Published: June 13, 2026 | Last Updated: June 13, 2026
TL;DR: DataCore has launched airank.datacore.vn, a free real-time AI leaderboard tracking 527 models from 58 providers. This AI leaderboard aggregates benchmark data from Artificial Analysis, OpenRouter, METR, and Aider Polyglot to compare every frontier model across intelligence, coding, math, speed, price, and time horizon. Free, no account required.
What Is the DataCore AI Leaderboard?
The DataCore AI leaderboard, live at airank.datacore.vn, is a free, continuously updated ranking of frontier AI models. Built by DataCore - Vietnam's financial and economic data platform - the AI leaderboard consolidates evaluation data from four independent benchmark providers into a single, filterable table that any developer, researcher, or decision maker can use instantly.
As of today, the AI leaderboard covers 527 models from 58 providers: Anthropic, OpenAI, Google DeepMind, xAI (Grok), DeepSeek, Meta AI, Alibaba Qwen, Mistral AI, Cohere, Moonshot AI, and 48 more. Of those, 220 are open-weight models and 72 support multimodal input. Data on the AI leaderboard refreshes approximately every 10 minutes from its upstream sources.
The AI leaderboard is completely free. No registration, no paywall, no data required from visitors. Open airank.datacore.vn and start comparing models immediately - no setup, no account.
Why Does the AI Landscape Need an AI Leaderboard?
The number of frontier AI models has grown dramatically in 2025 and 2026. Every major AI lab - OpenAI, Anthropic, Google DeepMind, xAI, DeepSeek, Meta, Alibaba - ships multiple model variants per year, each optimized for different trade-offs between capability, speed, and cost. Choosing the right model for a task (code generation, document analysis, mathematical reasoning, long-horizon agentic work) means comparing dozens of providers, each publishing benchmark numbers in different formats, on different scales, and under different methodologies.
An independent AI leaderboard solves this by normalizing data across multiple evaluation sources into one comparable view. The DataCore AI leaderboard draws from four primary data sources:
- Artificial Analysis (artificialanalysis.ai) - Intelligence Index, coding and math scores, speed (tokens/second), and latency across the broadest model set.
- OpenRouter (openrouter.ai) - Live model catalog including context window size, supported modalities, and real-time pricing per million output tokens.
- METR - Time horizon: the length of task in human working hours that a model can complete autonomously with 50% reliability.
- Aider Polyglot (aider.chat) - Polyglot code-editing pass rate, testing real multi-language programming tasks.
No single source covers all four dimensions. The DataCore AI leaderboard is one of the few public resources combining intelligence, coding, agentic capability, speed, and cost into one place - making it the most complete AI leaderboard for practical model selection.
What Does the AI Leaderboard Track? Eight Dimensions
Intelligence: The Artificial Analysis Intelligence Index. The current AI leaderboard leader is Claude Fable 5 (Anthropic) at 64.9.
Coding: Code-generation quality. Claude Fable 5 leads the coding dimension on the AI leaderboard with a score of 62.
Code (Aider): Aider Polyglot benchmark - targeted code edits across multiple programming languages. GPT-5 Chat (OpenAI) leads at 88.0% pass rate.
Math: Mathematical reasoning across competition-level problems. GPT-5.2 xhigh (OpenAI) tops the math dimension on the AI leaderboard with a near-perfect 99.
Time Horizon: METR's agentic task metric - the task length in human hours a model completes with 50% reliability. Claude Opus 4.6 (Anthropic) holds the record on the AI leaderboard at 14 hours.
Speed: Output tokens per second. Mercury 2 is the fastest model on the AI leaderboard at 947 tokens/second.
Price: Cost per million output tokens, pulled from OpenRouter's live pricing feed. The AI leaderboard price column updates in near real time.
Context Window: Maximum token input per request. Llama 4 Scout (Meta AI, open-weight) leads the AI leaderboard with a 10 million token context window.
Current Top Models on the Leaderboard
The leaderboard composite score normalizes and weights all eight dimensions. Full methodology: airank.datacore.vn/methodology. As of today, the top three on the leaderboard are:
- Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) - Anthropic - Score 63.9 | Intelligence 64.9 | Coding 62
- GPT-5.5 (xhigh) - OpenAI - Score 59.8 | Intelligence 60.2 | Coding 59.1
- Claude Opus 4.8 (Adaptive Reasoning, Max Effort) - Anthropic - Score 59.7 | Intelligence 61.4 | Coding 56.7
Rankings on the leaderboard shift continuously. When a major new model releases, it typically appears on the leaderboard within hours. Check airank.datacore.vn for the current live ranking.
