How long can an AI agent sustain useful work? Not minutes — days. That’s the question Vals AI is asking with its new Time Horizon Index, a benchmark designed specifically to evaluate AI agents on long-horizon tasks. The first challenge: build and run a space program in Kerbal Space Program. The time limit: five days.
The results are fascinating, humbling, and arguably more honest about frontier AI capabilities than most benchmarks we’ve seen.
What Is the Time Horizon Index?
The Time Horizon Index measures how far an AI agent can progress through a complex, multi-step task over an extended real-time window. The methodology draws inspiration from METR’s work on agent evaluation, applying a real-world task with objective scoring criteria, a separate referee process to prevent tampering, and a time horizon long enough to surface genuine sustained capability — not just burst performance.
The first game in the index is Kerbal Space Program (KSP), a rocket-building and orbital mechanics simulation. KSP is a particularly good evaluation environment because:
- It tracks physical constraints (mass, thrust, fuel, aerodynamics, heat) that can’t be shortcut
- Engineering tradeoffs are real: more fuel means more range but also a harder launch
- Success criteria are verifiable (did the Kerbal land and return safely?)
- The game has no shortcuts to the scoring progression
The Challenge: 30 Rungs, 5 Days
Agents are given five days (120 real hours) and a fixed 30-rung mission ladder. The ladder starts at the Mun:
- Rungs 1–2: Mun landing, then Mun landing with safe Kerbal return
- Rungs 3–4: Same missions for Minmus
- Rungs 5–28: Repeat the landing-and-return cycle for each of 12 additional celestial bodies (Duna, Ike, Gilly, Dres, Eeloo, Moho, Vall, Bop, Pol, Laythe, Tylo, Eve)
- Rung 29: Asteroid capture
- Rung 30: Single-Kerbal grand tour
Each completed rung scores one point (3.33%). Partial credit is awarded for reaching a target’s sphere of influence, entering orbit, or landing — even without a successful return.
Vals AI estimates an expert human needs 90–120 hours to complete the first 28 rungs. The last two (asteroid capture and grand tour) sit outside that estimate — they’re genuinely hard even for humans.
Agents interact with KSP by writing .craft files (ship designs) and Python flight scripts. The work is tool-use, not button-pressing.
Current Standings
Updated July 18, 2026:
| Model | Progress | What It Accomplished |
|---|---|---|
| Claude Opus 4.8 | 11.83% | Mun landing, Mun return, Minmus landing, Minmus liftoff |
| GPT 5.5 | — | Entered Minmus orbit |
| Grok 4.5 | — | Survived Kerbin reentry on Mun return (did not land safely) |
| Muse Spark 1.1 | — | Began Mun descent |
Claude Opus 4.8 leads the field by a meaningful margin — it’s the only model to complete the Minmus return phase. But “leading” at 11.83% means 88% of the ladder remains untouched by any current frontier model. No model reached another planet.
What This Tells Us About Long-Horizon AI Capability
The benchmark is designed to be honest in a way that short-horizon evaluations often aren’t. A few key observations:
Context degradation is real. Over 120 hours of work, an agent accumulates a massive context window worth of trial, error, and intermediate state. The KSP challenge exercises something that most benchmarks don’t: the ability to maintain coherent progress across a very long operational session without losing the thread.
Physical simulation is a genuine frontier. KSP isn’t a knowledge recall task — it requires understanding orbital mechanics well enough to write working flight scripts. The fact that no model reached another planet suggests that multi-step physical planning at this complexity level remains genuinely difficult.
11.83% is a humbling ceiling. The best model in the world right now gets roughly one-ninth of the way through what a skilled human can accomplish in the same time. This is useful signal for anyone calibrating expectations about what autonomous AI agents can handle in extended real-world tasks.
The benchmark is live and will update. As new frontier models ship, Vals AI will add them to the leaderboard. This is a living benchmark — the standings here will look different in six months.
Why This Benchmark Matters
Most AI benchmarks measure peak capability on well-defined tasks with known answer formats. The Time Horizon Index measures something different: sustained, tool-use-intensive work over days, with compound dependencies between steps.
This is much closer to how AI agents are actually deployed in production — not as one-shot query responders but as persistent collaborators that need to maintain coherent progress on complex multi-step work. The gap between “passes coding benchmark X” and “can build and run a KSP space program over 5 days” is a useful way to think about what we’re actually asking of agents in enterprise deployments.
Vals AI has signaled that KSP is just the first game in the index. Future challenges are expected to cover other long-horizon problem domains.
Sources
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260722-0800
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