need when customizing code-focused LLMs. Services like repo2txt make it easy to transform a repository into plain text files for model training or prompting.)

Another aspect is data gathering. Many top models benefited from huge training data crawled from the web. Initiatives like Crawl4AI have emerged to help researchers collect large-scale web data ethically for AI training. These crawl4ai efforts enable building custom knowledge corpora, which can be fed into open models to specialize them. For example, one might crawl domain-specific forums or documents (using a tool akin to Crawl4AI) and fine-tune an open LLM so that it becomes an expert in that niche. This approach was unheard of a few years back but is now quite feasible with the open models available.

Other Notable Open-Source Models

Mistral/Mixtral: Mistral AI (a startup known for the Mistral 7B model in 2023) released Mixtral 8×22B in April 2024, a sparse MoE model with 141B total parameters (8 experts of 22B each) that uses only 39B active params per token. It was one of the early demonstrations of efficient scaling – matching larger dense models on many tasks. Rumors hint that Mistral is working on a larger 100B+ dense model for 2025, but even the 141B MoE remains relevant as a high-performance model you can run on a few GPUs.
Phi-3 by Microsoft: Microsoft's research wing unveiled Phi-3 (a "small" language model series) in late 2024. The Phi-3 mini (3.8B parameters) impressed researchers by achieving competitive scores on MT-Bench and MMLU despite its tiny size, thanks to being trained on a massive 3.3 trillion tokens. It essentially reached GPT-3.5 level performance with <4B parameters – an efficiency win. Larger variants (7B "small" and 14B "medium") of Phi-3 are planned, which could become popular choices for applications that need decent intelligence in a lightweight model (e.g. mobile or on-device assistants).
GLM and others: In the multilingual domain, models like GLM-4 (General Language Model from Tsinghua/Zhipu) have been strong. The latest GLM-4 9B model (GLM-4V) was actually noted as the best performing open model in one multilingual benchmark (52% accuracy across 100 languages). And just like OpenAI and Anthropic, Chinese tech companies are releasing their LLMs: Tencent's Hunyuan and Baidu's ERNIE 4.0 claim high performance in Chinese and English tasks. Hunyuan-Turbo, for instance, appears on leaderboards with an Elo >1300, comparable to Anthropic's Claude 3.7.

The takeaway is that open models now cover the full spectrum – from ultra-large (405B LLaMA) to ultra-efficient (Phi-3 at 3.8B) – often with only a small performance trade-off compared to the very best closed models. This empowers developers to choose a model that fits their needs and resources. Whether you need a model to run locally (and maybe convert your entire codebase from a repository to text input for it) or you want a multilingual model to deploy in your own app, there's likely an open-source LLM that fits the bill.

Leading Commercial LLMs: GPT-4.5, Claude, Gemini, and More

On the commercial side, the past two months have seen significant upgrades from the "big three" of AI (OpenAI, Anthropic, and Google/DeepMind), as well as intriguing newcomers like xAI. These models are generally larger, closed-source, and accessible via APIs or platforms. They often still hold the crown on certain benchmarks and offer polished capabilities out-of-the-box, like multimodal understanding and fine-grained conversational skills.

OpenAI's GPT-4 and GPT-4.5: Still Top-Tier Generalists

OpenAI's GPT-4, released March 2023, remains one of the most powerful and versatile LLMs available. It set the standard last year by achieving approximately 86% on MMLU (5-shot) and around 8.9/10 on MT-Bench, outperforming most predecessors by large margins. On coding, GPT-4's prowess was evident with roughly 67% pass@1 on HumanEval (at release) and it could even solve many competitive programming problems that stumped earlier models. It was also the first to introduce strong multimodal capabilities – GPT-4 can accept images as input (and in internal tests, even audio), allowing it to interpret diagrams or screenshots. All this made GPT-4 the "fore-runner" of the LLM race.

Overall, OpenAI's GPT series (GPT-4 and 4.5) are still at or near the top on most benchmarks. For instance, in the Chatbot Arena full leaderboard, the ChatGPT-4o (latest) model had an Elo of 1406, only narrowly behind Google's very latest Gemini experimental model. GPT-4 also famously was the first to reach "superhuman" performance on the bar exam and many academic tests, highlighting its general intelligence. Access to GPT-4/4.5 is via API or ChatGPT (with GPT-4 available to subscribers). For many developers, GPT-4 is the go-to for building products unless they specifically need an open model or on-prem solution.

