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      "id": "2026-08-07-cheapest-llm-cost-per-token",
      "title": "Why the Cheapest LLM Can Cost You the Most",
      "description": "A lower cost per token does not mean a lower bill. Here is how a cheaper model runs up more spend through retries, verbose output, and reasoning tokens, and how to compare LLM cost by the finished task instead of the token.",
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        "url": "https://www.linkedin.com/in/anilmurty/"
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      "published_at": "2026-08-07",
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        "finops-for-ai"
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      "title": "AI Budget Overruns: Why Agentic Spend Is So Hard to Forecast",
      "description": "Most agentic AI projects overshoot their budget. Here is why agent spend resists forecasting, and what honest AI cost forecasting looks like when the agent decides how many tokens to use.",
      "url": "https://tokenjam.dev/blog/2026-08-05-ai-budget-overruns-forecasting-agent-spend",
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      "published_at": "2026-08-05",
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      "title": "Measuring AI Agent ROI: You Need the Cost Side First",
      "description": "ROI is value over cost, and most teams can only guess the cost. Here is why agent ROI is hard to prove, and what connecting token spend to business outcomes actually requires.",
      "url": "https://tokenjam.dev/blog/2026-08-03-measuring-agent-roi-token-spend-business-outcomes",
      "markdown_url": "https://tokenjam.dev/blog/2026-08-03-measuring-agent-roi-token-spend-business-outcomes.md",
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        "url": "https://www.linkedin.com/in/anilmurty/"
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      "published_at": "2026-08-03",
      "updated_at": null,
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      "cover_image_url": "https://tokenjam.dev/blog/images/measuring-agent-roi/header.jpg"
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      "id": "2026-07-31-why-ai-bills-rise-as-token-prices-fall",
      "title": "Why AI Bills Keep Rising While Token Prices Fall",
      "description": "Token prices drop about 10x a year, yet AI spending is forecast to hit $2.59 trillion in 2026. This is the Jevons paradox for AI, and the fix is measuring your own consumption.",
      "url": "https://tokenjam.dev/blog/2026-07-31-why-ai-bills-rise-as-token-prices-fall",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-31-why-ai-bills-rise-as-token-prices-fall.md",
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      "published_at": "2026-07-31",
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        "finops-for-ai"
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      "title": "AI Pricing Models: Why Token Cost Breaks Usage-Based and Subscription SaaS",
      "description": "When the unit of work is a metered token with a variable, often invisible cost, flat subscriptions and usage tiers stop mapping to value. The case for outcome-based pricing, and why it needs token-cost measurement first.",
      "url": "https://tokenjam.dev/blog/2026-07-29-ai-pricing-models-outcome-based-token-cost",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-29-ai-pricing-models-outcome-based-token-cost.md",
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        "name": "Anil Murty",
        "role": "Founder, Metabuilder Labs",
        "url": "https://www.linkedin.com/in/anilmurty/"
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      "published_at": "2026-07-29",
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      "title": "Model Downsizing: How to Run Cheap Agent Work on Cheap Models",
      "description": "Model downsizing runs mechanical agent turns on a cheaper model. How to spot downsizing candidates in your own usage and cut agent cost without guessing.",
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      "cover_image_url": "https://tokenjam.dev/blog/images/analyzer-series/downsize/header-16x9.jpg"
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      "title": "Prompt Caching Read vs Write: When Caching Costs More Than It Saves",
      "description": "A cache read is cheap, a cache write costs a premium. The break-even math, the net-negative case, and the write:read ratio that tells you which one you're in.",
      "url": "https://tokenjam.dev/blog/2026-07-13-prompt-caching-read-vs-write-cost",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-13-prompt-caching-read-vs-write-cost.md",
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        "name": "Anil Murty",
        "role": "Founder, Metabuilder Labs",
        "url": "https://www.linkedin.com/in/anilmurty/"
      },
      "published_at": "2026-07-13",
      "updated_at": null,
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        "optimization"
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      "cover_image_url": "https://tokenjam.dev/blog/images/prompt-caching-read-vs-write-cost/header.jpg"
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      "title": "Evals vs Benchmarks vs Certification: What Each One Actually Proves",
      "description": "A mechanism-level explainer of what an eval, a benchmark, and per-decision certification each prove about an AI agent or a model change, why an aggregate pass rate is not per-decision safety, and where the hard, largely-unsolved part still lives.",
      "url": "https://tokenjam.dev/blog/2026-07-10-evals-vs-benchmarks-vs-certification",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-10-evals-vs-benchmarks-vs-certification.md",
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        "role": "Founder, Metabuilder Labs",
        "url": "https://www.linkedin.com/in/anilmurty/"
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      "title": "Some of Your Agent's Tasks Don't Need an Agent",
      "description": "Parts of your agent run the same deterministic tool-call sequence on every run, and you pay model tokens each time to reproduce what a plain script would do for free.",
      "url": "https://tokenjam.dev/blog/2026-07-10-some-agent-tasks-dont-need-an-agent",
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        "name": "Ansh Saxena",
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        "url": "https://github.com/anshss"
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      "title": "Stop Paying to Re-Plan Work Your Agent Already Solved",
      "description": "Agents re-derive the same plan skeleton on every run. TokenJam clusters your runs by plan shape, isolates the planning tokens, and exports the repeated plans as templates you can feed back in.",
      "url": "https://tokenjam.dev/blog/2026-07-10-stop-re-planning-work-youve-solved",
