Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

The best-performing model across these three metrics was a pair of independent linear trends: one for reasoning models and one for non-reasoning models.

We spent days loading the system with hundreds of threads, refining rough edges and polishing corners that developers may never see.

All of this runs at Zed's famously buttery-smooth 120 fps

run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025

run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025

run-rate revenue has now surpassed $30 billion, up from approximately $9 billion at the end of 2025

🔹 **Rich World Knowledge:** Leads all current open models, trailing only Gemini-3.1-Pro.

🔹 **Enhanced Agentic Capabilities:** Open-source SOTA in Agentic Coding benchmarks.

The depth of recursion becomes a tunable compute axis at inference time, requiring no retraining. A small model, by reading itself, can iterate toward answers that neither it nor any of its workers could reach in a single pass.

Two variants are available: **Sakana Fugu Mini 🐟**, optimized with latency in mind, and **Sakana Fugu Ultra 🐡**, the full orchestration system, optimized for performance for demanding tasks.

GPQAD | 94.4 | 90.9 | 92.7 | 92.4 | **95.1** | LCBv6 | 90.3 | 92.1 | 92.4 | 90.4 | **93.2** | SWEPro | 48.4 | 51.2 | _53.4_ | 51.3 | **54.2**

The Prompt API for the web is still being developed. While we build this API, refer to our best practices on session management for optimal performance.

Kimi K2.6 demonstrates significant improvements over Kimi K2.5 in internal evaluations conducted by CodeBuddy: code generation accuracy increased by 12%, long-context stability improved by 18%, and tool invocation success rate reached 96.60%.

The median US buyout fund returns 13% to 16% net.

DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

despite rapidly improving capabilities, coding agents remain inefficient in natural settings

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

V3.3 achieves 70.4% in Mode A (zero-LLM), with +23.8pp on multi-hop and +12.7pp on adversarial. V3.2 achieved 74.8% Mode A and 87.7% Mode C; the 4.4pp gap reflects a deliberate architectural trade-off.

GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

It also surpasses all peer-scale dense models by a wide margin.

With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark

It also surpasses all peer-scale dense models by a wide margin.

Among the latest frontier models, GPT-5.4 over-edits the most.

It is full of real people who have decided that being present is too time-consuming, so they have automated the performance of presence instead.

When evaluated directly in the Codex app, best-of-ten model submissions ranked above the 95th percentile of human experts on the prediction task and around the 84th percentile of human experts on the sequence generation task.

Opus 4.7 introduces a new `xhigh` ('extra high') effort level between `high` and `max`, giving users finer control over the tradeoff between reasoning and latency on hard problems.

On our 93-task coding benchmark, Claude Opus 4.7 lifted resolution by 13% over Opus 4.6, including four tasks neither Opus 4.6 nor Sonnet 4.6 could solve.

Gemma 4 E4B matches or exceeds GPT-4o across multiple benchmarks including MATH, GSM8K, GPQA Diamond & HumanEval

Multiple community tests show llama.cpp running 1.8x faster than Ollama on the same hardware with the same model, 161 tokens per second versus 89.

The 66.6% medal rate on MLE Bench Lite, achieved autonomously over 24 hour windows, tells you something real about how this model behaves when you give it a hard problem and step back.

MiniMax claims it has reduced live production incident recovery time to under three minutes on multiple occasions using M2.7.

Contemplating mode provides significant capability improvements in challenging tasks, achieving 58% in Humanity's Last Exam and 38% in FrontierScience Research.

A small model trained on fewer than 2,000 examples from real lawyers, bankers, and consultants recently beat all but the best frontier models on corporate legal work, at a fraction of the price.

We see continued gains from inference scaling on larger projects, suggesting they may be solvable given enough tokens.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86. The fusions eliminate intermediate writes that pollute the cache, making the hot paths more predictable.

Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.

The variance is also worth noting: baseline+FA TG has ±19 t/s of noise, while optimized+FA has ±0.59 t/s on x86.

A healthcare LLM might be highly accurate for queries in English, but perform abominably when those same questions are presented in Spanish.

Performance: dev-browser: 3m53s, $0.88, 100% success rate — beats MCP configs, Chrome extensions, 'browser skill' stacks.

推理速度提升 2.5 倍

GLM-5.1 pushes this frontier further, delivering 3.6× speedup and continuing to make progress well into the run. While its rate of improvement also slows over time, it sustains useful optimization for substantially longer than GLM-5.

GLM-5V-Turbo 拿了 94.8 分，Claude Opus 4.6 是 77.3。差距不小。

Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore's Law would predict only about a 5x improvement over this period. We saw 50x.

70% of alerts resolved in under 5 minutes

We improved Claude's RCA accuracy by 12pp on OpenRCA

Using these ability scores, the method predicts performance on new tasks with ~88% accuracy, including for models such as GPT-4o and Llama-3.1.

I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters

experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.

We projected that, given 13 GB300 GPUs, FP8 precision, physical error rate of 0.003, 1000 rounds, Surface code d=13, the fast model can achieve 0.11 μs / round.

