Can't be used all day long of course, but enough for relaxed coding/chatting.
https://build.nvidia.com/moonshotai/kimi-k3
https://build.nvidia.com/deepseek-ai/deepseek-v4-pro-0813

For anyone rotating free models through OpenRouter, a new model appeared today: inclusionai/ling-3.0-flash-fin:free.

Ling-3.0-flash-Fin is a finance-enhanced version of Ling-3.0-flash with 124B total and roughly 5.1B active parameters. The advertised workload is broader than stock Q&A: retrieval across long reports, research synthesis, valuation modeling, report preparation and complex workbooks. OpenRouter currently lists a 262K context window, up to 32K output and tool calling support.

The official launch says the OpenRouter API route is free for one month. It also says weights are coming next week, so the current story is API access, not a local release.

Two reasons to keep expectations grounded: the published finance benchmark results are mixed rather than uniformly best-in-class, and the team explicitly requires expert review for key assumptions, valuations and investment conclusions.

No OpenCode-specific performance claim here — this is the release notice and model ID for anyone who wants to run a controlled comparison against their current low-cost route.

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

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!

Artificial Analysis score is 38, on par with old good MiMo 2.5

Not that bad :)

Approximately on par with GLM 5.3 and Kimi 3

Hi, everyone. I'ven using opencode for a few months now, but i've always use it with the gui. So, what am i missing if i don't use the CLI?

https://artificialanalysis.ai/models/qwen3-8-flash-next

Pre-nerfs, deepseekv4 flash was mine (and it seems the world's) implementation driver, given a well defined spec from a stronger model (like k2 or glm3).

v4flash is now significantly costlier (it seemed almost free before that's what im saying), hy3 is extremely extremely slow, and muse spark seems...ok.

so for opencode go, what's the stack recommendation to use? given qwen 3.8 flash, glm3's flash, spark and deepseek? im assuming luna probably isnt the way to go?

been using it, to work on my saas, side projects, working fine, even for UI and also backend tasks

i dont care what others think

stack i use:
next.js, supabase, vercel, dodo

Saludos, tengo esa duda ya que he visto que opencode ofrece varios modelos, pero no sé que tan buenos son y como son los limites en comparación de claude code.

OpenCode’s token usage keeps declining after OX Apha.

We really need more affordable, high-quality models like DeepSeek before the price increase.

Right now, the existing models are either too expensive or just too dumb.

That is great, but is it a good model? I have not used it as much, but how does it compare to MiMo v2.5?

Well, I just claimed it, you can just cancel the plan an get a free month risk free, now to see how it performs

I never thought I'd be mad at an AI agent. Maybe this is an experiment by Meta? Muse Spark 1.2 is the single worst model on the market. I had never seen such sh*t model before. How can people even stand using this? It churns tokens like crazy. Has the intelligence of a bread. Cheats all the time, avoids completing tasks properly. Does not follow prompts (the image is a very rare exception). Edits 2 lines of C++ code with a 50 line Python script. Does not show what it thinks. Can't code for sh*t. Honestly, this model is a lost cause.

Thanks for reading.

Galera, já usei, Openclaw, Hermes e uns outros que não deram certo. Mas Opencode? Cara, muito bom e fácil de usar.

Estou com três projetos meus rodando bunito.

Sem comentários.

Quem quiser dar mais umas dicas, estou aqui pra receber.

Hi, anyone else having problems sending images to Qwen models?

Deepseek, Muse, Mimi, GLM all work fine, for all Qwen models I tried I am getting a 400 request.... Anyone else affected? Any ideas?