How to Filter the Leaderboard
The DataCore leaderboard includes seven filter dimensions you can combine to narrow 527 models down to an actionable shortlist:
- Provider - Filter the leaderboard by any of the 58 providers: Anthropic, OpenAI, Google, xAI, DeepSeek, Meta, Alibaba, Mistral, Cohere, Moonshot AI, and more.
- Modality - Show only multimodal models or text-only models. 72 of the 527 leaderboard models are multimodal.
- License - Open weights or proprietary. 220 models on the leaderboard are open-weight.
- Minimum intelligence - Set an Intelligence Index floor (40, 50, 60, or 70) to exclude weaker models from the leaderboard view.
- Maximum price - Filter by output cost (under $1, $5, $15, or $50 per million tokens).
- Minimum context window - Filter by context length (32K, 128K, 200K, or 1M+ tokens).
All filters can be combined. A developer needing a cheap, high-intelligence, open-weight model with a large context can narrow the leaderboard to an actionable shortlist in seconds.
Who Is the Leaderboard For?
Developers and AI engineers use the leaderboard to find the best model for production API calls or agentic workflows. Speed and Price columns make it fast to find the most cost-efficient option that meets the quality bar.
Product teams use the leaderboard for fast capability benchmarking before committing to deeper internal evaluation. Intelligence and Coding scores give an apples-to-apples comparison across all providers.
Researchers and AI practitioners tracking the frontier use the leaderboard to monitor progress across labs. The Time Horizon column is especially useful for teams building long-running agentic systems.
Business decision makers evaluating AI vendors use the leaderboard Compare feature (airank.datacore.vn/compare) to select any two or more models and view their trade-offs side by side across all eight dimensions.
Open-source AI enthusiasts tracking open-weight model progress will find the "Open weights" filter on the leaderboard particularly useful - 220 open-weight models including DeepSeek, Qwen, Llama 4, and Mistral are all tracked and ranked.
What Is Time Horizon on the Leaderboard?
Time horizon is METR's metric for agentic AI performance. METR (Model Evaluation and Threat Research) defines it as the length of task - in human working hours - that a model can complete autonomously with a 50% success rate.
A model with a 14-hour time horizon (the current leaderboard record, held by Claude Opus 4.6 from Anthropic) can autonomously complete a task that would take a skilled human 14 hours of focused work. This metric is increasingly important as organizations deploy AI agents on complex, multi-step tasks: data pipeline builds, code refactors, research synthesis, and business process automation.
The DataCore leaderboard is one of the few public leaderboards to include METR time horizon data alongside intelligence, cost, and speed - making it particularly useful for teams evaluating models for agentic deployment.
About DataCore - Builder of the Leaderboard
DataCore is Vietnam's leading financial and economic data platform, providing structured data on companies, markets, and the economy to financial institutions, research teams, and enterprise technology teams. DataCore's services - including Company Intelligence, Address, and eKYC - leverage frontier AI models, many of them tracked on the leaderboard.
The DataCore leaderboard grew from an internal need: DataCore's own data pipelines run on frontier models and the team needed a systematic way to evaluate new releases. Rather than keeping those evaluations internal, DataCore published them as a free public resource.
For more on DataCore's approach to AI in Vietnam's financial sector, visit the DataCore blog.
How does DataCore rank frontier AI models in real time?
The DataCore AI leaderboard scores every model on a common set of dimensions, then updates the ranking as new results arrive. Instead of a one-off snapshot, it recalculates standings continuously so the order reflects the latest data.
Each model is evaluated on capability benchmarks, speed, cost, and context window, among other signals. Normalizing these onto comparable scales is what lets very different models sit on the same ranking.
Because providers ship updates often, a static list goes stale within weeks. Real-time scoring is the core reason the ranking stays useful between major model releases.
What counts as a frontier model?
A frontier model is one of the largest, most capable general-purpose systems at the cutting edge of what is publicly available. These are the models that set the pace on hard reasoning, coding, and multimodal tasks.
The ranking covers both proprietary and open-weight frontier models, so teams can compare a closed API against a model they can self-host on the same footing.
How to use the ranking for model selection
The best model on paper is not always the best model for a given job. A high-accuracy model may be too slow or too expensive for a high-volume workload, while a cheaper model may be ideal for routing or classification.
The practical workflow is to start from the constraint that matters most, cost, latency, accuracy, or context length, sort by it, then check the trade-offs on the other dimensions before committing.
