Best sites to track AI model releases

Decision in 20 seconds

The best sites to track AI model releases are organized by what you need to know: (1) for weights, benchmarks, and licensing on day one, use HuggingFace model hubs (huggingface.co/deepseek-ai, huggingface.co/Qwen, huggingface.co/mistralai) and official lab GitHub organizations — they publish model cards before media; (2) for the fastest news signal, TLDR AI sends a daily email within hours of a major release; (3) for Chinese lab releases specifically (Qwen, DeepSeek, Kimi, GLM), RadarAI covers them with deployment-relevant framing that English digests typically miss. In Q2 2026, the two most significant open-source releases were Qwen3 (April 2026, Apache 2.0, MMLU 87.1 for the 235B flagship) and DeepSeek-R1-0528 (May 2026, AIME 2024 pass@1 72.6%, MATH-500 97.3%) — both were trackable on HuggingFace before Western media coverage and verified in under 10 minutes by reading the model card.

Use this page when

• Setting up a monitoring system for open-weight AI model releases to evaluate for integration
• Verifying a specific benchmark claim before building a product on top of a model
• Tracking Chinese AI lab model releases that are not covered adequately in Western digests
• Comparing models for a specific task (code generation, math reasoning, long-context retrieval) using task-specific leaderboards

This page is not for

• Closed/proprietary model API updates (GPT-4o, Claude) — for those, use vendor changelog pages directly, not general tracking sites
• AI hardware and infrastructure news — use SemiAnalysis, The Next Platform, or AnandTech for chip and data center tracking
• AI company financial or corporate news — for acquisitions, funding, and market moves, use KR-Asia, TechCrunch, or Bloomberg

Key points

• HuggingFace model hubs are the single most reliable primary source for model releases: lab organizations publish model cards with benchmark data, weights, and license terms before any press coverage — bookmark specific org pages and use the "follow" feature for release notifications.
• The QwenLM GitHub organization (github.com/QwenLM) and DeepSeek GitHub (github.com/deepseek-ai) are the authoritative primary sources for Chinese open-source models — their release notes contain training details, benchmark methodology, and inference requirements not carried in secondary coverage.
• RadarAI tracks Chinese AI model releases for English-reading builders with deployment framing — context window, API cost, open-source license status — that is absent from most English digests that undercover Chinese labs until their releases become too prominent to ignore.
• The Hugging Face Open LLM Leaderboard (huggingface.co/spaces/open-llm-leaderboard) provides ongoing independent benchmark evaluation of open-weight models — scores are reproduced independently rather than lab-reported, making it a useful cross-check when a model card's benchmark claims seem high.
• LMSYS Chatbot Arena (lmarena.ai) is the best real-world ranking signal because scores come from actual human preference votes rather than automated benchmarks — models that score well on Arena but poorly on MMLU are typically better for chat and instruction-following tasks than the static numbers suggest.
• Papers With Code maintains task-specific benchmark leaderboards (code: LiveCodeBench; math: MATH-500 and AIME; reasoning: GPQA Diamond) that are updated with new model scores as labs publish evaluations — useful for tracking progress on specific tasks you actually use.
• GitHub star velocity (how fast a model repo gains stars in the first 48 hours after release) is an underrated signal for developer adoption momentum — a model gaining 5,000 stars in 24 hours signals genuine developer interest more reliably than PR press coverage.

What changed recently

• May 2026: DeepSeek-R1-0528 released on HuggingFace with full model card — AIME 2024 pass@1 72.6%, MATH-500 97.3%, GPQA Diamond 81.0%. Model weights publicly available. HuggingFace model card published simultaneously with the release blog post at blog.deepseek.com.
• April 2026: Qwen3 series launched across 0.6B to 235B parameter sizes, all under Apache 2.0 license. Critical deployment detail: Qwen3-30B-A3B is a MoE model with 30B total parameters but only 3B active at inference — this makes it significantly cheaper to serve than the parameter count implies. HuggingFace org (huggingface.co/Qwen) published cards for all sizes simultaneously.
• March 2026: Kimi k2 (Moonshot AI) added 128K context window support — model card published at huggingface.co/moonshotai. English coverage lagged primary source by 3-4 days.
• Ongoing 2026: The Open LLM Leaderboard v2 now evaluates on harder benchmarks including IFEval (instruction following), BBH (Big Bench Hard), and MATH Lvl 5 — older MMLU-only comparisons are less useful than v2 multi-benchmark scores for distinguishing top-tier models.

Explanation

Model releases in 2026 follow a consistent publication pattern that determines where you should look first. Labs publish model weights and model cards to HuggingFace and/or GitHub simultaneously with or before their blog announcement. The model card is the technical specification document — it contains benchmark scores, evaluation methodology, training data composition summary, license terms, and inference requirements. Everything in secondary coverage (media, newsletters, social media) is derived from the model card and the blog post, with varying levels of accuracy in the derivation.

The benchmark interpretation problem is significant for builders. Most media coverage reports MMLU scores without context, but MMLU scores require context to be meaningful: what shot count (0-shot vs 5-shot can differ by 5-15 points), which MMLU version (original MMLU vs MMLU-Pro have different difficulty distributions), and whether the evaluation was run by the lab or independently reproduced. The Open LLM Leaderboard solves the third problem by independently reproducing evaluations for open-weight models — if a model scores 85 on MMLU in the lab's report but 76 when independently evaluated on the Leaderboard, that is a meaningful signal about the reliability of the lab's benchmark process.