One thing to note is OpenAI's mysterious internal codenames that have popped up in discussions: you'll hear about "o1", "o3", "o3-mini" etc. These refer to experimental models or variants. For example, "o1" might be a reference to an OpenAI model focused on chain-of-thought (some speculate it's a version of GPT-4 with reasoning RLHF). "o3" seems to be a next-gen model with heavy reasoning training. In some evaluations, an OpenAI o1-1217 model scored as high as 91.8% on MMLU, and an o3-mini has extremely high coding/math abilities (it scored 93.4 percentile on Codeforces coding test, practically reaching human expert level). These aren't openly available, but it's clear OpenAI is testing specialized models internally. The reason this matters is because open competitors like DeepSeek often compare against "OpenAI o1" or "o3-mini" in their papers. For our purposes, just know that GPT-4.5 is the main public model now, and it excels at conversation and knowledge, while OpenAI likely has an GPT-5 or similar in the works that will combine the best of both worlds (they've hinted GPT-4.5 is the last without built-in reasoning, implying the next major release will marry the unsupervised power with strong reasoning skills).

As of 2025, GPT-4 continues to be a workhorse for many developers and companies, thanks to its reliability and broad ability. OpenAI has improved it over time (e.g., the version referred to as ChatGPT-4o or GPT-4 Turbo). It also expanded GPT-4's context length to 128k tokens for premium users, meaning it can read long PDFs or even handle a full book in one go. This long context is similar to what the open models above achieved, and is great for tasks like analyzing large codebases or lengthy legal documents. In fact, a common practice when using GPT-4 for code analysis is to feed it entire repositories by converting the repo to text, often using specialized tools or services, then having it reason about that large input. (Many developers use GPT-4 in combination with tools to get GitHub to plain text and then ask the model questions about the repository, design diagrams, etc.)

In late March 2025, OpenAI announced GPT-4.5, an interim model that is described as their "largest and most knowledgeable" model yet. GPT-4.5 did not focus on further improving chain-of-thought reasoning; rather, it scaled up the unsupervised training to improve knowledge and conversational ability. OpenAI essentially took a different direction for this release – emphasizing dialog flow, factuality, and even emotional intelligence (the ability to detect sentiment and respond sensitively). Sam Altman even remarked that GPT-4.5 "is the first model that feels like talking to a thoughtful person" in terms of its conversational tone.

So what does this mean in numbers? GPT-4.5 reportedly scored slightly lower on pure reasoning benchmarks than some specialized models. For example, it falls behind OpenAI's own chain-of-thought tuned model (often codenamed "o3-mini") on tasks like complex math proofs. However, GPT-4.5 improved in multilingual understanding and knowledge – it achieved 85.1% on a multilingual MMLU test, better than GPT-4's 81-82%. It also cut down the hallucination rate significantly (only ~37% hallucination on factual queries, down from 61% in GPT-4 Turbo). On MT-Bench style chat evaluations, GPT-4.5 is excellent, and it remains very strong on coding tasks. Interestingly, one report noted GPT-4.5 "outperforms o3-mini on a real-world coding benchmark (SWE-Lancer) but underperforms on pure coding puzzles" – suggesting that while it might not get the highest score on HumanEval, it is actually very useful for practical coding assistance where understanding the context and requirements (conversation) matters as much as writing correct syntax.

Anthropic's Claude 3.5 and 3.7 "Sonnet": Safe, Steady Improvement with Huge Context

Anthropic's Claude has always been pitched as a safer, more "aligned" AI assistant. Claude 2 (July 2023) brought 100k token context and very solid performance close to GPT-4, especially in areas like summarization and reasoning with long documents. In late 2024, Anthropic introduced Claude 3.5 "Sonnet", which significantly boosted Claude's capabilities. By October 2024, Claude 3.5 was outperforming even GPT-4 on some evaluations, and beating its predecessor Claude 3. For example, Claude 3.5 was reported to score slightly higher than GPT-4 on certain coding benchmarks and had become "the industry bar" for many tasks. One source noted Claude 3.5 got 92% on HumanEval vs GPT-4's ~90% – an impressive feat for Anthropic.

In February 2025, Anthropic released Claude 3.7 Sonnet (sometimes just called Claude 3.7). This model further refines the AI, especially in areas of tool use and coding. Claude 3.7 is described as "our most accurate model to reliably use tools" by Anthropic. In practical terms, Claude can now not only write code but also execute code or use a virtual browser, etc., as part of its responses (this is somewhat analogous to OpenAI's "code interpreter" plugin, but Anthropic built it into the model's skillset). Developers have found Claude 3.7 to be extremely good at tasks like reading documentation and then writing code or scripts to accomplish a goal. It also maintains the advantage of a 100K context window (meaning Claude can take in huge amounts of text as input, great for analyzing logs, books, or doing deep summarization).

