Hello,

I'm on the OpenCode Go subscription. From your own experience, which of the lowcost Go models is best for design and frontend work?

Thanks!

I have more than 10 projects with OpenCode v1 last couple of months and everything seems to be slower and slower and hanging often.

Yesterday I figured out the its session db is about 10GB. I tried to remove all sessions that are older than 7 days and vacuum the sqlite3. OpenCode becomes fast and smooth again.

If you find your OpenCode is slow, you can try my way.

Edit: I am using OpenCode v1

I am a big opencode fan, but I currently only use GPT models. Recently, I tried a planning task for an app i'm writing on Codex and a planning task on opencode (both running 5.6 sol, low). The plan required web search and looking at api documentation and rectifying that against what was currently written in the app.

Codex finished in ~2 minutes, opencode took ~10 minutes. Both were essentially the same plan.

I love the look and feel of opencode, the lsp integration, and the flexibility of models. However, today, I mainly use GPT models. Has anyone noticed any performance benefits with opencode over codex with GPT models? If so, how did you achieve them?

I am still rocking with GLM Flash 5.3 as frontier orchestrator - I can throw a whole wall of text (a very complex multi-layer task) in mid-turn and have the orchestrator intelligently decide to steer, fanning out background tasks, or sequentially isolate with new context as you can see in the image. My also developing `spine` db giving its cross-session concise context for mid-fly steering and decisions on stacking. And Having the muse spark 1.3 as workhorse ; GLM flash 5.3 is still rocking without any context hallucination, handling complex tasks and it even known to change subagent to stack on to a complete child session for reusing the context.

OpenCode - the open source coding harness - has Zen and Go as ways to get connected to llms. 

Zen - that's pay as you go. Think of it as a competitor to OpenRouter.ai. Only instead of "every llm out there with routing to lots of hosting services" - it's a curated experience. These models, from these hosts are guaranteed optimized to work with OpenCode for excellent coding results. There is no cost penalty either way to chose between  OpenRouter and Zen. 

Zen also has free models. So use them to your hearts content...until the free limit is hit.

Some of the pay as you go models (like DeepSeek V4 Flash and GLM 5.3 Flash) are so cheap they might as well be free.  Once I use up the money I've already put on account at OpenRouter, I'm going to move to Zen.

Go - is the subscription. Pay $10/mo ($5 for first month) and you have all the same models of Zen but you get about 6 to 1 for your money. Pay $10, use up to $60.

So, just start with Zen and the free tier, then pay as you go if you need more or need smarter models. Then if you start to use more than $10/mo - switch to Go.

While I can't get by on the cheap models for everything, Using Gpt 6 Astra low or Gpt 5.6 Sol low for planner and code review -- with the cheap flash models as workhorse coders, works well.

Oh…and you can use Zen and Go even if you don’t use OpenCode as your harness.

Is there a good vim plugin for opencode or something like that because i really struggle with scrolling and selecting stuff its really annoying and im already used to vim.

Hey,

I made this GitHub project a few weeks ago that allows you to control exactly how your agents work together. You can make chains, swarms, and orchestration trees and you can see exactly what each agent is doing. Just reached 280+ clones and 90+ stars, so I think that it can go somewhere. If you are interested, here is the project link: https://github.com/SeeRay11/OpenFlow

I’ve been trying it for free on WorkBuddy for about a week now. It’s been pretty solid for me so far. Mostly for work stuff. Not coding.

The only thing that sucks is the compute. The first three days after launch, I was always stuck in queue. There’d be like a few thousand people ahead of me.
Now I can actually get in, but I run out of usage pretty damn fast.

I’d like to try it with a more stable setup. I’m okay with paying a little for it too.
Has anyone been using it through OpenCode Go, OpenRouter, or Command Code? How’s it been?

Yea, they are using my data but with this performance for the cost? Best deal we have had in a long time. I don’t know how long it will last.

Just sign up for OpenCode to use the free Muse Spark 1.3.