Ising-Calibration-1 repeatedly outperforms state-of-the-art open and closed models of a range of parameters. As shown in Figure 1, Ising Calibration 1 scores 3.27% better on average than Gemini 3.1 Pro, 9.68% better than Claude Opus 4.6, and 14.5% better than GPT 5.4.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

Cost (USD) to run the evaluation: GPT-5.4 (xhigh): $1,110, Claude Opus 4.6 (max): $1,055

You don’t realise how slow/laggy VSCode and the all of the forks are until

Uni-1 ranks first in human preference Elo for Overall, Style & Editing, and Reference-Based Generation, and second in Text-to-Image.

Cai et al. [117] interviewed 21 pathologists who used a deep neural network to aid in thediagnosis of prostate cancer. The interviews showed that pathologists needed to learn moreabout the network’s strengths and limitations to use it effectively. They also wanted to knowthe design objective of the network and the kind of data on which it was trained.

TriAttention matches Full Attention reasoning accuracy while achieving 2.5x higher throughput or 10.7x KV memory reduction

We've seen customers go from 10-20% field accuracy with a frontier model to 99-100% just by switching to using Reducto's Deep Extract.

For the documents that matter most, it gets to 99–100% field accuracy, even out-performing expert human labelers on extraction tasks.

Experimental results show the best model, Gemini3-pro, achieves 56.3% overall accuracy, which falls significantly to 23.0% on Level-3 tasks

our approach improves Qwen3.5-4B from 63.8 percent to 66.7 percent (+2.9pp) and Qwen3-30B-A3B from 58.0 percent to 69.5 percent (+11.5pp)

the trained 4B model exceeding GPT-4.1 (49.4 percent) and GPT-4o (42.8 percent) despite being 50 times smaller

不是改画面元素，而是改角色在演什么

VMs provision in under 700ms from API request to ready machine.

NVIDIA yields unmatched inference throughput across the broadest range of workloads, from massive LLMs to advanced vision language models, to generative recommender systems and more, on industry-standard benchmarks.

Co-designed hardware, software, and models are key to delivering the highest AI factory throughput and lowest token cost. Measuring this goes far beyond peak chip specifications.

Byte for byte, the most capable open models

When the sudden drop to a pianissimo occurred towards the ending of the piece, the perceived arousal responses of CHM and WM dropped slightly but rose again immediately to end on a high arousal. These two groups of listeners appear to have anticipated a return to a loud and majestic close and therefore kept their arousal responses higher than those of the NM.

CHM, who are more experienced with the instruments and compositional techniques used in Chinese orchestral music, might have had an idea of which features figure more prominently in the communication of particular intentions, and therefore would have more information available for their judgments.

The perception of affective intentions in music is influenced by the degree of familiarity listeners have with a musical tradition, the content implicated in the music, and the complex sonic environment created by the composer's creation and the musicians' interpretation.

The version that participants heard was a premier of the work by the Taipei Chinese orchestra.

The communication of emotions or affect takes place when listeners perceive emotional meaning that is expressed by performers in music (Juslin, 2013a, 2013b).

actos de extrañeza

Lua is more dynamic than Wren which makes its job harder. Lua also tries very hard to be compatible across a wide range of hardware and compilers. If you have a C89 compiler for it, odds are very good that you can run Lua on it. Wren cares about compatibility, but it requires C99 or C++98 and IEEE double precision floats. That may exclude some edge case hardware, but makes things like NaN tagging, computed gotos, and some other tricks possible.

Languages come in four rough performance buckets, from slowest to fastest

This makes questions like “how fast is WebAssembly” a bit hard to answer. You don’t ask how fast algebraic notation is—it’s not a very sensible question. Taken in the context of something like JavaScript, the language is only as fast as the engine running it. JavaScript the language has no speed, but you can benchmark JS engines like V8, SpiderMonkey, and JavaScriptCore. You can benchmark the IO libraries of JS runtimes like Bun, Deno, and Node. What people actually mean is “how useful are the constructs of this language to efficient mappings of modern hardware” and “what is the current landscape of systems taking advantage of these constructs”.

Deleting unnecessary work is almost always more impactful than doing necessary work faster. The fastest code is code that never runs. Before you optimize, question whether the work should exist at all.

It’s painfully slow (on a newish mobile).

Vite's job is to get your source modules into a form that can run in the browser as fast as possible. To that end, we recommend separating static analysis checks from Vite's transform pipeline.

Vite uses esbuild to transpile TypeScript into JavaScript which is about 20~30x faster than vanilla tsc, and HMR updates can reflect in the browser in under 50ms.

Above: the time to do a production bundle

making the future like Judith Butler’s famous observations about gender: ‘real only to the extent that it is performed’ (Butler, 1988, p. 527)

Simply choosing a more efficient programming language speeds up this calculation

In my brag document, I like to do this by making a section for areas that I’ve been focused on (like “security”) and listing all the work I’ve done in that area there. This is especially good if you’re working on something fuzzy like “building a stronger culture of code review” where all the individual actions you do towards that might be relatively small and there isn’t a big shiny ship.

it's a linear increase in performance and the reason I mentioned that is because as probably know that's the signature of unconscious learning

If one profiles what is going on in this whole process, it becomes clear that I/O dominates. Of the cpucycles expended, most go into conversion to and from intermediate file formats.