On the key benchmarks: Claude 3.7 is very strong on MT-Bench (chat quality). On coding, it was noted as one of the top in March 2025 – some rankings put Claude 3.7 and GPT-4 nearly neck-and-neck for best coding assistant AI. However, Claude's coding style tends to be verbose (it likes to explain its reasoning), which some automated evaluations penalize. For math (GSM8K), Claude is slightly behind GPT-4, but not by a huge margin – perhaps in the 80% range. On MMLU, Claude 3.5/3.7 were in the high 80s%. One interesting point: Anthropic has continuously emphasized harmlessness and factuality, so Claude is less likely to produce offensive or extremely wrong answers (it has a high truthfulness on simple QA as indicated by internal evaluations). For businesses that prioritize safety, Claude is a popular choice.

Claude is available via API (and through partners like AWS Bedrock, where Claude is offered as a managed model). Anthropic's business model allows a lot of usage for free via certain channels, which gave Claude a user base in consumer assistant apps as well. By early 2025, Anthropic also started offering Claude Instant 100k, a faster/cheaper variant that still has the large context. This has made it feasible to use Claude for tasks like processing codebases to text and answering questions, without splitting the code into too many chunks.

In summary, Claude 3.7 Sonnet stands as one of the top LLMs, especially in conversational and coding domains. It might not absolutely top GPT-4 on every benchmark, but it's very close and sometimes better (as seen in HumanEval and some chat ratings). With its massive context window and focus on tool use, it's extremely practical. For example, a developer could ask Claude to analyze a 50,000-line log file (just by pasting it, thanks to 100k context) and get insightful summaries or anomaly detection – a task that would be hard for most other models without splitting the input.

Google's Gemini 2.0 and 2.5: Multimodal and Expansive Context

Google (now in collaboration with DeepMind) has upped its game with the Gemini family of models. After the release of PaLM 2 in 2023, Google worked on Gemini, a suite of multimodal models intended to compete directly with GPT-4. As of April 2025, Google has released Gemini 2.0 (various variants like Flash, Pro) and even previewed a Gemini 2.5 Pro Experimental. These models are showing cutting-edge performance and unique capabilities.

One standout feature of Gemini is its unprecedented context window. The Gemini 2.0 Pro model offers up to 2 million tokens of context in the experimental version. This is not a typo – 2 million tokens (equivalent to reading an entire library of documents in one go). In practical terms, it means Gemini could ingest huge datasets, codebases, or even hours of audio transcripts at once. Google achieved this via a combination of efficient attention mechanisms and likely some external memory system. The Gemini 2.0 Flash models, which are optimized for speed, have a slightly lower but still massive context (around 1 million tokens). For most tasks, 1M vs 2M tokens doesn't matter, both are essentially "unlimited" for human-sized inputs. The key is: Gemini can handle more information in a single prompt than any other public model, which opens up new use cases (like analyzing whole databases, performing huge code refactors, or truly lengthy conversations that span novels of text).

Gemini is also multimodal – it was designed to handle text, images, and even audio/video inputs. As of early 2025, the text+image input is generally available (for example, you can give Gemini an image and a question about it). Google demonstrated Gemini's multimodal prowess in tasks like describing diagrams, interpreting charts, and even controlling robot actions via natural language. Essentially, it's the culmination of Google DeepMind's research in vision-language models (like Flamingo, Perceiver, etc.) integrated with a powerful LLM.

On benchmarks, Gemini 2.0 Pro and 2.5 Pro are at the very top. The Chatbot Arena leaderboard at the moment has Gemini 2.5 Pro (March 25, 2025) as the #1 model with Elo ~1437 – slightly above GPT-4 and Claude. It has excellent coding ability (some internal Google benchmarks gave it near GPT-4 level on Codeforces and HumanEval) and superb reasoning. One area Gemini has been tuned for is tool use: it can call external APIs, do web searches, and perform code execution during its reasoning process. In fact, Gemini has a mechanism to interface with tools like Google Search internally. This means when you ask Gemini a difficult question, it can decide to query a knowledge base or run a computation behind the scenes and then return the answer – a step towards an "agentic" AI. For example, given a complex math problem, Gemini might generate some Python code, execute it (via an internal sandbox), and use the result to answer the question correctly. This approach helped Gemini absolutely excel at challenges like GSM8K (it can solve nearly all of them by actually doing the math, not just guessing). So on GSM8K, Gemini likely tops the charts with ~98-99% accuracy when tool use is enabled.

