SimuLangのご紹介：デスクトップ全体のためのPlaywright

Results

For pure models, higher success rate comes at a higher cost. Sai breaks that curve: the highest score on the chart, at less than two-thirds the cost of the models it beats.

Five points: Sai · Claude Opus 5 · GPT-5.6 Sol · Claude Opus 4.8 (reported by OSWorld) · GPT-5.5 (reported by OSWorld). X: API cost per task (USD). Y: partial score (%).

Sai achieves this better outcome-cost tradeoff by:

1. Neuro-symbolic planning: Sai uses ~1.5x fewer model calls on average than pure models by taking more actions per turn, using Simulang code as a powerful symbolic language for planning and executing longer subtasks.
2. Efficient caching and memory: Sai keeps the input size bounded by adaptive summarization and maintains a constant prompt prefix for as long as possible before summarization. Planning in code lets Sai maintain and access critical task information in runtime memory across the full run of a task.
3. Model orchestration: Sai invokes specialist models and interfaces for localizing UI elements, pure reasoning, and verification, thereby avoiding expensive models for steps where the full task context is unnecessary.

See it work: Where the score comes from

Two tasks showing Sai, GPT 5.6 Sol and Opus side by side, measured for reliability and efficiency.

1. Task 68: Chrome Dino Game

The agent has to play Chrome Dino, a repetitive real-time game, and clear a score target on a live page. Scoring is pass/fail on the final score, and all three agents scored 1.0, so what actually separates them is the cost of getting there. Sai finished in 23 model turns, Sol needed 46, and Opus 4.7 took 201.

Sai treated a repetitive real-time game as a programming problem instead of a clicking problem. Sai measured the ground line, the obstacle speed, and its own input latency from raw pixels, then wrote a control loop that captures the screen, detects obstacles, and jumps, and ran that loop on the VM inside a single execute call. Four versions of the code and 23 turns produced a 93-second run with 96 clean jumps and a final score of 768, nearly six times the target, and Sai read the score back off the page before it called the task done.

By contrast, Sol reached the same idea and then typed its bot into a terminal keystroke by keystroke through pyautogui.typewrite, spending five full rewrites on it. Opus 4.7 gave up on playing entirely, opened DevTools, set the game's score variable to 150 by hand, and sent it to the server. That score was written, not earned. The older evaluator passed it, but the current one zeroes any run that touches DevTools.

2. Task 28: Vaccine booking

Sai is for everyone, not just a lab result

Since 2023, Simular has been researching and pushing the frontier of Computer Use Agents (CUA). In December 2025, Agent S, Simular’s open-source CUA, became the first to reach human-level performance on OSWorld 1.0, scoring 72.6% against a human baseline at 72.36%.

Our research team builds and open-sources the Agent S family of frameworks, and our product team ships Sai, a computer agent that runs on its own cloud desktop and does real jobs for real customers. That combination keeps our research grounded in economically valuable work instead of demos. It also means we understand the entire stack of a computer agent: the models, the planning and grounding, the scripting layer, the virtual machines the agent runs on, and the user interface.

Today, there’s a gap between benchmark performance and real-world production needs. Simular and others have surpassed the human success rate on OSWorld 1.0, and now Sai leads OSWorld 2.0 – yet human-level speed, cost, and reliability remain our north star to be achieved. Benchmarks report pass@k, or success across independent attempts; but real work demands consistent success every time, even on the hundredth run. That gap is the problem our neuro-symbolic approach is built for.

Most work is repetition. Sai's strength is carrying out routine tasks reliably and cheaply: summarizing your inbox every morning, filing a month's worth of invoices. Our neurosymbolic method pairs the exploratory power of neural networks with the precision of symbolic code, resulting in reliability and cost saving. Once Sai completes a new task once, the solution exists as robust code that replays the same way every time, so processing the hundredth invoice does not cost what the first one did.

We founded Simular to liberate humans from digital labor – the repetitive clicking, typing, and moving the cursor. Liberating people from digital labor takes an agent that is not just capable, but reliable and cost-efficient too. That is the standard we are building toward.