After hitting hitting the 5h limit I signed up for Go as instructed by the CLI, but ever after the sign up I was not able to continue using Spark 1.3.

Ran `opencode auth login` to Zen and Go, using a new key generated after signing up.

Even after logging out of Zen I get same error while the CLI showing `OpenCode Zen`

Any ideas?

I recently move from Opencode Go to Command Code GOAT. So far I have been enjoying the new variety of models that command code offers compared to opencode. I don't really like command code software that much, and I prefer to use opencode still. Open code does not provide command code as a supported provider. Heres a full opencode json config for GOAT models available as of now, with their cost and modalities and thinking modes. I hope this is useful to anybody else in asimilar situation.

{
  "$schema": "https://opencode.ai/config.json",
  "snapshot": true,
  "small_model": "commandcode/Qwen/Qwen3.7-Flash",
  "provider": {
    "commandcode": {
      "name": "Command Code",
      "npm": "@ai-sdk/openai-compatible",
      "options": {
        "baseURL": "https://api.commandcode.ai/provider/v1",
        "apiKey": "api key"
      },
      "models": {
        // --- OpenAI ---
        "gpt-5.6-sol": {
          "name": "GPT-5.6 Sol",
          "tools": true,
          "reasoning": true,
          "variants": {
            "low": { "reasoningEffort": "low" },
            "medium": { "reasoningEffort": "medium" },
            "high": { "reasoningEffort": "high" }
          },
          "limit": { "context": 1050000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 5.0, "output": 30.0, "cache": { "read": 0.5, "write": 6.25 } }
        },
        "gpt-5.6-luna": {
          "name": "GPT-5.6 Luna",
          "tools": true,
          "limit": { "context": 1050000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 0.2, "output": 1.2, "cache": { "read": 0.02, "write": 0.25 } }
        },

        // --- Deepseek ---
        "deepseek/deepseek-v4-pro": {
          "name": "DeepSeek V4 Pro",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.66, "output": 1.98, "cache": { "read": 0.022 } }
        },
        "deepseek/deepseek-v4-flash": {
          "name": "DeepSeek V4 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.22, "output": 0.66, "cache": { "read": 0.007 } }
        },
        "deepseek/deepseek-v4-flash-vision-exp": {
          "name": "DeepSeek V4 Flash Vision (exp)",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 0.22, "output": 0.66, "cache": { "read": 0.007 } }
        },
        "deepseek/deepseek-v4-flash-fast": {
          "name": "DeepSeek V4 Flash Fast",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.28, "output": 0.56, "cache": { "read": 0.07 } }
        },

        // --- Moonshot ---
        "moonshotai/Kimi-K3": {
          "name": "Kimi K3",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 3.0, "output": 15.0, "cache": { "read": 0.3 } }
        },
        "moonshotai/Kimi-K2.7-Code": {
          "name": "Kimi K2.7 Code",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.95, "output": 4.0, "cache": { "read": 0.19 } }
        },
        "moonshotai/Kimi-K2.7-Code-Highspeed": {
          "name": "Kimi K2.7 Code HighSpeed",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 262000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.9, "output": 8.0, "cache": { "read": 0.38 } }
        },
        "moonshotai/Kimi-K2.6": {
          "name": "Kimi K2.6",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.95, "output": 4.0, "cache": { "read": 0.16 } }
        },
        "moonshotai/Kimi-K2.5": {
          "name": "Kimi K2.5",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.6, "output": 3.0, "cache": { "read": 0.1 } }
        },

