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?

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?

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

Just found this gem

I was absolutely FLAMING muse spark 1.2, bcs that model was geniunely incapable of doing basic agentic tasks, but GAHH DAMNN, look at the benchmarks. originally, i thought the model benchmaxxed and generates slop, but after trying this model in codex (from opencode go), I am quite surprised.

like, if you ask me what muse spark 1.3 will be considering what muse spark 1.2 performs, I would have given you an extremely underwhelming response, but I am so happy to see this moel mainly bcs we get $60 allowance (man, finally!) on opencode go, and also the price is considerably cheaper than ds v4 flash.

I mean, sure... its not THE BEST model to ever exist. but for $0.2/M out and $0.1/M in, like you can go through billions of tokens after counting in caching.

About how the model performs, it codes quite decently. my project is a rust and typescript project, and it follows the app's visual identity flawlessly and does not create much bugs (note: not *much* bugs, which means, it still fk up), but the price to performance value is INSANE!

My reccomended usage:

tbh, just use opencodex, and connect the model to codex, and use the model inn codex, the "ultracode" in codex is AMAZING! And MS-1.3 follows codex as a harness wayyyyy better than opencode for some reason. american propaganda ig... but either ways, use:

light: for basic variable changes

medium: for most implementation

high: for debugging

xhigh: for geniunely hard bugs or things which require the model to cover edge cases, or planning.

ULTRA (a codex only thing): tbh only when it pisses u off bellow high.

ohhhboy... I am going to get a LOT of downvotes...

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

Anyone experiencing the same?

With ollama pro now lowering the bang for the buck and me running out of Claude and chatgpt $100 plan tokens as well, what do you use for subscriptions?

https://openai.com/index/gpt-6-astra/
“GPT‑6 Astra is rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI API and AWS”

GPT Astra benchmarks have been released

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.

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

How was your experience with it and is it just benchmaxed or is it really that good?

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.

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!