Section 1
What an agent needs from a model
Whatever framework drives the loop, the model must keep the plan coherent, pick the right tool at the right time, and recover gracefully when a step fails. Context length is paramount here; agent loops generate heavy log output, and if a model truncates early or loses attention, the whole workflow collapses.
- Context length: can the model retain instructions and tool outputs across dozens of turns?
- Tool usage: does it strictly follow formatting to execute the right operation instead of guessing?
- Planning & Stability: does it keep context and avoid drifting off task?
Section 2
Recommended selection strategy
Start with a model that has strong quality scores, then validate that it still runs well enough on your hardware to keep the loop responsive. In an agent loop a slightly slower but more dependable model often wins, because fewer bad tool calls means fewer wasted iterations.
- Choose the strongest agent-capable model that fits your VRAM budget.
- Favor consistent quality over benchmark spikes that do not repeat.
- Test with the benchmark data and then with your own agent workflow.
Section 3
What to avoid
Do not pick purely on speed if the model regularly misses steps or produces weak plans. In an agent loop, bad reasoning costs more than a few seconds of latency, because every mistake compounds across the workflow.
- Avoid models that fit only by pushing VRAM to the limit.
- Avoid low-quality runs that rely on lucky one-off outputs.
- Avoid choosing a model before checking its benchmark evidence.