{
  "version": "1.0.0",
  "generated": "2026-05-24T00:16:00Z",
  "source": "orchestrator-deep-analysis",
  "models": [
    {
      "id": "deepseek-v4-pro-max",
      "name": "DeepSeek V4-Pro Max",
      "organization": "DeepSeek",
      "parameters": "1.6T/49B active MoE",
      "context_window": "1M",
      "swe_bench": 80.6,
      "if_score": 89,
      "categories": ["coding", "agent", "reasoning"],
      "provider": "ollama-cloud"
    },
    {
      "id": "kimi-k2-6",
      "name": "Kimi K2.6",
      "organization": "Moonshot AI",
      "parameters": "1T/32B active MoE",
      "context_window": "256K→1M",
      "swe_bench": 80.2,
      "if_score": 91,
      "categories": ["coding", "agent", "multimodal"],
      "provider": "ollama-cloud"
    },
    {
      "id": "qwen3-coder-480b",
      "name": "Qwen3-Coder 480B",
      "organization": "Qwen",
      "parameters": "480B/35B active",
      "context_window": "256K→1M",
      "swe_bench": 66.5,
      "if_score": 88,
      "categories": ["coding", "agent"],
      "provider": "ollama-cloud"
    },
    {
      "id": "minimax-m2.5",
      "name": "MiniMax M2.5",
      "organization": "MiniMax",
      "parameters": "MoE undisclosed",
      "context_window": "128K",
      "swe_bench": 80.2,
      "if_score": 82,
      "categories": ["coding", "agent"],
      "provider": "ollama-cloud"
    },
    {
      "id": "glm-5.1",
      "name": "GLM-5",
      "organization": "Z.ai",
      "parameters": "744B/40B active",
      "context_window": "128K",
      "swe_bench": null,
      "if_score": 90,
      "categories": ["reasoning", "agent"],
      "provider": "ollama-cloud"
    },
    {
      "id": "qwen3-6-plus",
      "name": "Qwen 3.6 Plus",
      "organization": "Qwen",
      "parameters": "Hybrid MoE",
      "context_window": "1M",
      "swe_bench": 78.8,
      "if_score": 91,
      "categories": ["coding", "agent", "reasoning"],
      "provider": "openrouter",
      "note": "FREE on OpenRouter. Rate-limited."
    }
  ],
  "recommendations": [
    {
      "agent": "frontend-developer",
      "action": "sync_to_source_of_truth",
      "current_model_in_agent_versions": "ollama-cloud/qwen3-coder:480b",
      "source_of_truth_model": "ollama-cloud/minimax-m2.5",
      "impact": "high",
      "expected_improvement": {
        "quality": "+6% (92 vs 86 in benchmark matrix)",
        "speed": "~1x",
        "context_window": "128K"
      },
      "score_before": 86,
      "score_after": 92,
      "score_delta": 6,
      "rationale": "agent-versions.json is stale. kilo-meta.json (source of truth) already has minimax-m2.5. Matrix score for frontend-dev on M2.5 = 92 (highest!). MiniMax also leads SWE-bench at 80.2%.",
      "applied": false,
      "applied_date": null
    },
    {
      "agent": "lead-developer",
      "action": "sync_to_source_of_truth",
      "current_model_in_agent_versions": "ollama-cloud/nemotron-3-super",
      "source_of_truth_model": "ollama-cloud/qwen3-coder:480b",
      "impact": "high",
      "expected_improvement": {
        "quality": "+22% (92 vs 70 in benchmark matrix)",
        "speed": "~1x",
        "context_window": "256K→1M"
      },
      "score_before": 70,
      "score_after": 92,
      "score_delta": 22,
      "rationale": "agent-versions.json shows nemotron-3-super (outdated). kilo-meta.json has qwen3-coder:480b. Matrix score: qwen3-coder 92 is the highest for lead-developer. SWE-bench 66.5% and massive coding context make it the SOTA choice.",
      "applied": false,
      "applied_date": null
    },
    {
      "agent": "system-analyst",
      "action": "consider_upgrade",
      "current_model": "ollama-cloud/glm-5.1",
      "recommended_model": "ollama-cloud/deepseek-v4-pro-max",
      "impact": "medium",
      "expected_improvement": {
        "quality": "+6% (88 vs 82 in benchmark matrix)",
        "speed": "~1x",
        "context_window": "128K→1M"
      },
      "score_before": 82,
      "score_after": 88,
      "score_delta": 6,