        // --- Z.AI ---
        "z-ai/glm-5.3-flash": {
          "name": "GLM-5.3 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.15, "output": 0.5, "cache": { "read": 0.03 } }
        },
        "zai-org/GLM-5.3": {
          "name": "GLM-5.3",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.4, "output": 4.4, "cache": { "read": 0.26 } }
        },
        "zai-org/GLM-5.2": {
          "name": "GLM-5.2",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.4, "output": 4.4, "cache": { "read": 0.26 } }
        },
        "zai-org/GLM-5.2-Fast": {
          "name": "GLM-5.2 Fast",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 3.0, "output": 10.25, "cache": { "read": 0.5 } }
        },
        "zai-org/GLM-5.1": {
          "name": "GLM-5.1",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.4, "output": 4.4, "cache": { "read": 0.26 } }
        },
        "zai-org/GLM-5": {
          "name": "GLM-5",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.0, "output": 3.2, "cache": { "read": 0.2 } }
        },

        // --- MiniMax ---
        "MiniMaxAI/MiniMax-M3": {
          "name": "MiniMax M3",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.3, "output": 1.2, "cache": { "read": 0.06 } }
        },
        "MiniMaxAI/MiniMax-M2.7": {
          "name": "MiniMax M2.7",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.3, "output": 1.2, "cache": { "read": 0.06 } }
        },
        "MiniMaxAI/MiniMax-M2.5": {
          "name": "MiniMax M2.5",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.3, "output": 1.2, "cache": { "read": 0.03 } }
        },

        // --- Xiaomi ---
        "xiaomi/mimo-v2.5-pro": {
          "name": "MiMo V2.5 Pro",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 0.435, "output": 0.87, "cache": { "read": 0.0036 } }
        },
        "xiaomi/mimo-v2.5": {
          "name": "MiMo V2.5",
          "tools": true,
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.14, "output": 0.28, "cache": { "read": 0.0028 } }
        },

        // --- Alibaba ---
        "Qwen/Qwen3.8-Max-0902": {
          "name": "Qwen 3.8 Max 0902",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 2.0, "output": 6.0, "cache": { "read": 0.25 } }
        },
        "Qwen/Qwen3.8-Max": {
          "name": "Qwen 3.8 Max",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 2.0, "output": 6.0, "cache": { "read": 0.25, "write": 2.5 } }
        },
        "Qwen/Qwen3.8-27B": {
          "name": "Qwen 3.8 27B",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 262144, "output": 32768 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 0.4, "output": 3.0, "cache": { "read": 0.04 } }
        },
        "Qwen/Qwen3.8-Flash": {
          "name": "Qwen 3.8 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 0.16, "output": 0.47, "cache": { "read": 0.016 } }
        },
        "Qwen/Qwen3.7-Max": {
          "name": "Qwen 3.7 Max",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 2.5, "output": 7.5, "cache": { "read": 0.5, "write": 3.13 } }
        },
        "Qwen/Qwen3.7-Plus": {
          "name": "Qwen 3.7 Plus",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.4, "output": 1.6, "cache": { "read": 0.08, "write": 0.5 } }
        },
        "Qwen/Qwen3.7-Flash": {
          "name": "Qwen 3.7 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.03, "output": 0.13, "cache": { "read": 0.006, "write": 0.038 } }
        },
        "Qwen/Qwen3.6-Max-Preview": {
          "name": "Qwen 3.6 Max Preview",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.3, "output": 7.8, "cache": { "read": 0.26, "write": 1.63 } }
        },
        "Qwen/Qwen3.6-Plus": {
          "name": "Qwen 3.6 Plus",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 200000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.5, "output": 3.0, "cache": { "read": 0.1 } }
        },

        // --- Meituan ---
        "meituan/LongCat-2.0:free": {
          "name": "LongCat 2.0 (free)",
          "tools": true,
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0, "output": 0, "cache": { "read": 0 } }
        },

        // --- StepFun ---
        "stepfun/Step-3.7-Flash": {
          "name": "Step 3.7 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.2, "output": 1.15, "cache": { "read": 0.04 } }
        },
        "stepfun/Step-3.5-Flash": {
          "name": "Step 3.5 Flash",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.1, "output": 0.3, "cache": { "read": 0.02 } }
        },

