
MuJoCo dev at @GoogleDeepMind | Ex @McLarenF1 | Maths PhD at @uniGoettingen | 🇮🇹 | Lived in 🇸🇪🇩🇪🇨🇭🇬🇧
London, England
Joined August 2013
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Alessio Quaglino retweeted
Google’s new Gemini 4 Argon equals GPT-6 Astra on the Artificial Analysis Intelligence Index at 60% of the Cost per Task with discounted prices. Google is now back to being one of the top three labs in intelligence achieved
Gemini 4 Argon is @GoogleDeepMind’s first proprietary model above the Flash class in over 7 months. With high reasoning (the highest available), it scores 53 on the Artificial Analysis Intelligence Index, matching GPT-6 Astra (max, 53) and 1 point ahead of GPT-6.1 Sol (max, 52), with gains driven by lower hallucinations and stronger agentic capabilities.
At its current 50% pricing discount and with cache discounts increased to 95%, Gemini 4 Argon costs $1.99 per Intelligence Index task, 60% of GPT-6 Astra (max), but 2.7x GPT-6.1 Sol (max). After the discount ends, this will rise to $3.98 (~1.2x GPT-6 Astra (max)).
Gemini 4 Argon is currently being rolled out to selected users and is not publicly available. The 50% discount is an initial promotion. Google has not yet confirmed the promotion end date
Key benchmarking results for Gemini 4 Argon with high reasoning:
➤ Google returns as one of the top three labs on intelligence: Gemini 4 Argon (high) scores 53 on the Artificial Analysis Intelligence Index, matching GPT-6 Astra (max, 53) and 1 point ahead of GPT-6.1 Sol (max, 52). This is 23 points above Google’s previous non-Flash model, Gemini 3.1 Pro Preview (30) and 12 points ahead of Gemini 3.8 Flash (high)
➤ Launch discounts of 50% make Gemini 4 Argon competitive on Cost per Task: At current discounted pricing, Gemini 4 Argon (high) costs $1.99 per Intelligence Index task, 60% of GPT-6 Astra (max, $3.26) for a comparable level of intelligence. This cost efficiency is driven by lower token prices, rather than reduced token use, with Gemini 4 Argon averaging 62k output tokens per task, compared with 27k for GPT-6 Astra (max). Google has not yet confirmed the promotion end date, but on standard pricing, Cost per Task will increase to $3.98
➤ Stronger agentic performance: Historically a weaker area for Gemini models, Gemini 4 Argon shows improvements across agentic evaluations. It ranks #1 on AutomationBench-AA at 77.5%, 6 points ahead of Claude Sonnet 5.5 (max, 71.3%). On Terminal Bench 4, Gemini 4 Argon achieves 57%, a +53 point improvement from Gemini 3.1 Pro Preview, only behind Claude Sonnet 5.5 (max, 64%), Claude Opus 5.5 (max, 60%) and GPT-6 Astra (59%). On AA-Briefcase, it reaches 1494 Elo. This is driven by a 65% rubric pass rate, the highest we have recorded, but lower Analytical Quality (1576 Elo) and Presentation Quality (1308 Elo)
➤ Lowest hallucination rate among leading models: On AA-Omniscience, Gemini 4 Argon has a 15% hallucination rate, the lowest of any model scoring 45+ on the Intelligence Index, compared with 51% for GPT-6 Astra (max) and 54% for GPT-6.1 Sol (max). This means Argon is much more likely to acknowledge when it does not know an answer rather than guess incorrectly. On accuracy, Gemini 4 Argon scores 50%, a 5 point decrease from Gemini 3.1 Pro Preview, and 13 points below GPT-6 Astra (max, 63%). With this slightly lower accuracy, its overall AA-Omniscience score of 42 remains in line with GPT-6 Astra (43) and GPT-6.1 Sol (42)
Key model details:
➤ Context Window: 1M tokens
➤ Multimodality: Text, image, video, and speech input, with text output
➤ Pricing: $4/$20 per 1M input/output tokens at standard pricing, currently discounted 50% to $2/$10. Cached input tokens receive a 95% discount ($0.10 per 1M at discounted pricing), up from 90% on Gemini 3.8 Flash
➤ Long Decode Continuation: We tested Gemini 4 Argon with Long Decode Continuation, a new Gemini API feature that pauses long responses and resumes them across follow-up calls. This lets reasoning run up to 1M output tokens without request timeouts
Weapons of Mosquito Destruction, by @tomaspueyo open.substack.com/pub/unchar…
Possibly the most interesting post I've ever read by @profplum99 open.substack.com/pub/michae…
Alessio Quaglino retweeted
Google DeepMind 🤝 @BostonDynamics