      "rationale": "system-analyst matrix: glm-5.1 = 82, deepseek-v4-pro-max = 88. 1M context is critical for architecture docs. However GLM-5.1 has Arena ELO 1451 and strong reasoning. Keep GLM-5.1 if standardization across 12 agents matters; otherwise deepseek-v4-pro-max gives measurable gain.",
      "applied": false,
      "applied_date": null
    },
    {
      "agent": "evaluator",
      "action": "consider_upgrade",
      "current_model": "ollama-cloud/glm-5.1",
      "recommended_model": "ollama-cloud/kimi-k2.6",
      "impact": "medium",
      "expected_improvement": {
        "quality": "+6% (84 vs 78)",
        "speed": "~1x",
        "context_window": "128K→256K"
      },
      "score_before": 78,
      "score_after": 84,
      "score_delta": 6,
      "rationale": "evaluator needs high IF and reasoning accuracy. kimi-k2-6 IF=91, matrix score 84 vs glm-5.1 78. Alternative: deepseek-v4-pro-max also 84.",
      "applied": false,
      "applied_date": null
    },
    {
      "agent": "planner",
      "action": "confirm_current",
      "current_model": "ollama-cloud/deepseek-v4-pro-max",
      "impact": "low",
      "expected_improvement": {
        "quality": "0% (already optimal)",
        "speed": "~1x",
        "context_window": "1M"
      },
      "score_before": 88,
      "score_after": 88,
      "score_delta": 0,
      "rationale": "planner is already on deepseek-v4-pro-max, which is the best model for this role (88). GPQA 90.1 confirms strong reasoning for chain-of-thought planning. No change needed.",
      "applied": true,
      "applied_date": "2026-04-27"
    },
    {
      "agent": "reflector",
      "action": "confirm_current",
      "current_model": "ollama-cloud/deepseek-v4-pro-max",
      "impact": "low",
      "expected_improvement": {
        "quality": "0% (already optimal)",
        "speed": "~1x",
        "context_window": "1M"
      },
      "score_before": 84,
      "score_after": 84,
      "score_delta": 0,
      "rationale": "reflector already on deepseek-v4-pro-max (84), the best fit. Self-reflection requires strong reasoning chains; deepseek-v4 excels here.",
      "applied": true,
      "applied_date": "2026-04-27"
    },
    {
      "agent": "workflow-architect",
      "action": "consider_upgrade",
      "current_model": "ollama-cloud/glm-5.1",
      "recommended_model": "ollama-cloud/kimi-k2.6",
      "impact": "medium",
      "expected_improvement": {
        "quality": "+6% (82 vs 76)",
        "speed": "~1x",
        "context_window": "128K→256K"
      },
      "score_before": 76,
      "score_after": 82,
      "score_delta": 6,
      "rationale": "workflow-architect matrix: glm-5.1 = 76, kimi-k2-6 = 82. Alternative deepseek-v4-pro-max = 80.",
      "applied": false,
      "applied_date": null
    },
    {
      "agent": "pipeline-judge",
      "action": "consider_free_tier",
      "current_model": "ollama-cloud/glm-5.1",
      "recommended_model": "openrouter/qwen3-6-plus:free",
      "impact": "low",
      "expected_improvement": {
        "quality": "+4% (80 vs 76)",
        "speed": "~1x (rate-limited)",
        "context_window": "128K→1M"
      },
      "score_before": 76,
      "score_after": 80,
      "score_delta": 4,
      "rationale": "qwen3-6-plus is FREE on OpenRouter with IF=91 and SWE-bench 78.8. For pipeline-judge (measurement-only, no code writing) free tier can cut costs. BUT: OpenRouter free has strict rate limits; verify before production.",
      "applied": false,
      "applied_date": null,
    },
    {
      "agent": "orchestrator",
      "action": "confirm_current",
      "current_model": "ollama-cloud/kimi-k2.6",
      "impact": "low",
      "expected_improvement": {
        "quality": "0% (already optimal)",
        "speed": "~1x",
        "context_window": "256K"
      },
      "score_before": 92,
      "score_after": 92,
      "score_delta": 0,
      "rationale": "orchestrator on kimi-k2.6 is the absolute best fit (92). 300 sub-agent swarm capability aligns with orchestration needs. IF=91 ensures routing accuracy.",
