Best generative AI models at the beginning of 2026
Kamil Rzechowski
Software Engineer
Published: Jan 27, 2026|19 min read19 minutes read
With the rapid growth of generative AI, we have a new great model coming out every month. It makes it hard to keep track of all the different kinds of models and choose the right one for your task. In this article, I will cover the best generative AI models at the doors of 2026. I hope to make your selection of a model at least a bit easier.
One of the best LLM benchmarks is the Epoch Capabilities Index (ECI) from Epoch AI. It combines 39 benchmark scores into a single score. This creates the most generic capabilities evaluation, allowing for fair comparisons of LLM models. The ECI aggregates multiple benchmarks while taking into account the difficulty of tasks solved by each benchmark. It assigns higher ECI scores to models that perform better on harder benchmarks.
Source: https://epoch.ai/benchmarks. The ECI benchmark aggregates 39 LLM benchmarks into a one score
Based on the ECI, the leaderboard opens with commercial models from Google (Gemini 3 Pro), OpenAI (GPT-5.2), Antropic (Claude Opus 4.5), and xAI (Grok 4). However, we can see that the open source Qwen3-Max gets really close to the top-performing commercial models, making it an appealing choice for self-hosting solutions. Who knows, maybe in the upcoming year, we will see for the first time Chinese models taking over the leadership. Unfortunately two most recent open source models are missing: Kimi K2 Thinking and DeekSeek v3.2, so let's take a look at other benchmarks, to take them into account as well.
General-purpose models are great; however, we often want them to be task-specific. It becomes even more important in multi-agent systems, where we assign different responsibilities to each agent and narrow their responsibilities to expertise in a single domain. So let’s take a look at how the leaderboard changes if we change the task.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index combines 10 different benchmarks related to question answering. It includes, for example, Massive Multitask Language Understanding (MMLU) or GPQA Diamond. MMLU is a set of over 12,000 multiple-choice (mainly 10-choice) questions. Questions are drawn from academic exams and textbooks, and cover 14 diverse domains, including Biology, Business, Chemistry, Computer Science, Economics, Engineering, Health, History, Law, Math, Physics, Psychology, and Others. GPQA Diamond takes a very similar approach by evaluating the model on multiple-choice questions from various domains.
It is estimated that about 41% of code in 2025 was AI-generated. WIth rapid growth of AI code assistants and AI developers, it becomes important to have the best model for your development tasks. The Artificial Analysis Coding Index averages three coding benchmarks: LiveCodeBench, SciCode, and Terminal-Bench Hard. LiveCodeBench evaluates models on a variety of code-related scenarios, such as code generation, self-repair, test output prediction, and code execution. SciCode addresses the science coding challenges in Chemistry, Math, Physics, and Biology. For example, “Generate an array of Chem numbers for the Haldane model…”. Terminal-Bench Hard is an agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks. For example, “Build Linux kernel linux-6.9 from source. I've created an initial `initramfs.list` in the `ramfs` directory for you.”
Widespread adaptation of AI involves applying it to more and more challenging tasks, often involving interaction with the environment. For that purpose, LLMs need to reason, plan, operate through iterative cycles, use tools, and plan and adapt their behavior to the changing environment. With the task complexity growth, the single LLM pipeline might not be enough, and LLMs might need to cooperate with other specialised LLMs, called agents. For evaluating LLMs on agentic tasks, a good benchmark is the Artificial Analysis Agentic Index. It averages scores among Terminal-Bench Hard and 𝜏²-Bench Telecom benchmarks. Terminal-Bench Hard was already discussed in the previous section. 𝜏²-Bench Telecom simulates troubleshooting scenarios, where the agent and user simultaneously modify the shared world context, and the agent needs to guide the user to fix the problem.
Kimi K2 Thinking and DeepSeek 3.2 both perform well in different benchmarks, however struggle in terms of latency. The good explanation for that is that nobody took the effort to optimise them for inference purposes, while other models are highly optimised. We should keep in mind that those models were released 1 month ago, so it may take time to get well-optimized versions of those models.
So far, we’ve looked at the best LLM models. Let’s take a look now at the popularity of open source models. Checking the most downloaded models on Huggingface in the last 30 days, we get all-MiniLM-L6-v2, nsfw_image_detection, and electra-base-discriminator.
Source: Huggingface
all-MiniLM-L6-v2: is the sentence-transformers model. It is used to create 384-dimensional feature vectors, used for similarity search and clustering.
Nsfw_image_detections: is a BERT for images. It is an image transformer encoder adopted for image classification tasks, with the primary purpose of NSFW (Not Safe for Work) image classification.
Electra-base-discriminator: is the fake-real text discriminator. Electra is a training technique where text encoders are trained as Discriminators rather than generators. It is a similar approach to training GANs in computer vision. The text discriminator can be further finetuned on downstream tasks like classification tasks, QA tasks, or sequence tagging tasks
Bert-base-uncased: this model does not require much explanation. It is a transformer-based model pretrained on a large corpus of English data in a self-supervised fashion that can be fine-tuned. It’s meant for fine-tuning on the downstream tasks like sequence classification, token classification, or question answering.
fairface_age_image_detection: Detects age group with about 59% accuracy based on an image.
all-mpnet-base-v2: it is a sentence-transformer model that maps text to a 768 dimensional feature vector. It can be used for tasks like clustering or semantic search.
Mobilenetv3_small_100.lamb_in1k: lightweight model for image classification
paraphrase-multilingual-MiniLM-L12-v2: is the sentence-transformers model. It is used to create 384-dimensional feature vectors, used for similarity search and clustering.
Cllip-vit-base-patch32: model that learns image-text alignment. Used for zero-shot image classification or as a feature backbone for downstream tasks.
Segmentation-3.0: takes as an input a mono 10 sec audio and segments an audio recording to identify who spoke when
Text generation Huggingface top-5 in December 2025
All analysed benchmarks confirm Gemini 3 Pro, GPT 5.2, and Clause Opus 4.5 to be the best at the beginning of 2026 for both general-purpose and specific tasks related scenarios. It is interesting to see open source alternatives getting into the top-10 models. Interestingly, those models don’t get most downloads on Huggingface. Most probably due to their enormous size, which limits their applicability for most of the users. DeepSeek 3.2 got only 67,173 downloads in the last 30 days (although it was uploaded only 15 days ago), and Kimi K2 Thinking got 397,536 downloads on the Huggingface platform.
Although DeepSeek and 3.2 and Kimi K2 Thinking get good spots in QA, reasoning, intelligence, math, and agentic benchmarks, they fall behind in latency. Most likely, nobody has taken enough effort so far to optimise those models well. Not a lot of time has passed since their release.
The Huggingface top-downloaded models dominate smaller models that are easy to run locally. The leaderboard mainly consists of sentence-transformers and a few models for image classification and feature extraction. Interestingly, older models like BERT and MobileNet are still on top in terms of the number of monthly downloads.