Our new research partnership will bring together our advancements in Gemini Robotics’s foundational capabilities to their new Atlas® humanoids. 🦾
Find out more → goo.gle/49paguA
Alessio Quaglino retweeted
We're excited to share that MuJoCo Warp is available at HEAD in MJX and for all environments in MuJoCo Playground. See the post here github.com/google-deepmind/m…
Alessio Quaglino retweeted
I’m so excited to announce Gemma 3n is here! 🎉
🔊Multimodal (text/audio/image/video) understanding
🤯Runs with as little as 2GB of RAM
🏆First model under 10B with @lmarena_ai score of 1300+
Available now on @huggingface, @kaggle, llama.cpp, ai.dev, and more
Alessio Quaglino retweeted
We're also launching the Gemini Robotics software development kit (SDK) to help developers fine-tune the model for their own applications, including by testing it in the MuJoCo physics simulator. 🌐
Alessio Quaglino retweeted
We’re bringing powerful AI directly onto robots with Gemini Robotics On-Device. 🤖
It’s our first vision-language-action model to help make robots faster, highly efficient, and adaptable to new tasks and environments - without needing a constant internet connection. 🧵
Alessio Quaglino retweeted
We built an AI model to simulate how a fruit fly walks, flies and behaves – in partnership with @HHMIJanelia. 🪰
Our computerized insect replicates realistic motion, and can even use its eyes to control its actions.
Here’s how we developed it – and what it means for science. 🧵
Alessio Quaglino retweeted
📣MuJoCo announcement 📣
Thrilled to share that @GoogleDeepMind has unveiled MuJoCo-Warp at @nvidia's #GTC25!
🚀 We've expanded our open-source MuJoCo simulator with MuJoCo-Warp, leveraging NVIDIA’s Warp framework for incredible acceleration. This marks a significant step in making high-performance simulation more accessible.
Alessio Quaglino retweeted
The ultimate test of any physics simulator is its ability to deliver real-world results.
With MuJoCo Playground, we’ve combined the very best: MuJoCo’s rich and thriving ecosystem, massively parallel GPU-accelerated simulation, and real-world results across a diverse range of robot platforms: quadrupeds, humanoids, dexterous hands, and arms.
Best of all? You can get started today with a single command: pip install playground
playground.mujoco.org/
Alessio Quaglino retweeted
Yesterday the hyped Genesis simulator released. But it's up to 10x slower than existing GPU sims, not 10-80x faster or 430,000x faster than realtime since they benchmark mostly static environments
blog post with corrected open source benchmarks & details: stoneztao.substack.com/p/the…
Alessio Quaglino retweeted
MuJoCo 3.1.6 has just been released! MJX now comes with out-of-the-box support for height fields. Check out this joystick policy that @the_real_btaba trained (~2M steps per second on an A100).
Colab to reproduce: colab.research.google.com/gi…
Alessio Quaglino retweeted
The G1 humanoid from @UnitreeRobotics is now available in MuJoCo Menagerie: github.com/google-deepmind/m…
Alessio Quaglino retweeted
We just released a new version of #LocoMuJoCo now supporting the brand-new @UnitreeRobotics G1 humanoid 🤖
Train you own policies using imitation or reinforcement learning! We provide scripts to train the policies shown below 👇
pip install loco-mujoco
github.com/robfiras/loco-muj…
Alessio Quaglino retweeted
Our recommendation? @GoogleDeepMind MuJoCo.
After the update to version 3 the simulation of deformable objects has significantly improved!
It closely matches the groundtruth on both dynamic (left) and quasi-static (right) tasks and allows importing cloth meshes.
Alessio Quaglino retweeted
It's always great to look back on the year in a year-in-review blogpost with @JeffDean & James Manyika. It's been an amazingly productive year for us, doing awesome research, shipping products and advancing science - 2024 is going to be incredible!
deepmind.google/discover/blo…
Alessio Quaglino retweeted
Just released a MuJoCo implementation of end-effector control with differential inverse kinematics. It's blazing fast, super readable, has 0 external dependencies and best of all, it's just a few lines of code: github.com/kevinzakka/mjctrl…
Alessio Quaglino retweeted
The newest version of Bard is here - it’s far more capable at things like understanding and summarising, reasoning, coding, and planning ✨
Try it today ↓
bard.google.com/















