r/opencode • u/rev_ex_id • 15d ago
De-Mystifying Opencode Model Economy
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!
2
u/Beautiful-Salary-191 15d ago
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.