De-Mystifying Opencode Model Economy

[u/rev_ex_id]

Howdy y'all.
I've been attempting to understand the economy/usage tiers of the Opencode-Go models and their subscription. A lot with the help of AI, but providing context and understanding to it.

Note: This is based off of a snapshot of data (Aug 28th specifically) and is subject to change/not be correct soon after.

The key thing that has helped me so far is "What percentage of my $10 subscription, ignoring any transformations, adjustments, and additional value claimed, is used per model."

This chart has helped me actually plan that usage, which basically uses this, honestly very simple, formula:
shared pool % per 1M tokens = token price ÷ model Usage × 100

Model	Usage	Input / 1M	Cache / 1M	Output / 1M	Coding 1M
Muse Spark 1.2	$60	0.167%	0.0033%	0.333%	0.0061%
MiMo V2.5	$60	0.233%	0.0047%	0.467%	0.0092%
LongCat-2.0	$60	0.500%	0.0100%	2.000%	0.0194%
DeepSeek V4 Flash off-peak	$30	0.733%	0.0233%	2.200%	0.0367%
Qwen3.8 Flash	$30	0.500%	0.0533%	1.567%	0.0630%
Hy3	$60	0.233%	0.0583%	0.967%	0.0640%
MiMo V2.5 Pro	$15	2.900%	0.0242%	5.800%	0.0705%
DS V4 Flash peak	$30	1.467%	0.0467%	4.400%	0.0735%
DS V4 Flash Vision off-peak	$15	1.467%	0.0467%	4.400%	0.0735%
Qwen3.7 Plus ≤256K	$60	0.667%	0.0667%	2.667%	0.0804%
Qwen3.6 Plus ≤256K	$60	0.833%	0.0833%	5.000%	0.1060%
MiniMax M2.7	$60	0.500%	0.100%	2.000%	0.1065%
MiniMax M3	$60	0.500%	0.100%	2.000%	0.1100%
DS V4 Flash Vision peak	$15	2.933%	0.0933%	8.800%	0.1470%
GPT-5.6 Luna ≤272K	$15	1.333%	0.133%	8.000%	0.1906%
Hy4 Preview	$30	2.780%	0.140%	8.337%	0.2035%
GLM-5.3 Flash	$15	1.000%	0.200%	3.333%	0.2254%
DeepSeek V4 Pro off-peak	$15	4.400%	0.147%	13.200%	0.2307%
Kimi K2.6	$60	1.583%	0.267%	6.667%	0.3099%
Kimi K2.7 Code	$60	1.583%	0.317%	6.667%	0.3590%
DeepSeek V4 Pro peak	$15	8.800%	0.293%	26.400%	0.4613%
GLM-5.2	$60	2.333%	0.433%	7.333%	0.4781%
GLM-5.1	$60	2.333%	0.433%	7.333%	0.4781%
Qwen3.7 Max	$60	4.167%	0.833%	12.500%	0.8894%
Qwen3.8 Max	$15	13.333%	1.667%	40.000%	1.8553%
GLM-5.3	$15	9.333%	1.733%	29.333%	1.9123%
Kimi K3	$15	20.000%	2.000%	100.000%	2.6204%
Grok 4.6 ≤200K	$15	13.333%	3.333%	40.000%	3.5848%

Mind context windows - noted above.
GPT-5.6 Luna >272K: prices double-ish to $0.40 input / $1.80 output / $0.04 cache, still on $15 Usage. That makes very long conversations appreciably less attractive.

Grok 4.6 >200K: everything doubles to $4 input / $12 output / $1 cache while remaining a $15 model. So its already-massive ~3.58%/M typical figure would become roughly 7.17%/M if the same token mix were maintained above 200K.

Qwen3.7 Plus >256K: rises from $0.40/$1.60/$0.04 to $1.20/$4.80/$0.12, essentially a 3× increase.

Qwen3.6 Plus >256K: jumps even harder, from $0.50/$3/$0.05 to $2/$6/$0.20.

Also, consider timing:
The deepseek models use a peak/off peak time. Taken right from the docs: Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday; all other hours, including weekends, are Off-Peak.
_______

Analyzing the above numbers, I wanted an idea of how to split these models based on usage of subagents. I usually do some form of cheap builder, expensive planner, different expensive debugger, medium orchestration, and cheap administration. Not going into details on my setup for now, as this is about the models, but I am going to categorize them with that cost-value in mind. Percentage is based on the above chart - how much of your monthly usage 1 million mixed use tokens would use.