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        "url": "https://github.com/anshss"
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      "cover_image_url": "https://tokenjam.dev/blog/images/stop-re-planning-work-youve-solved/header.jpg"
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      "title": "Stop Paying Frontier-Model Prices for Work a Cheaper Model Handles",
      "description": "Find the agent calls where a cheaper model would likely hold, priced in dollars against your own trace history, so you stop paying frontier rates for mechanical work.",
      "url": "https://tokenjam.dev/blog/2026-07-09-downsize-agent-model-spend",
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        "name": "Ansh Saxena",
        "role": "Engineer, Metabuilder Labs",
        "url": "https://github.com/anshss"
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        "cost",
        "model-selection",
        "optimization"
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      "title": "Instrument Your AI Agent, Then Find Where the Money Goes",
      "description": "Patch your provider client in one line so the TokenJam SDK captures every LLM call to a local, on-disk trace, then run local analyzers that turn those traces into priced savings across your self-built agent.",
      "url": "https://tokenjam.dev/blog/2026-07-09-instrument-your-ai-agent-with-tokenjam-sdk",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-09-instrument-your-ai-agent-with-tokenjam-sdk.md",
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        "url": "https://github.com/anshss"
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        "cost",
        "agents"
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      "title": "CLAUDE.md Best Practices: What a Good One Actually Looks Like",
      "description": "A good CLAUDE.md gives Claude Code the architecture, the critical rules, and the worktree discipline it needs to work in a multi-agent repo. Here's the anatomy, with real examples.",
      "url": "https://tokenjam.dev/blog/2026-07-08-claude-md-best-practices",
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        "name": "Anil Murty",
        "role": "Founder, Metabuilder Labs",
        "url": "https://www.linkedin.com/in/anilmurty/"
      },
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        "multi-agent",
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      "cover_image_url": "https://tokenjam.dev/blog/claude-md-best-practices-hero.jpg"
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      "title": "Did That Session Even Need Opus?",
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        "url": "https://github.com/anshss"
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      "title": "From Tokenmaxxing to Tokenminimizing",
      "description": "The culture is shifting from throwing tokens at every problem to seeing and cutting the waste, and for Claude Code subscribers that changes what a quota tool is even for.",
      "url": "https://tokenjam.dev/blog/2026-07-08-from-tokenmaxxing-to-tokenminimizing",
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        "name": "Ansh Saxena",
        "role": "Engineer, Metabuilder Labs",
        "url": "https://github.com/anshss"
      },
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    },
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      "title": "Half Your System Prompt Isn't Doing Any Work",
      "description": "System prompts quietly accumulate dead-weight tokens you re-pay on every call, and TokenJam's Trim lever scores which tokens carry little significance so you can see what to cut.",
      "url": "https://tokenjam.dev/blog/2026-07-07-half-your-system-prompt-isnt-working",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-07-half-your-system-prompt-isnt-working.md",
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      "author": {
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        "name": "Ansh Saxena",
        "role": "Engineer, Metabuilder Labs",
        "url": "https://github.com/anshss"
      },
      "published_at": "2026-07-07",
      "updated_at": null,
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        "prompt-engineering",
        "cost",
        "optimization"
      ],
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      "reading_time_minutes": 8,
      "cover_image_url": "https://tokenjam.dev/blog/images/half-your-system-prompt-isnt-working/header.jpg"
    },
    {
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      "title": "The Prompt-Caching Discount Most Agents Leave on the Table",
      "description": "Prompt caching gives roughly 30-60% off the repeated prefix tokens your agent re-sends every call, and TokenJam measures your current cache usage and recommends where to place cache_control.",
      "url": "https://tokenjam.dev/blog/2026-07-07-prompt-caching-discount-youre-not-using",
      "markdown_url": "https://tokenjam.dev/blog/2026-07-07-prompt-caching-discount-youre-not-using.md",
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      "author": {
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        "name": "Ansh Saxena",
        "role": "Engineer, Metabuilder Labs",
        "url": "https://github.com/anshss"
      },
      "published_at": "2026-07-07",
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        "cost",
        "optimization"
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      "cover_image_url": "https://tokenjam.dev/blog/images/prompt-caching-discount-youre-not-using/header.jpg"
    },
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      "id": "2026-07-06-model-autorouting-savings-proof-step",
      "title": "Why Model Autorouting Savings Need a Proof Step",
      "description": "Model autorouting to a cheaper, smaller, or open-source model shows a big savings number before any work is redone. That figure is a prediction of your AI spend, not a result. Here's why LLM cost savings from an autorouted swap stay a hypothesis until you replay it on your own tasks and measure whether quality holds or regresses.",
      "url": "https://tokenjam.dev/blog/2026-07-06-model-autorouting-savings-proof-step",
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        "name": "Anil Murty",
        "role": "Founder, Metabuilder Labs",
        "url": "https://www.linkedin.com/in/anilmurty/"
      },
      "published_at": "2026-07-06",
      "updated_at": null,
      "tags": [
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        "agents",
        "models",
        "evaluation",
        "optimization"
      ],
      "pillar": "thesis",
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      "cover_image_url": "https://tokenjam.dev/blog/2026-07-06-model-autorouting-savings-proof-step.jpg"
    },
    {
      "object": "blog_post",
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