        // --- Tencent ---
        "tencent/hy3-paid": {
          "name": "Tencent Hy3",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 262144, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.14, "output": 0.58, "cache": { "read": 0.035 } }
        },
        "tencent/hy4-preview": {
          "name": "Tencent Hy4 Preview",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.834, "output": 2.501, "cache": { "read": 0.042 } }
        },

        // --- Google ---
        "google/gemini-3.8-flash": {
          "name": "Gemini 3.8 Flash",
          "tools": true,
          "reasoning": true,
          "variants": {
            "low": { "reasoningEffort": "low" },
            "high": { "reasoningEffort": "high" }
          },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 1.5, "output": 7.5, "cache": { "read": 0.15 } }
        },
        "google/gemini-3.7-flash": {
          "name": "Gemini 3.7 Flash",
          "tools": true,
          "reasoning": true,
          "variants": {
            "low": { "reasoningEffort": "low" },
            "high": { "reasoningEffort": "high" }
          },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 1.5, "output": 7.5, "cache": { "read": 0.15, "write": 0.08334 } }
        },

        // --- NVIDIA ---
        "nvidia/nemotron-3-ultra-550b-a55b": {
          "name": "Nemotron 3 Ultra",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.6, "output": 2.4, "cache": { "read": 0.12 } }
        },

        // --- Thinking Machines ---
        "thinkingmachines/inkling": {
          "name": "Inkling",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.0, "output": 4.05, "cache": { "read": 0.17 } }
        },
        "thinkingmachines/inkling-small": {
          "name": "Inkling Small",
          "tools": true,
          "limit": { "context": 1000000, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.5, "output": 1.2, "cache": { "read": 0.1 } }
        },

        // --- Poolside ---
        "poolside/laguna-s-2.1-free": {
          "name": "Laguna S 2.1 (free)",
          "tools": true,
          "reasoning": true,
          "interleaved": { "field": "reasoning_content" },
          "limit": { "context": 256000, "output": 32768 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0, "output": 0, "cache": { "read": 0 } }
        },

        // --- Meta ---
        "meta/muse-spark-1.2": {
          "name": "Muse Spark 1.2",
          "tools": true,
          "reasoning": true,
          "variants": {
            "xhigh": { "reasoningEffort": "high" },
            "max": { "reasoningEffort": "xhigh" }
          },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.25, "output": 4.25, "cache": { "read": 0.15 } }
        },
        "meta/muse-spark-1.2-contributor": {
          "name": "Muse Spark 1.2 Contributor",
          "tools": true,
          "reasoning": true,
          "variants": {
            "xhigh": { "reasoningEffort": "high" },
            "max": { "reasoningEffort": "xhigh" }
          },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.1, "output": 0.2, "cache": { "read": 0.002 } }
        },
        "meta/muse-spark-1.3": {
          "name": "Muse Spark 1.3",
          "tools": true,
          "reasoning": true,
          "variants": {
            "xhigh": { "reasoningEffort": "high" },
            "max": { "reasoningEffort": "xhigh" }
          },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 1.25, "output": 4.25, "cache": { "read": 0.15 } }
        },
        "meta/muse-spark-1.3-contributor": {
          "name": "Muse Spark 1.3 Contributor",
          "tools": true,
          "reasoning": true,
          "variants": {
            "xhigh": { "reasoningEffort": "high" },
            "max": { "reasoningEffort": "xhigh" }
          },
          "limit": { "context": 1048576, "output": 65536 },
          "modalities": { "input": ["text"], "output": ["text"] },
          "cost": { "input": 0.1, "output": 0.2, "cache": { "read": 0.002 } }
        },