Google's strategy has been to offer Gemini through its cloud platforms: Vertex AI and the Gemini app. As of Feb 2025, Gemini 2.0 Flash is generally available via API, and Gemini 2.0 Pro (experimental) is available to select users for advanced coding and prompting needs. They also introduced Gemini 2.0 Flash-Lite, a cost-efficient smaller model for those who don't need full power. All Gemini models currently output text (with image output like generation coming soon, according to Google's roadmap).

From a developer perspective, if you need the absolute best model for, say, coding or complex multi-step tasks, Gemini is now a strong contender to consider alongside OpenAI and Anthropic's offerings. For instance, imagine you have a large codebase and you want an AI to not only answer questions about it but possibly refactor it or insert new features. With Gemini, you could in principle dump the entire repository (millions of lines) into one prompt thanks to the huge context, and ask it to perform the modifications. This might not be 100% reliable yet, but it's the direction things are going. (And yes, you would probably use a tool to convert that git repo to text and feed it in a structured way to Gemini.)

Google's prior model, PaLM 2, shouldn't be forgotten – it's still powering Google Bard and other products. PaLM 2 had strong multilingual and reasoning skills (even reportedly surpassing GPT-4 on some reasoning benchmarks like WinoGrande and XCOPA). But Gemini is the future, combining those language skills with multimodal and agent abilities. The code-named smaller version "Gemma" (like Gemma-3 27B mentioned in leaderboards) indicates Google is also making smaller fine-tuned models for specific domains – possibly for internal use or specific products.

xAI's Grok and Other New Challengers

Elon Musk's new AI venture, xAI, entered the scene with a model called Grok. In late 2024 they launched a preview of Grok-3, initially to a limited set of users on X (Twitter) as a chatbot for premium subscribers. Grok's design philosophy, per Musk, was to be a "maximum truth-seeking AI" with a bit of wit (they literally said it's designed to answer using humor at times). While early skepticism was high, Grok-3 has shown surprising progress. By Feb 2025, Grok-3 (Preview) was actually ranking extremely high on some leaderboards – it achieved an Elo of 1402 on Chatbot Arena, putting it virtually tied with GPT-4 and just behind the top models. This is an impressive debut, suggesting xAI's team (which includes former DeepMind and OpenAI engineers) managed to fine-tune Grok to a very high quality, likely leveraging existing open models and additional training. Grok is proprietary, of course, and not widely available except through the X platform. It's hard to get exact benchmark numbers for it, but it seems very capable in coding and conversation (some reports show Grok's coding score nearly matching GPT-4's). However, one area Grok might lag is in specialized reasoning tasks – it's not clear if xAI trained it with heavy RL on reasoning like others did. It also currently has a smaller context window (maybe 16k or so). Still, Grok is a project to watch as an independent alternative in the commercial space.

Real-World Applications and the Road Ahead

With such an array of powerful LLMs available, what are people doing with them in the real world? The short answer: almost everything you can imagine involving language. Here are some key application areas and how these top models stack up:

Coding Assistants: Perhaps one of the most impactful uses has been AI coding copilots. Models like GPT-4, Claude 3.7, and Gemini 2.0 Pro are being used to suggest code, find bugs, and even generate entire modules from scratch. For instance, a developer can paste a function or error message and get immediate suggestions for fixes. On the more adventurous side, companies are experimenting with letting LLMs handle large-scale code refactoring – something only feasible with models that have large context (to see the whole codebase). With tools to convert a codebase to text and feed it to an LLM, you could ask, "Hey AI, here's my entire repository, can you upgrade this app to use a new framework?" and the model could output the diffs for each file. We are not fully there yet in reliability, but the top models' HumanEval and Codeforces scores show they're getting closer to writing correct, complex code . In fact, specialized coding models (like a hypothetical fine-tuned GPT-4 just for coding) are hitting >90% success on coding challenges that a year ago only the very best humans could solve.
Data Analysis and Business Intelligence: Organizations are harnessing LLMs to query and summarize data. With models like GPT-4.5 and Claude, which are good at factual accuracy and summary, you can feed in raw data (sales figures, logs, etc.) and literally ask in plain English for insights. The model might generate a summary report or even SQL queries to run. Some forward-thinking companies combine LLMs with their databases: for example, they use a tool like repo2txt or similar to flatten a database schema or repository of reports, let the LLM ingest that, and then the LLM can answer questions like "What was our revenue growth in Europe last quarter and what were the main drivers?" The ability of LLMs to interpret the question, retrieve relevant info (via RAG or tool use), and present a coherent answer is revolutionizing business analytics.
Customer Service and Virtual Agents: Many customer support systems are now powered by these LLMs. A model like Claude or GPT-4 can be fine-tuned (or prompt-tuned) on a company's support knowledge base and then used to chat with customers. Because of their language understanding, they can handle complex, conversational inquiries that rule-based chatbots never could. And with reasoning abilities, they can troubleshoot issues step-by-step. The challenge has been ensuring they don't hallucinate incorrect info – but techniques like grounding the model's answers in a retrieved document (say, pulling the relevant support article text) have largely solved that. The result is customers get much more satisfying and immediate answers. Even voice-based assistants are now leveraging these models to sound more natural and handle follow-up questions seamlessly.
Content Generation and Creativity: For writing marketing copy, generating reports, or even drafting articles (👀), top LLMs are the go-to tool now. GPT-4.5's improved conversational style makes it great for producing engaging prose. Models like Gemini with multimodal capabilities can even generate image captions or suggest graphical elements to accompany text. Long-form content benefit from the models' ability to maintain context – with 100k+ tokens, an AI writer can keep track of a whole story plot or a comprehensive report outline without losing the thread.
Education and Training: LLMs are tutoring students, explaining concepts, and creating training materials. An AI like Grok or Claude can be an on-demand tutor that explains calculus problems or converses in Spanish for practice. Since these models can adjust the complexity of their language, they can cater to different learning levels. We're also seeing them being used to generate practice questions and answers, and even to grade essays with rationales. The conversational aspect (MT-Bench skills) is key here – students can ask why an answer is what it is, and a good model will break down the reasoning in a human-like way.
Agentic Tasks and Automation: Perhaps the most futuristic application is using LLMs as "brain" for agents that can perform actions. For example, some devs connect an LLM to a browser (so it can search the web) and a terminal (so it can run commands), and then give it a goal like "Find me the cheapest flight next month from NYC to London and book it". The LLM will plan a multi-step approach: search for flights, compare, maybe use APIs, and carry out the booking. Models that excel in benchmarks like Arena Hard and IF-Eval (which test following instructions and taking actions) – e.g. QwQ-32B or OpenAI's o1 models – are especially suited here. This is still experimental, but early results are promising. It's like having a really smart assistant that not only tells you what to do, but can do it for you (within a digital realm). Of course, guardrails and oversight are needed, which is why Anthropic's emphasis on safety is valued in such scenarios.

Given all these applications, there's also a burgeoning supporting ecosystem to help integrate LLMs. For instance, converting data from one form to another (like code repos to text, PDFs to text, etc.) is often necessary – again, tools like Repo2Txt or services that offer GitHub repo to txt online are commonly used by developers prepping training data or feeding context to models. Similarly, companies offering AI/ML services are in high demand: not every business has in-house expertise to fine-tune a LLaMA or to implement a GPT-4 solution, so they partner with consultants. An IT outsourcing company that offers full stack development services including AI/ML, web development, app development, and DevOps services can take a business problem and deliver an AI-powered solution. These partners handle the heavy lifting of setting up the model pipelines, whether it's hosting an open-source model on cloud infrastructure or integrating an API like Gemini into a mobile app. For example, IT Svit Group (itsitgroup.com) is one such firm providing end-to-end development and DevOps services – companies turn to these experts to efficiently implement LLM solutions without building everything from scratch in-house.

(The above highlights how working with firms experienced in full stack development services and DevOps services can accelerate AI adoption – they ensure that your use of an LLM is scalable, secure, and integrated with your existing software.)

Conclusion

As of April 2025, the world of LLMs is richer and more exciting than ever. We have open-source models (DeepSeek-R1, QwQ-32B, LLaMA 3.1) that democratize access to high performance, and we have commercial giants (GPT-4.5, Claude 3.7, Gemini 2.5) that continue to push the frontier with massive context windows and multimodal abilities. These models are not just lab curiosities scoring high on benchmarks – they are actively changing how software is built and how people interact with technology. From writing code and cracking math problems to conversing across languages and analyzing huge data troves, LLMs are becoming indispensable tools.