Chinese open-source models deserve specific attention because they represent the best cost-performance ratio for many specific tasks in 2026, yet are systematically undercovered in English-language tracking sources. Qwen3-30B-A3B (April 2026) is the clearest example: it uses a MoE architecture with 30B total parameters but only 3B active at inference time, which means it runs at the speed and cost of a 3B dense model while delivering benchmark performance comparable to much larger dense models. This efficiency advantage is obvious from the model card but was absent or buried in most English media coverage, which focused on the headline 235B flagship instead.

The LMSYS Chatbot Arena is valuable for a different reason than static benchmarks. Arena scores come from thousands of real human preference votes across a wide range of tasks — users chat with two anonymous models and vote for the one they prefer. This produces an ELO-style ranking that reflects real-world usability more accurately than MMLU or coding benchmarks designed to be reproducible but not necessarily representative of how models get used. A model with a high Arena score but modest MMLU is typically excellent for conversational and generalist tasks; one with high MMLU but low Arena may be benchmark-optimized in ways that don't transfer to real usage.

For builders tracking specific capability areas, Papers With Code task leaderboards are more useful than overall capability rankings. The LiveCodeBench leaderboard (live.code-benchmark.org) evaluates code generation on problems post-dating the training cutoff of most models, making it more honest about current capability than HumanEval scores on problems that may be in training data. The AIME 2025 benchmark is becoming a standard for math reasoning — unlike MATH-500 which has been in training data for most current models, recent AIME competition problems provide a genuinely held-out evaluation.

Model Release Tracking Source Selection

Match your tracking need to the right source. Mixing up these categories is the most common error: checking tech media for benchmark details, or checking Reddit for licensing terms, wastes time and produces unreliable information.

How to verify the answer

These are the direct technical publishing points for model releases — check here first before any secondary source:

Tools / Examples

• HuggingFace Model Hub — huggingface.co — Primary distribution platform for open-weight models. Follow lab organizations (deepseek-ai, Qwen, mistralai, meta-llama, google, moonshotai) for release notifications. Model cards contain benchmark data, licensing, inference requirements, and download links.
• QwenLM GitHub — github.com/QwenLM — Alibaba's Qwen model organization. Published Qwen3 series (April 2026) with full benchmark data, Apache 2.0 license confirmation, and technical report explaining MoE architecture and training approach. Subscribe to release notifications for same-day alerts.
• DeepSeek GitHub and HuggingFace — github.com/deepseek-ai + huggingface.co/deepseek-ai — Authoritative sources for all DeepSeek model releases. R1-0528 (May 2026) model card published simultaneously with training details, full benchmark suite, and weights. Contains methodology details absent from all secondary coverage.
• Open LLM Leaderboard — huggingface.co/spaces/open-llm-leaderboard — Independent benchmark evaluation of open-weight models. V2 evaluates on IFEval, BBH, MATH Lvl 5, GPQA, MuSR, and MMLU-Pro — harder benchmarks than the original leaderboard. Use for cross-checking lab-reported scores.
• LMSYS Chatbot Arena — lmarena.ai — ELO-style ranking from real human preference votes across thousands of model comparisons. Best signal for real-world chat and instruction-following quality. Includes both open and closed models. Updated continuously as new votes come in.
• Papers With Code Benchmarks — paperswithcode.com/sota — Task-specific state-of-the-art leaderboards updated as labs publish new evaluation results. Use for domain-specific tracking: LiveCodeBench for code, MATH-500 / AIME for math reasoning, MMMU for multimodal, GPQA Diamond for graduate-level science reasoning.
• RadarAI China AI Tracker — radarai.top/en — Weekly English digest covering Chinese lab model releases (Qwen, DeepSeek, Kimi, GLM, ERNIE) with deployment framing: context window, API availability, license status, and integration constraints. Fills the China AI coverage gap in other English digests.
• TLDR AI Newsletter — tldr.tech/ai — Daily email with 2-3 sentence summaries of the most significant AI news. Typically covers major model releases within 4-8 hours of publication. Best for same-day awareness without requiring active monitoring.
• LiveCodeBench — live.code-benchmark.org — Code generation benchmark using competition problems released after the training cutoff dates of current models — eliminates the training data contamination problem that makes HumanEval increasingly unreliable as a comparison tool. Updated monthly with new problems.
• r/LocalLLaMA — reddit.com/r/LocalLLaMA — Practitioner community running open-weight models locally. First user evaluations of new model releases appear here within hours — valuable for real-world performance signal on consumer and workstation hardware, prompt sensitivity observations, and early integration reports.
• Moonshot AI HuggingFace — huggingface.co/moonshotai — Official model cards for Kimi series. Kimi k2 (March 2026, 128K context) and prior Kimi releases tracked here. English documentation for Moonshot's models is cleaner on HuggingFace than on the main website.
• OpenRouter — openrouter.ai — Aggregates API access to 50+ models from multiple providers with per-model pricing. Useful for tracking which models have API availability and at what cost per token — provides a practical deployment reality check alongside capability benchmarks.

Evidence timeline

Sources

• RadarAI Methodology
• Sources & Coverage
• Signals Library

FAQ

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best sites to track AI model releases AI model release tracker 2026 how to track new AI models where to follow AI model releases AI benchmark tracking sites Chinese AI model releases English new AI models tracking website

Related

• Sites to follow China AI in English
• AI news sources for builders
• China AI companies to watch in 2026
• China AI news sources in English

Go deeper

• Open LLM Leaderboard v2 — independent benchmark reproduction
• Chatbot Arena — real-world human preference ranking
• LiveCodeBench — code generation without training data contamination
• RadarAI — Chinese AI model release tracking for English builders

Last updated: 2026-06-26 · Policy: Editorial standards · Methodology