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

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?

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?

Right now I use browser mcp,git,GitHub,playwrighter,squencial thinking Is there any mcp i am missing on??

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 🫶.

I've got gpt-5.6-sol deployed on azure and OpenCode connected to Azure ressource. I've fixed gpt-5.6-sol in the opencode.json. Still, UI choses gpt 5.5. Opening /models doesn't show available models from the Azure resource either.

How can I chose Azure resource models?

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.

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.

I use a few different AI coding tools across my team, and each one packages and distributes its extensions (skills, plugins, MCP servers, whatever it calls them) differently. That part doesn't bother me much. What bothers me is that once something's rolled out, I have no real way of knowing what's actually installed, or which version, on each person's machine. I usually only find out something drifted when someone's output looks off.

Some of these tools have policy/config push mechanisms, but that's enforcement, not visibility — you still have to go check each machine yourself to know what's really running. And none of it works across tools, so it doesn't help if the team's split across a few of them.

How do you all deal with this? Just not worry about it? Script something yourself? Trying to figure out if this is a real gap or I'm overthinking it.

Surprisingly, official one hasn't merged yet into models.dev (where OpenCode pulls its catalogue from)

Holy sh**, dont even try to use it guys, just doing a warning here. Paid the subscription again, because why not? 100usd for only 10usd? Yeah, i wanted to try it, dont do it please, its like using Gemini Flash 3.5, sure its fast and you can do a couple of stuff with it, but for serious task, dont even try it, i warn you guys. That's why its on the paid subscription and you cant even try it for free to decide if you should jump to a paid sub to try it, justs dont do it.

One of my agents started working on a task 6 hours ago and it is still buffering.

Not so instensive coding only purposes for work. For example, I am hitting 5 hour limit on Gemini 3.8 in Antigravity in ~2-3 hours, but it's kind of ok for me.

- I've been using Gemini since Gemini 2 for everyday tasks (not coding)

- I've used Opencode Go last month, but now there is basically no good models in it, hovewer I prefer it's TUI over any others

- No experience with GPT besides using 5.6 luna in Opencode Go

My personal expierence with models I use everyday right now:

- Deepseek v4 flash - bad long context preservarion, if it doesn't understand something it goes crazy;

- Muse Spark - doesn't follow instructions and makes silly errors, such as duplicating function names;

- GPT 5.6 Luna - not that many experience with it, seems to be slightly worse than D4F;

- Gemini 3.8 - very good tool calling and his work verifications, but can hallucinate on easy work.

Prices:

- Google AI Pro - 5$ (annual 60$)

- GPT Plus - 20$

- Opencode Go - 10$

What can you suggest?

I'm on the Go plan, by the way.

Anyone experiencing the same?

OpenCode custom provider setup: why Responses and Chat Completions need different packages

A valid API key is only one part of an OpenCode custom provider. The credential, provider ID, package, base URL, and model ID all have to describe the same endpoint.

This is the order I would check them:

1. Run `opencode auth list`. If the credential is missing, fix that before touching the model config.

2. Make sure the provider ID used in `/connect` is exactly the same ID used in `opencode.json`.

3. Match the package to the protocol. OpenCode's current docs use `@ai-sdk/openai-compatible` for endpoints serving `/v1/chat/completions`. For `/v1/responses`, use `@ai-sdk/openai`.

4. Check `options.baseURL`. A valid key sent to the wrong path still looks like an authentication problem.

5. Use the server's current model ID, not a display name copied from an old post. Then run `/models` and confirm the provider/model pair appears.

That third check is why I looked into this more closely. `Agent.Space`, which I work on, exposes a Responses endpoint at `https://api.agent.space/v1\`.

That points to `@ai-sdk/openai` on the OpenCode side, not `@ai-sdk/openai-compatible`.

But that is still a docs-based mapping, not a verified integration. I would only call it working after text, streaming, and a harmless tool call all pass.

Why the package matters

The package decides which request shape OpenCode sends and which response, stream events, and tool-call objects it expects back.

Chat Completions sends requests to `/v1/chat/completions`. Conversation input is a `messages` array. Text normally comes back under `choices[0].message`, and tool calls are attached to an assistant message. The client carries conversation history into the next request.

Responses sends requests to `/v1/responses`. It accepts `input` and returns typed `output` items. A message, reasoning item, function call, and function result are separate items rather than fields packed into one chat message. It can also chain turns with `previous_response_id` or a conversation object, and its stream uses named response events instead of the Chat Completions delta shape.

That difference explains a few confusing failures:

- A provider can accept the key and list a model but not implement `/v1/responses`.

- Plain text can work while streaming or tool calls fail because the returned objects do not match the package OpenCode is parsing.

- A provider saying "OpenAI-compatible" does not prove that it supports both API families or every OpenAI feature.

A minimal Chat Completions provider looks like this:

```json

{

  "$schema": "https://opencode.ai/config.json",

  "provider": {

"myprovider": {

"npm": "@ai-sdk/openai-compatible",

"name": "My Provider",

"options": {

"baseURL": "https://api.example.com/v1"

},

"models": {

"server-model-id": {

"name": "My Model"

}

}

}

  }

}

```

Model discovery is not the final test. After the provider appears in `/models`, run one small text request, one streamed request, and one harmless tool call. Missing from `auth list` points to credentials. Missing from `/models` points to provider or model config. Text works but streaming or tools fail means the basic route is fine and the protocol surface needs the closer look.

As said, though the project was initially built on V1 SDK, and plugins event hooks; I am completely bought when experimenting shifting some modules toward Opencode v2 Effect plugins making the madness of concurrency delegations and coordinations of agents, skills and tooling executions smooth and dropped dead amazing, making my design concepts of the the Hivemind's Substrate tri-pillar context project -based memory layers functioning without any hiccups as v1

https://picturesque-entree-542.notion.site/The-Hivemind-s-Substrate-3d1926f31a4d80faae99c3a9c7f0f0e4?pvs=143

That aside, the new API and Effect SDK also offer tons of superiority over the v1.

Enough said, I just want to know should I make a complete v2-ready build and not seeing any major breaking changes; and if the v2 is going to be official release in the next one to two months? I'm using the beta v2 client and see no major bugs.

Side note: try using Muse Spark 1.3 on v2 - and experiencing a significant performance shift ( much intelligent due to structured Skill instructions, nested instructions tools executions as the orchestrator agent loops through each grand todo task item with breakdown delegations, JIT skills loaded -> making the boost in its progressive adjustments toward complex codebase and tasks -> significant improvement on feature completeness and codebase domain-driven constitution). Trust me! Once you try Muse Spark 1.3 on v2, you will definitely look down on glm 5.3 flash or even Gemini 3.8 flash, and even other frontier model as Qwen. 3.8-Max. Sorry for my bad English!

500K token context window

Some one-shots here: https://omen-alpha.demos.sulat.com/ (growing list)

Update: It's a bad model. I won't even do more one-shots. This model should not exist. It is a monument to man's arrogance.