OpenCode Go + Oh My OpenAgent: The Model Routing Config That Actually Saves Money

Most guides on OpenCode Go start with the models. I want to start with the thing most guides get wrong: the limits are denominated in dollars, not requests.

That sounds like a minor distinction. It isn't.

The thing everyone misses

OpenCode Go costs $5 for the first month, then $10/month. Your usage cap is $12 per 5-hour window, $30/week, $60/month.

When you spend $12 in a 5-hour window on DeepSeek V4 Flash, you get approximately 31,650 requests. When you spend the same $12 on GLM-5.1, you get around 880. Same budget. 36x difference in volume.

This is why routing actually matters. If you pick one model and use it for everything, you are either burning premium requests on tasks that don't need them, or you are under-using cheap models that are surprisingly capable. The right move is assigning models to tasks based on what each task actually requires.

MiniMax M2.5 has a hard cap of 100,000 requests per month regardless of cost. It activates only ~10B parameters and is priced at 16.7x cheaper than Claude Opus 4.6 on input tokens. For high-volume low-complexity work, it is the obvious choice, and most people don't know it exists.

What you lose running on a single premium model

Say you put everything through DeepSeek V4 Pro: 10,200 requests per 5-hour window. That sounds fine for light use. But Oh My OpenAgent runs multiple agents in parallel. Prometheus decomposes your task, Metis synthesizes context, Atlas manages sequencing, Sisyphus runs execution, and the Librarian reads docs. A single complex task can fan out into 30-50 requests without you doing anything. Your 5-hour budget evaporates in a few hours of active work.

The problem isn't the quality gap. V4 Pro at 80.6% is within 7 percentage points of Claude Opus 4.7 at 87.6%, and for most routine tickets that gap is invisible. The problem is you don't need that quality for every step of a multi-agent workflow.

The tier breakdown with actual numbers

Here is what the available models score on benchmarks that matter for coding tasks, plus the API pricing that drives the routing math:

(Requests per 5-hour window calculated at roughly 2,500 average tokens per request.)

Sisyphus gets Kimi K2.6 because it runs extended thinking at up to 32K tokens. You want the strongest reasoning model here, even at lower volume. Kimi's 256K context window handles long execution traces.

Librarian and Explore get V4 Flash. These agents read docs, fetch context, and do lookup work. They do not need frontier-level reasoning. Wasting V4 Pro on Librarian is the single most common budget mistake I see.

Oracle and Prometheus both get GLM-5.1. Planning and deep reasoning are where GLM-5.1 earns its slot. It is not the cheapest model, but it is not the most expensive either, and it performs well on the kinds of open-ended decomposition tasks these agents handle.

Hephaestus (the primary coding agent) gets V4 Pro as primary with V4 Flash as fallback. The gap between them is small enough that on simpler coding tasks, falling back to Flash costs you nothing visible.

MiMo-V2.5-Pro on Multimodal-Looker is deliberate. It scored 78.9% on SWE-Bench Verified and is specifically designed for agentic workflows.

The routing decision rule

Route through V4 Flash first for any task that will exceed 100 requests. Escalate to Kimi K2.6 or V4 Pro only if V4 Flash gets stuck.

This works because V4 Flash at 79.0% SWE-Bench Verified handles the majority of real-world coding tasks correctly. The one-point gap to V4 Pro is real but rarely shows up unless you are hitting genuinely hard tickets. When it does, the fallback chain handles it.

Do not escalate preemptively. Let the model fail first, then escalate. Preemptive escalation is how you burn through your window in an hour.

. It covers the earlier v4.0-v4.1 configuration in detail and is worth reading alongside this.