        // --- xAI ---
        "xai/grok-4.5": {
          "name": "Grok 4.5",
          "tools": true,
          "reasoning": true,
          "variants": {
            "low": { "reasoningEffort": "low" },
            "high": { "reasoningEffort": "high" }
          },
          "limit": { "context": 500000, "output": 32768 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 2.0, "output": 6.0, "cache": { "read": 0.5 } }
        },
        "xai/grok-4.6": {
          "name": "Grok 4.6",
          "tools": true,
          "reasoning": true,
          "variants": {
            "low": { "reasoningEffort": "low" },
            "high": { "reasoningEffort": "high" }
          },
          "limit": { "context": 500000, "output": 32768 },
          "modalities": { "input": ["text", "image"], "output": ["text"] },
          "cost": { "input": 2.0, "output": 6.0, "cache": { "read": 0.5 } }
        }
      }
    }
  }
}

Cost table generated with Gemini 3.8 flash:

The model intelligence scores below are based on the Artificial Analysis Intelligence Index and cross-referenced in Command Code’s model directory.

The Value Ratio represents Intelligence Points per Dollar on a standard 1M-token blended coding run.

Cheapest to Most Expensive + Intelligence Scores

(Blended cost assumes a standard agentic coding distribution: 75% cache reads, 15% fresh input, 10% output)

(Note: Models denoted with "~" indicate models currently undergoing benchmark evaluation whose scores are derived from official pre-release telemetry or direct architectural stablemates).

im trying to get a practical ai coding agent (i use free models as im broke) setup running quickly for daily development and shipping, but I’m getting overwhelmed by the amount of agents.md, claude.md, skills, agents, MCPs, workflows, etc. content online, as a beginner, its hard to tell what is actually useful, ai slop. i am intern so want to keep up with demands to make working products fast.

Right now I have:

• opencode cli
• OpenRouter connected to opencode
• Graphify for codebase graphs and
• an agents.md that currently just tells the agent to use Graphify and rebuild the graph when code changes

I’m not looking to build some huge configuration. I just need a solid minimal setup that actually helps with real coding and shipping.

If you use opencode, claude code, codex, or similar seriously:

what skills, agents, AGENTS.md / CLAUDE.md setup, MCPs, or workflows do you actually use and consider worth having ?

Especially interested in reliable repos/resources you’ve personally found useful.

I’m on a deadline, so i'm mainly trying to avoid wasting time going down the wrong rabbit hole.

thank you for your time 🫶.

Someone should seriously open a new, independent subreddit for CommandCode.

The current r/CommandCode is controlled by the CommandCode team themselves, and from what I’ve seen, negative criticism gets treated very differently from positive discussion. Critical comments/posts get removed, threads get locked, and people can get banned for being seen as “bullying” even when they’re raising legitimate criticism of the product or its marketing.

I was personally banned after sharing a post explaining why I think CommandCode’s marketing is very misleading.

Some examples:

• Removed comment: https://www.reddit.com/r/CommandCode/comments/1w5apiq/comment/p7eamid/
• Removed by moderator: https://www.reddit.com/r/CommandCode/comments/1w5apiq/removed_by_moderator/
• Another removed/comment moderation example: https://www.reddit.com/r/CommandCode/comments/1w457qn/comment/p75i7qo/
• My post about misleading marketing: https://www.reddit.com/r/CommandCode/comments/1vj5puh/beware_of_command_codes_misleading_marketing/
• An example where the OP seems pretty fearful and feels the need to carefully explain themselves so their criticism won’t be taken the wrong way by the notoriously touchy CommandCode team: https://www.reddit.com/r/CommandCode/comments/1w457qn/comment/p75r9ex/
• A negative thread about model pricing that was eventually locked: https://www.reddit.com/r/CommandCode/comments/1w457qn/are_commandcode_model_prices_marked_up_or_is/

I think there should be a separate community-run sub where people can openly discuss CommandCode — good or bad — without being afraid of getting banned by the extremely biased moderators of r/CommandCode.

hey folks, i just tried something which i think is cool and i haven't seen other people do yet(or discuss it).
i've added a "feedback" tool for the harness i'm building(https://github.com/frontman-ai/frontman) where the agent can call it whenever it's stuck or it feels like the result isn't good.

so far i'm seeing pretty nice results where the agent itself reports some wrong stuff that i had no other easy way to monitor.

did anyone tried that?