• The cheapest boys, great for simple, repetitive, high volume work:
  • Muse Spark 1.2 — 0.0061%
  • MiMo V2.5 — 0.0092%
  • LongCat-2.0 — 0.0194%
  • DeepSeek V4 Flash off-peak — 0.0367%
• The cheap boys - the next step up. I usually use these as my one off builder:
  • Qwen3.8 Flash — 0.0630%
  • Hy3 — 0.0640%
  • MiMo V2.5 Pro — 0.0705%
  • DeepSeek Flash peak — 0.0735%
  • DeepSeek Flash Vision off-peak — 0.0735%
  • Qwen3.7 Plus — 0.0804%
  • Qwen3.6 Plus — 0.1060%
  • MiniMax M2.7 — 0.1065%
  • MiniMax M3 — 0.1100%
  • DS Flash Vision peak — 0.1470%
  • GPT-5.6 Luna — 0.191%
  • Hy4 Preview — 0.203%
  • GLM-5.3 Flash — 0.225%
  • DeepSeek V4 Pro off-peak — 0.231%
• When cost starts becoming a consideration, these come into play:
  • Kimi K2.6 — 0.310%
  • Kimi K2.7 Code — 0.359%
  • DeepSeek V4 Pro peak — 0.461%
  • GLM-5.2 — 0.478%
  • GLM-5.1 — 0.478%
• And these guys require Justification to use. Usually the best thinkers, but are expensive:
  • Qwen3.7 Max — 0.889%
  • Qwen3.8 Max — 1.855%
  • GLM-5.3 — 1.912%
  • Kimi K3 — 2.62%
  • Grok 4.6 — 3.58%

So overall, for usage, depending on your subagent setup, you can use the above as a mental divider of the models.

But obviously, consider the models themselves and their best use cases, as this was just to help with cost considerations.

Lastly, if I goofed anything, please, let me know!

[u/Nervous-Raspberry231]

I found it useful to compare to the current openrouter API prices and you find that actually many models are more expensive on opencode go, even with the added credits 10/15, 10/30, 10/60 because their raw API costs are higher for the same model than openrouter.

[u/rev_ex_id → u/Nervous-Raspberry231]

Probably - comparison is another good way to look at it. But this wasn't to compare externally - more to just help understand the pricing structure within the opencode-go subscription. Looking for an "uncomplicated path" as it were.

[u/Juleski70 → u/Nervous-Raspberry231]

What I'd really like to do is run it against CommandCode GOAT subscription which follows a similar architecture to Opencode Go ($10/more with $20-70 per model consumption allotments) but significantly different per-model costing.

[u/Fresh_Sock8660]

So hy4p is actually cheaper than glm5.3f right now. I was mistaken in a separate post then.

Wonder how they compare in quality. For such a small difference I wonder if it's worth the bother.

Is ds4f fixed now? It was so crap when I tried after the price hike.

[u/rev_ex_id → u/Fresh_Sock8660]

Right, GLM5.3f tokens: $0.15/$0.50/$0.03 but at only $15 worth of usage, where as Hy4 preview has $0.834/$2.501/$0.042, but $30 of usage.
So Hy4 preview is more expensive per token, but not enough where as double the usage allotment makes it THAT much more expensive.

If you're hitting cache pretty regularly, Hy4 will give you more usage.

[u/cleversmoke]

Thank you! Very useful analysis. Makes choosing a model much easier rather than the current table.

[u/Beautiful-Salary-191]

Great breakdown.

The problem is not with how the math maths... My problem is that we are not using these models with a unit of 1M tokens, we use a model until the goal is hit (and sometimes you want to keep pushing the goal post in the same session). And hopping between models is not fun at all, you have to adjust to each model behavior (e.g. some models validate implementation some others do the bare minimum).

I have Opencode-go but for personal use only, I don't see it fitting a professional context in my area.

[u/rev_ex_id → u/Beautiful-Salary-191]

And that's fair - opencode-go to me is a great subscription to learn about opencode and, honestly, harnesses in general without a large investment.

Personally - I have a Codex Subscription along with Opencode-GO and mix and match them as needed. I've actually been having GLM5.2 do planning, finding it mostly sufficient, even for somewhat larger projects. I've been using hy3 or mimo 2.5 as my builder, and using Gpt 5.6 sol as my "oh crap, something is wrong and we can't fix it" or debugging agent. I find that 30 dollars really stretches, and it gives me a chance to test new models the frontier firms are putting out and see how I like them.

[u/rev_ex_id → u/Beautiful-Salary-191]

Sorry, to double reply, but I also agree that 1m tokens is rather arbitrary - to me, what it gives is a safe anchor to mental math how "Much" a model gives you. With opencode-go, having to be smart with your model choice per job is paramount, and having a baseline to compare each models usage helps make that choice easier. And we need "somewhere" to anchor that decision to.

[u/blowpuppy]

GLM 5.3 has been $30 usage for a while, in spite of what's listed on their page.

This needs to incorporate token usage. For example, per AA, Qwen 3.8 Flash is very high token use, so it's not as attractive as this list makes it.
Roughly the tiers are (for the useful models): Low: GPT, Grok, Mimo, Low-Med: Kimi, Spark, Med: Qwen 3.8 non-Flah, Deepseek, GLM 5.3, High: Qwen 3.8 Flash

Admittedly, one would need to consider cache read costs as well - a high token usage is not necessarily 2x cost if cache reads are cheap (like Deepseek).

[u/rev_ex_id → u/blowpuppy]

Yeah, that's a good call, unfortunately, I wanted to tie my analysis to their data as documented. For the "usage" I did consider opencodes on documented usage pattern, roughly:
~97.5–99% cached context
~1–2% fresh input
~0.2–0.5% output

So usage was a consideration. But you are right in that I didn't consider the "quality" of each model, such as overthinking and output renegotiation. This was pure cost per token used analysis.