      "applied": true,
      "applied_date": "2026-04-27"
    },
    {
      "agent": "the-fixer",
      "action": "confirm_current",
      "current_model": "ollama-cloud/kimi-k2.6",
      "impact": "low",
      "expected_improvement": {
        "quality": "0% (already optimal)",
        "speed": "~1x",
        "context_window": "256K"
      },
      "score_before": 90,
      "score_after": 90,
      "score_delta": 0,
      "rationale": "the-fixer on kimi-k2.6 (90) is optimal. SWE-Pro 58.6 (#1!) and strong bug-fixing capabilities make it the best choice. MiniMax M2.5 and DeepSeek V4-Pro Max tie at 88, but kimi-k2-6 leads.",
      "applied": true,
      "applied_date": "2026-04-27"
    },
    {
      "agent": "memory-manager",
      "action": "confirm_current",
      "current_model": "ollama-cloud/qwen3.6-plus",
      "impact": "low",
      "expected_improvement": {
        "quality": "0% (already optimal)",
        "speed": "~1x",
        "context_window": "1M"
      },
      "score_before": 87,
      "score_after": 87,
      "score_delta": 0,
      "rationale": "memory-manager on qwen3.6-plus (87) is the best fit. 1M context is critical for memory operations. DeepSeek V4-Pro Max and Nemotron-3-Super tie at 86.",
      "applied": true,
      "applied_date": "2026-04-27"
    }
  ],
  "data_gaps": [
    {
      "gap": "performance_log is empty for ALL agents",
      "severity": "critical",
      "impact": "Cannot compute Avg Score, Success Rate, Avg Duration",
      "action": "Instrument agent-executions.jsonl parser into sync-agent-history.ts to populate performance_log from Gitea issue comments"
    },
    {
      "gap": "No latency / TPS per model",
      "severity": "high",
      "impact": "Cannot optimize speed or cost-per-token for high-frequency agents (orchestrator, code-skeptic)",
      "action": "Add timing instrumentation to pipeline-judge and log wall-clock time per agent invocation"
    },
    {
      "gap": "No invocation frequency / heatmap per agent",
      "severity": "medium",
      "impact": "Cannot identify bottlenecks or overused agents; no data for load-balancing decisions",
      "action": "Add invocation counter to agent-executions.jsonl and build frequency heatmap in dashboard"
    },
    {
      "gap": "No A/B test results for model changes",
      "severity": "medium",
      "impact": "Recommendations are purely benchmark-based, not validated with real pipeline data",
      "action": "After any model change, run 5 pipeline iterations and compare fitness scores before/after"
    },
    {
      "gap": "Missing cost data for OpenRouter free-tier agents",
      "severity": "medium",
      "impact": "Cannot compute true ROI for pipeline-judge / evaluator if switched to free models",
      "action": "Track actual token consumption per provider and compute $/task"
    },
    {
      "gap": "Stale agent-versions.json (not synced with kilo-meta.json)",
      "severity": "high",
      "impact": "Dashboard shows incorrect current models for 8+ agents; recommendations targeting wrong baseline",
      "action": "Run sync-agent-history.ts with kilo-meta.json as primary source and fix JSON parse error in kilo.jsonc"
    },
    {
      "gap": "No custom benchmark for markdown-validator",
      "severity": "low",
      "impact": "markdown-validator scores are lowest across matrix (68 max). Need lightweight-model benchmark.",
      "action": "Create micro-benchmark for YAML frontmatter validation and test nano/instant models"
    }
  ],
  "summary": {
    "agents_total": 34,
    "agents_optimal": 22,
    "agents_need_sync": 2,
    "agents_need_upgrade": 4,
    "agents_consider_free_tier": 1,
    "avg_quality_improvement_potential": "+4.2%",
    "providers_used": ["ollama-cloud", "openrouter"],
    "key_models": ["kimi-k2.6", "deepseek-v4-pro-max", "qwen3-coder-480b", "minimax-m2.5", "glm-5.1"],
    "pending_recommendations": 11,
    "critical_data_gaps": 2
  }
}