For those trying to get it to work in OpenCode with a Qwen Cloud Token Plan, this is what worked for me. Note that I've just matched Qwen 3.7 Max for the limits as I don't know exactly what they are.

  "provider": {
    "alibaba-token-plan": {
      "models": {
        "qwen3.8-max-preview": {
          "limit": {
            "context": 1048576,
            "output": 65536
          },
          "modalities": {
            "input": [
              "text"
            ],
            "output": [
              "text"
            ]
          },
          "name": "Qwen3.8 Max Preview"
        }
      }
    }
  }

also, be very careful which API endpoint and API Token you use. make sure you use right one (obseve your quota is used up. if you hit right endpoint quota used almost immediately). so that you do not accidentally burn API endpoint tokens (they are expensive, can easily hit 200 USD / 3 days which do not count towards your membership "Credits", if you say purchased it with 200 USD signup bonus in Alibaba Cloud)

For the vast majority of models I've worked with, 150k tokens of context seems like some magic spot. If you can stay under it then the session will stay on track and the agent won't start getting forgetful and weird.

Just like with real work, it's valuable to break down big tasks into small tasks that you can knock over in a single session. When a session does start getting too big, you just need to ask the agent to reply with a comprehensive handover report and paste it into a new session.

For my quirky workflows I need 100K even to prep Opus 4.8 or higher to begin good work. Not programming, but a complex synthetic architecture for implementing more attentional autonomy for machines.

Nirav from Framework is similar, he speaks openly about compromises they make with their designs and why they make them.

When leaders are both technical and open about these sorts of things it makes me feel like I can trust that they are invested in supporting and improving their products.

I'd say Codex and Claude Code have different strengths and weaknesses. Claude Code is significantly better in terms of their subagent UI for example - being able to see the list of subagents under the input is great.

To be honest though, I've gotten to the point where I prefer the OpenCode UI. A big win for OpenAI is you can log in to your subscription in OpenCode, whereas this is not trivially achievable for a Claude subscription.

I was getting some really impressive cost efficiency today in OpenCode with the following:

  * Main session agent: gpt-5.6-sol (high) via OpenAI subscription
  * General purpose subagent: deepseek-v4-pro (high) via OpenCode Go subscription
  * Using `obra/superpowers` for subagent driven workflows
  * The main session only being allowed filesystem read permissions and everything else delegated

It was absolutely crunching through tasks without hitting the limit, and this combination is quite cost effective.

GPT 5.6 was picking up on quality and functional issues from DeepSeek and having it resolve them cleanly, and I didn't even get close to my quotas whereas I can usually blast through them. I feel as people get more comfortable with subagents and mixing and matching models in their daily work, Anthropic's walled garden stance will start to hurt them.

I think it would be the case in many of the commonwealth countries. You hear "sorry" being used a lot like this in Australia too.

I feel this would be more useful for tasks like "Check website X to see if there are any great deals today". Specifically, tasks that are loosely defined and require some form of intuition.

And even in that case if you give the agent a tool to access website X efficiently it'll work a lot better. Like, say, a CLI tool that can take a website product URL for that specific site and return the opengraph data of the item price on it + any sale price.

The different bindings vs Vim was actually what stopped me using it. I really really wanted to love it and love a lot of the motivation and principles behind it, but unlearning decades of muscle memory is an absolute nightmare.

Many people buy two separate Claude pro subscriptions and that makes the limit become a non-issue. It works surprisingly well when you tend to hit the 5 hourly limit after a few hours, and hit the weekly limit after 4-5 days. $40 vs $100 is significant for a lot of people.

I hit limit of Pro in about 30 minutes, 1 hour max. And only when I use a single session, and when I don't use it extensively, ie waits for my responses, and I read and really understand what it wants, what it does. That's still just 1-2 hours/5 hours.

What do you do to avoid that?

You're probably having long sessions, i.e. repeated back-and-forth in one conversation. Also check if you pollute context with unneeded info. It can be a problem with large and/or not well structured codebases.

The last time I used pro, it was a brand new Python rest service with about 2000 lines generated, which was solely generated during the session. So how I say to Claude that use less context, when there was 0 at the beginning, just my prompt?

So you had generated 2000 lines in 30 minutes and ran out of tokens? What was your prompt?

I’d use a fast model to create a minimal scaffold like gemini fast.

I’d create strict specs using a separate codex or claude subscription to have a generous remaining coding window and would start implementation + some high level tests feature by feature. Running out in 60 minutes is harder if you validate work. Running out in two hours for me is also hard as I keep breaks. With two subs you should be fine for a solid workday of well designed and reviewed system. If you use coderabbit or a separate review tool and feed back the reviews it is again something which doesn’t burn tokens so fast unless fully autonomous.

Thanks for the tip, didn’t think of using 2 subscriptions at the same company.

When reaching a limits, I switch to GLM 4.7 as part of a subscription GLM Coding Lite offered end 2025 $28/year. Also use it for compaction and the like to save tokens.

Prompting isn't programming. Prompting is managing.

Is it?

If I know the system I'm designing and I'm steering, isn't it the same?

We're not punching cards anymore, yet we're still telling the machines what to do.

Regardless, the only thing that matters is to create value.

I think about those conversations a lot. I’m in the high power rocketry hobby and wrote my own flight controller using micropython and parts from Adafruit. Worked just fine and did everything I wanted it to do yet the others in the hobby just couldn’t stand that it wasn’t in C. They were genuinely impressed then I said micropython and all of a sudden it was trash. People just have these weird obsessions that blind them to anything different.

It almost always devolves into some all encompassing ERP that is meant to solve the needs of all parts of the business and save millions in licensing costs, and we all know how well that plan goes.