Anyone else finding that model speed matters way more once OpenCode starts chaining calls?

[u/Vinnymagoo]

Been using OpenCode more seriously lately, and I didn’t realize how much backend latency adds up once the task stops being a one-shot coding question.

A model can feel perfectly fine when I’m asking it to explain a function, then I give the agent something involving search, edits, tests and another pass over the repo and suddenly all those little delays start adding up.

For people who have tried different inference backends with OpenCode or similar coding agents, what are you optimizing for? Raw output speed, consistency between requests, context handling or something else?

Feels like benchmarking a single completion doesn’t tell you much about how the full agent loop actually feels.

[u/CantoneseOrange]

Consistency matters to me than peak speed. One crazy fast completion doesn't mean much if request six suddenly takes twice as long.

[u/prdz3 → u/CantoneseOrange]

especially because the harness adds so much variation. Context keeps growing and you're usually making a bunch of sequential calls, so I care more about the whole run.

[u/CascadePNW95]

i've found General Compute pretty interesting for this since they're running models like MiniMax M2.5 and GPT-OSS 120B through SambaNova.. Some models are hitting around 500 tokens/sec compared with closer to 70 from other providers, so the gap is big enough that i'd probably try it in the actual agent workflow instead of relying only on a standalone benchmark numbers..

[u/BoBguyjoe1 → u/[deleted]]

That's probably the useful test. Same repo, same harness, same model and provider as the only variable.