“When mixed into the plaster, the aerogels can reduce thermal conductivity by a factor of two when compared to polystyrene; that is truly huge.

The perfect index for the above query would be a multi-column index spanning all three columns in matching sequence and with matching sort order:

j'ai fait plusieurs travaux sur ces questionsl et ce slide en résume un rapport de 150 pages 00:19:33 sur que font les systèm éducatif qui appartenaient au cadrant en haut à droite que je vous ai montré au début donc ces systèmes éducatifs dit meilleur qui sont à la fois performants et équitables on est allé regarder ce 00:19:45 qu'ils font avec leur profession enseignante qu'est-ce qu'ils font de leurs enseignants quelle politique pour viser les enseignants et bien il y a une variété d'approches très importantes mais on a trouvé trois éléments communs 00:19:57 on a on a trouvé que c'est dans ces pays-là les enseignants avaient une expérience pratique longue obligatoire pendant leur formation initiale donc ils sont face à une salle de classe dès leur 00:20:10 formation initiale avec un tuteur et ils progressent et cette expérience pratique est longue et j'insiste là-dessus c'est pas l'histoire de faire 3 semaines un mois et cetera en sal de classe euh je pense que oui c'est au 00:20:24 minimum un semestre alors là aussi il y a des variétés mais oui minimum un semestre 1 an idéalement peut-être 2 ans oui j'ai plus les détails en tête euh aussi les enseignants peuvent suivre 00:20:38 des formations continues qui répondent à leurs besoins donc pas des formations continues qui sont proposées voilà sur le plan national mais qui viennent répondre aux besoins d'un établissement en particulier l'établissement dans lesquel il se trouve et dernier point 00:20:52 les mécanismes d'évaluation des enseignants sont reliés à la formation continue et à ce qui leur est proposé en terme de formation continue on fait le lien entre ben voilà on a vu des vous pouvez vous améliorer dans tel domaine 00:21:03 on va vous prer proposer une formation continue qui répond à ce besoin

on est tous et 01:02:17 toutes dans une anxiété de performance et dans une anxiété de compétition sociale euh et donc bah ça voilà ça c'est la seule façon d'y arriver c'est de vraiment ne pas se soucier de ce de 01:02:31 cette compétition mais mais ça veut dire qu'on va jamais nous organiser pour changer les choses donc d'où le danger de développement personnel alors du coup non parce que plus on plus on plus 01:02:46 on va bien et moins on veut s'organiser c'est c'est voilà plus on est en colère c'est un peu aussi que c'est les gens passer un militant sont en colère mais justement parce que c'est un peu important de de garder cette émotion pour s'organiser

Aunque Brysk (2013) se pregunta si las redes sociales puedenser utilizadas como una herramienta performativa (o como unamáscara) para reclamar derechos –ya que estas pueden generaruna respuesta emocional, aun cuando ese momento de cuidadopuede evaporarse rápidamente sin tener un efecto duradero–,propongo que la performance de ser persona –en su carácter de más-cara que permite reclamar derechos– no es el único acto de hablaa través del cual afirmar la propia humanidad.

Practice, learning, getting better at what you do is hard work and not “fun” or “flow”. “Flow” is for performance, rather than practice, for when you’re on stage, rather than in the rehearsal studio.

Don't worry about performance too early though - it's more important to get the design right, and "right" in this case means using the database the way a database is meant to be used, as a transactional system.

The only issue left to tackle is the performance issue. In many cases it actually turns out to be a non-issue because of the clustered index on AgreementStatus (AgreementId, EffectiveDate) - there's very little I/O seeking going on there. But if it is ever an issue, there are ways to solve that, using triggers, indexed/materialized views, application-level events, etc.

climate change performance index

The next point when using a parallel execution model like multiprocessing or sub interpreters is how you share data.

Interpreting accuracy is one of the most commonly used indicators of cognitive demands in experimental interpreting studies. One possibility to assess interpreting performance is to analyse interpreting accuracy based on meaning units. The methodological approaches used thus far, however, have some drawbacks: (a) they are limited to an assessment of sense consistency with no indication of the logical cohesion of the rendition, (b) they do not take into account the difference between unintended and strategic omissions or, more generally, the prioritization of source speech information as an interpreting strategy, and (c) they do not allow for the observation of fluctuations of cognitive load or effects of fatigue. In this article, we will present a refined approach to unit-based accuracy analysis that may contribute to solving the issues mentioned above.

While social media emphasizes the show-off stuff — the vacation in Puerto Vallarta, the full kitchen remodel, the night out on the town — on blogs it still seems that people are sharing more than signalling.

A more efficient but more complicated way to simulate perfect guessing is to guess both options simultaneously

TurboWish is a framework for profiling Rust programs, focused on illuminating the performance and resource usage of task-oriented code written with async/await.

Serializing the data with a function specialized to your data shape can be more than 10x compared with JSON.stringify.