Benchmarks measure whether a model can. ForgeBench measures whether an agent will finish.
A capability benchmark asks whether a model can produce the right answer. ForgeBench asks whether an agent finishes a task when the run has a budget, information costs something, and mistakes stay made. These are the properties of the environment that make that a different question.
Every action costs action points from a budget that does not refill. An agent can be capable and still run out.
Observing the world consumes the same budget as changing it, so an agent that spends its allowance looking never finishes.
A wasted action is not retried for free. Consequences carry forward through the rest of the run.
Decisions made early constrain what is reachable later, across tens of turns rather than one exchange.
The final obstacle requires resources gathered earlier. Arriving unprepared is a distinct failure from never arriving.
Read this before any number on this site.
ForgeBench predicts behaviour in ForgeBench, not in a buyer's stack. The external-anchor study that would license a broader claim has not established correlation.
No controlled study has produced data yet. The metrics and protocols below are defined and implemented; the studies that would turn them into findings are listed with their status, and none has run.
ForgeBench is built and run by ForgeAI, which also operates the dungeon competition the runs come from. No model provider funds, reviews, or approves these results. ForgeAI is not independent of the environment being measured, which is exactly why the scaffold, seeds, configuration, and replay evidence for every Official figure are published alongside it.
Completion rate is primary. The operational metrics describe how a run went, not just whether it ended well. Supporting metrics provide context and are never the headline.
Share of qualified runs that finished the dungeon.
Share of spent budget that went to actions which succeeded.
Share of runs containing at least one run of identical consecutive failing actions.
Share of runs that reached the terminal obstacle without what it required.
How much of a result is attributable to the harness rather than the model.
Share of steps that gathered information rather than changing the world.
Mean within-dungeon placement percentile across qualified solves, on a 0–100 scale.
Token usage and USD cost per run, and per solve where a run solved.
Each study's question and protocol are recorded before its result exists. A study that fails or comes back inconclusive is published the same way as one that succeeds.
"You measured the harness, not the model" is the standard objection to an agent benchmark. Rather than argue with it, ForgeBench declares the scaffold, versions it, stamps it on every run, and measures its effect as a study in its own right.
Read state, choose an action, submit, bounded parse retry. Nothing else. The model gets no help remembering, pacing, or checking itself.
The same model, wrapped in the scaffolding a competent agent builder would add. The delta against minimal is the measurement.
Changing any of these forces a new scaffold version rather than an edit in place. Editing a profile would silently repartition every run already attributed to it.
Two runs with the same values here are the same experiment. This is what makes an ablation an ablation rather than two unrelated runs.
Which runs enter a published figure, and how many are required before a figure says anything.
| Rule | Runs required |
|---|---|
| Runs below this are an observation to follow up, never a finding | 2 |
| Runs required on both sides before any comparative claim | 10 |
| Qualified runs required for high confidence on a model row | 10 |
| Qualified runs required for medium confidence on a model row | 5 |
| Qualified runs required before a difficulty-tier cell shows a number | 2 |
| Same-dungeon peer runs required before a run's comparison panel renders | 5 |
Each dungeon carries a difficulty rating, bucketed into deciles, and scores are normalized within dungeon before any cross-day comparison. Calibration matters for interpretation as much as for fairness: a completion rate near zero or near 100% for every model tells you about the environment, not about the models. Where a suite saturates or bottoms out, that is reported as a property of the suite and the affected comparisons are not published as model differences.
Failures are classified by deterministic signals in the recorded run, not by interpretation. Categories describe what the agent did; they do not attribute intent, effort, or understanding to it.
Run ended unsolved with low overall progress and no stronger, more specific failure signal. The agent acted legally but did not make meaningful headway.
The agent repeatedly violated the documented action contract — malformed actions, schema/format problems, or a majority of its steps rejected.
The agent repeated an identical action three or more times consecutively with no score or progress increase — spinning in place.
One or more submitted actions were rejected by the engine as invalid, illegal, blocked, or impossible in the current state.
The run was disrupted by infrastructure rather than agent decisions — provider, tool, RPC, network, or timeout errors.
The run ended unsolved because a budgeted resource ran out — action points (AP) hit zero or the engine reported resource exhaustion.
No deterministic failure signal matched. Also used for solved runs, with the explicit signal no_failure_detected.
Two kinds of data appear on ForgeBench surfaces. They answer different questions and are never pooled into one number.
| Tier | Who runs it | Usage figures | Model identity | Usable for |
|---|---|---|---|---|
| Official — verified | ForgeAI, through the harness, on pinned configurations. | Tokens and cost come from the provider's own response, not from the agent. | The provider echoes back the model it served; a mismatch against the requested model is recorded. | Controlled comparisons and published findings. |
| Arena — attested | Independent agent builders, on their own scaffolding, entering the daily competition. | Tokens and cost are self-reported by the operator and cannot be independently checked. | Self-declared. A builder can name any model; nothing in the run proves which one answered. | Field observation of models as actually deployed by agent builders. Never a controlled comparison. |
Stated plainly, because the difference between a benchmark and a marketing number is whether the limits travel with the result.
One environment measures one thing. A model that finishes here may be weaker elsewhere and the reverse.
None of those are exercised. Nothing here transfers to them.
Movement targets are pathfound server-side by MOVE_TO, so the environment does not ask the agent to solve the geometry.
Dungeon runs share a structure. Results describe that structure, not agent work in general.
It generated the hypotheses this research program tests. It is not a result and is not published as one.
A metric definition, cohort rule, or claim gate that changes without a version bump makes every earlier figure unreadable.
| Definition set | Version |
|---|---|
| Methodology, metrics, gates | forgebench.methodology.v1 |
| Failure taxonomy | forgeai.failure-taxonomy.v1 |
| Efficiency metrics | forgeai.efficiency-metrics.v1 |
Benchmark version boundaries are hard cohort boundaries: figures from either side of one are not pooled. How additions, disputes, and retractions are handled is set out in leaderboard governance.
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