Open-weight versus proprietary models on the ranking
Open-weight models can be downloaded and run on your own hardware, giving control over data and cost. Proprietary models are reached through an API and often lead on raw capability but lock you into a vendor.
Putting both on one ranking makes the trade-off explicit: you can see exactly how much capability you give up, if any, by choosing a model you can self-host.
What are the limits of any AI leaderboard?
Benchmarks measure what they measure, not everything. A model can top a public benchmark and still underperform on your specific data, especially when benchmark contamination inflates scores.
Treat the AI leaderboard as a shortlist tool, not a final verdict. The reliable last step is always a small evaluation on your own tasks before you ship to production.
Why benchmark scores alone do not pick your model
Two models with near-identical benchmark scores can behave very differently on your prompts, your formatting, and your latency budget. Aggregate scores hide this variance.
That is why the ranking pairs each capability score with cost and speed. The cheapest acceptable model usually beats the most capable one on total value for production workloads.
How often does the leaderboard update?
The ranking refreshes as new benchmark results, pricing changes, and model versions are published. That cadence is what separates a living AI leaderboard from a blog post that was accurate only on the day it was published.
What does real-time ranking actually mean here?
Real-time does not mean the standings flicker every second. It means that whenever a meaningful input changes, a new model version, a fresh benchmark result, or a price cut, the ranking is recomputed and republished rather than waiting for a manual quarterly update.
For fast-moving teams, that distinction matters. A model that was mid-table last month can jump after a single capability update, and a static list would simply miss it.
Where does the underlying model data come from?
The data is drawn from published benchmark suites, official provider documentation, and public pricing pages, then standardized so that scores, latency figures, and costs are stated on the same basis. Standardization is the unglamorous work that makes any cross-model comparison trustworthy.
When a figure cannot be verified from a primary source, it is left out rather than estimated. That keeps the ranking defensible and avoids passing guesswork off as measurement.
Bottom line: why a live ranking beats a static list
The value of the DataCore AI leaderboard is not any single number, it is the combination of breadth, fresh data, and comparable metrics in one place. With 527 frontier models scored on the same dimensions, you can move from a vague sense of which model is good to a defensible shortlist in minutes.
Use it to narrow the field, then validate the top one or two candidates on your own tasks. That two-step approach, screen on the ranking, confirm on your data, is the fastest reliable path to the right model.
Frequently Asked Questions - DataCore Leaderboard
What is the best AI model right now?
According to the DataCore leaderboard composite score (intelligence, coding, math, speed), Claude Fable 5 by Anthropic currently leads the ranking of 527 models. Rankings update continuously. Check airank.datacore.vn for the live top model.
What is the fastest AI model?
Mercury 2 is the fastest model on the DataCore leaderboard, reaching approximately 947 tokens per second as measured by Artificial Analysis under standardized conditions.
Which AI model has the largest context window?
Llama 4 Scout by Meta AI supports a 10 million token context window - the largest on the leaderboard - enabling processing of entire codebases or document libraries in a single API call.
What is AI model time horizon?
Time horizon is METR's agentic performance metric: the task length in human working hours a model can complete with 50% reliability. Claude Opus 4.6 holds the record on the leaderboard at 14 hours. For agentic deployments, time horizon is often more relevant than raw intelligence benchmarks.
Are open-weight models on the leaderboard?
Yes. The DataCore leaderboard tracks 220 open-weight models including DeepSeek, Qwen (Alibaba), Meta's Llama 4 family, Mistral, and GLM. Use the "Open weights" filter to compare them against proprietary models.
Where does the leaderboard data come from?
The DataCore leaderboard aggregates from Artificial Analysis (intelligence, coding, math, speed, latency), OpenRouter (catalog, live pricing, modalities), METR (time horizon), and Aider Polyglot (code-editing pass rate). All sources are credited and linked on the leaderboard.
Is the leaderboard free?
Yes. airank.datacore.vn is completely free. No account required. The full leaderboard, all filters, Compare feature, and model profiles are all accessible without signing up.
How many models does the ranking cover? It currently scores 527 frontier and near-frontier models, spanning major proprietary APIs and widely used open-weight releases, all on the same set of dimensions.
Can I compare models by cost alone? Yes. Sort by price and the cheapest options rise to the top, but always read the accuracy and latency columns next to the price so a low cost does not hide a quality or speed problem.
How is this different from a single benchmark site? A single benchmark answers one narrow question. This ranking combines capability, speed, cost, and context into one comparable view, which is closer to how a real model-selection decision is actually made.
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