The key benchmarks we discussed – MMLU, MT-Bench, HumanEval, GSM8K – give us objective ways to track progress. And the progress has been astounding: just in the last year, benchmark ceilings have been raised repeatedly. Models are now exceeding 90%+ on MMLU and HumanEval, essentially mastering those challenge sets. When an AI can ace college exams, write correct code nearly every time, and chat fluently about virtually any topic – we are truly in new territory.

Of course, challenges remain. These models can still err (especially outside their training distribution), and concerns about factual accuracy, bias, and security are ongoing. The competitive race has fortunately encouraged work on these fronts too – e.g., reducing hallucinations (GPT-4.5 showed improvement there) and letting models cite sources or use tools to verify information.

For developers and businesses, the current landscape offers a spectrum of choices. If you need the absolute cutting-edge for a general AI assistant, you might call an API for GPT-4.5 or Gemini 2.5. If you need a customized solution (say, an AI that knows everything about your company's products or code), you might fine-tune an open model like Qwen-Plus or LLaMA-70B with your data. If you're cost-sensitive or want something small-scale, you might use a distilled model or a clever 7B like Phi-3 that's been pushed to its limits. The good news is that as of 2025, there is likely an LLM that fits almost every scenario.

Finally, it's worth emphasizing how important architectural innovations have been in this period. The introduction of reinforcement learning for reasoning (giving models a way to learn from trial and error on complex tasks) has markedly improved benchmarks. Mixture-of-Experts and sparse models are allowing scaling to trillions of parameters without proportional cost. Longer contexts and multimodality are expanding what tasks models can handle in one go. These innovations, coupled with massive training data and clever fine-tuning, are bringing us ever closer to the coveted goal of artificial general intelligence. While we're not there yet, one can't help but be impressed by how "smart" these systems have become in such a short time.

As we continue through 2025, we can expect even more exciting developments: perhaps OpenAI's GPT-5 or "Orion" (GPT-4.5's successor) will appear with combined reasoning and knowledge power. Maybe LLaMA 4 or Llama Maverick will set a new open benchmark. Or new players like IBM or Apple might throw their hats in the ring with specialized models (IBM is already hosting LLaMA 3.1 on WatsonX cloud, and there are whispers of an "Apple GPT" for on-device AI). One thing is certain – the rapid, repo-to-text evolution of LLMs will continue, and we'll keep seeing leaps on both the technical and practical fronts.

For now, the models we've discussed are the cream of the crop in early 2025. Whether you're a developer looking to build an AI-powered app, or an AI enthusiast experimenting with prompts, there's never been a better time to dive in and leverage these top-performing LLMs for your projects. The combination of their high benchmark scores and real-world effectiveness means they're not just benchmarks – they're tools ready to use. And with a bit of creativity, you can apply them to solve problems that once seemed intractable.

Other companies are also producing noteworthy models: Cohere, for instance, has its Command series. The latest Cohere Command model (March 2025), sometimes called Command X or Command R+, appears in benchmarks with an Elo ~1305. Cohere's models are aimed at enterprise use, with strong performance in text generation, summarization, and business document QA. They often emphasize reliable output and allow fine-tuning on their platform. While Cohere's 2025 models aren't topping GPT-4, they are competitive with second-tier models and can be more cost-effective.

AI21 Labs released Jamba (March 2024) which was a 52B-parameter model with a hybrid architecture (transformer + state-space model). Jamba was an experiment to reduce the cost while maintaining performance. It reportedly does well on tasks like reading comprehension and some logic puzzles, but hasn't become a mainstream choice yet. AI21's focus is also on specialized tasks (like their excellent Jurassic-2 models for writing assistance).

On the enterprise-focused front, Databricks/MosaicML open-sourced a 2024 model called DBRX (MPT-131B) which had 132B parameters. That model was mainly used on their platform as a proof-of-concept that you can train such a large model in a cost-effective way (thanks to MosaicML's training optimizations). Its performance is solid (in the range of GPT-3.5 on many tasks), but it wasn't fine-tuned to chase state-of-the-art on benchmarks.

Finally, Inflection AI's Pi model (mentioned as Inflection-2.5/3.0) is another interesting case. Inflection aimed for a highly personable AI for casual dialogue. Their model isn't usually in benchmark shootouts, but they claimed that by early 2024 it achieved 94% of GPT-4's performance with only 40% of the training compute – a testament to efficient scaling. Inflection's focus was more on product (the Pi chatbot) than on bragging rights, but under the hood their tech is quite advanced and might influence future efficiency techniques.

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