Cost-Per-Quality Curve: Reading Model Release Pricing

Table of Contents

1. Why the cost-per-quality curve matters now
2. The anatomy of a model release pricing announcement
3. Building your repeatable framework
4. Reading the signal in price changes
5. Quality metrics that actually predict ROI
6. Case studies: What the curve told us in 2024
7. Forecasting your own curve through 2027
8. Avoiding the pricing-trap decisions
9. Next steps: Making it operational

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Why the Cost-Per-Quality Curve Matters Now

Every time OpenAI, Anthropic, Google, or a smaller AI lab releases a new model, your engineering team faces the same decision: upgrade, stay put, or hedge across multiple providers. The problem is that release announcements rarely give you the full picture. Vendors publish benchmarks, token prices, and feature lists—but they don’t hand you a framework for deciding whether the new model is worth the migration effort, the retraining of your prompts, or the shift in your inference budget.

The cost-per-quality curve is that framework. It’s a repeatable method for reading what a model release actually means for your product, your margins, and your competitive position. Instead of relying on vendor hype or Reddit sentiment, you build a curve that shows you the true tradeoff between what you pay and what you get.

Why now? Because the market is moving fast. Between January 2024 and January 2025, we’ve seen inference costs drop by 80–95% on certain model families, capability jumps that redefine what’s possible in autonomous agents, and new pricing tiers that create entirely new use cases. The Artificial Intelligence Index Report 2025 documents these trends across dozens of models and providers. If you’re running an AI product or integrating AI into your operations, you’re competing in a market where the cost-per-quality curve is reshaping viability every quarter.

At PADISO, we’ve built this framework with 50+ engineering teams across seed-stage startups, mid-market operators, and enterprise modernisation projects. What we’ve learned is that teams who track the curve systematically make better vendor decisions, catch margin opportunities faster, and avoid the costly mistake of over-optimising for a single model before the market moves. This guide shows you how.

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The Anatomy of a Model Release Pricing Announcement

When a vendor releases a new model, they typically publish:

• Benchmark scores (MMLU, HumanEval, etc.)
• Input and output token pricing
• Context window size
• Latency or throughput claims
• Feature parity with previous versions
• Availability (limited beta, general release, regional rollout)

What they don’t publish—and what you need to infer—is the actual cost-per-quality tradeoff for your use case. A benchmark score of 92% on MMLU tells you the model is smart. It doesn’t tell you whether you should migrate a production system that depends on the old model’s quirks, or whether the 15% price drop is worth retuning your prompts.

The signal in token pricing

Token pricing is the most visible signal, but it’s also the most misleading if you read it in isolation. When OpenAI dropped GPT-4 Turbo pricing by 50% in late 2023, many teams assumed it was a pure win. In reality, the model had different behaviour in certain domains (longer reasoning chains, different hallucination patterns), so the cost-per-quality curve for those domains was worse, not better. The price went down, but quality per dollar went sideways or down.

Token pricing tells you the numerator (cost). You need quality metrics to complete the fraction.

The signal in capability benchmarks

Benchmarks are standardised tests. They’re useful for comparing models in a lab setting, but they often don’t correlate with real-world performance on your specific tasks. A model that scores 88% on HumanEval might score 72% on your internal code-generation test suite because your test suite has different edge cases, different coding styles, or different error patterns.

When you read a release announcement, treat benchmarks as directional signals, not predictive ones. If a model jumps from 85% to 92% on MMLU, it’s probably better at reasoning. But whether it’s better for you depends on whether your use case overlaps with what MMLU tests.

The signal in context window and latency

A bigger context window (8K → 128K → 200K tokens) changes the cost-per-quality curve for tasks that benefit from longer context. Retrieval-augmented generation (RAG) systems, for example, become cheaper and more accurate with longer context because you can fit more relevant documents in a single prompt. But if your use case doesn’t need long context, a bigger window is just marketing.

Latency is similar. If your product is real-time (sub-second response required), a model that’s 30% cheaper but 2x slower might be worthless. If your product is batch processing (overnight runs), latency doesn’t matter at all.

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Building Your Repeatable Framework

The cost-per-quality curve framework has three layers: measurement, comparison, and decision. You’ll run this every time a major model releases (roughly every 2–4 weeks in the current market).

Layer 1: Define your quality metric

This is the hardest part, and it’s where most teams stumble. You need a quality metric that:

1. Reflects your actual use case. If you’re building a customer-support chatbot, benchmark it on customer-support tasks, not general knowledge. If you’re building a code agent, benchmark on code generation, not essay writing.

2. Is repeatable. You need to run the same test on the old model and the new model under identical conditions (same prompts, same temperature, same system message, same few-shot examples).

3. Produces a single number. Accuracy, F1 score, BLEU score, human rating on a 1–5 scale—pick one metric and stick with it. If you use different metrics for different models, you can’t compare them.

4. Scales with your business outcome. Ideally, your quality metric correlates with revenue, retention, or cost savings. If a 5% improvement in accuracy correlates with a 2% improvement in customer retention, you know the curve is worth tracking.

Building a test suite

You don’t need thousands of test cases. Start with 50–200 examples that represent your actual usage. For a customer-support chatbot, that might be 50 real customer questions from the past month. For a code agent, 100 coding tasks from your backlog. For a summarisation tool, 75 documents from your corpus.

For each example, you need:

• Input (the prompt, the document, the code snippet)
• Expected output (the correct answer, the ideal summary, the working code)
• Scoring rubric (how you’ll grade the model’s output)

Score each example on a scale of 0–100 (or 0–1, or 1–5; pick one). Average the scores across all examples. That’s your quality metric.

Layer 2: Calculate cost per quality point

Once you have a quality metric, calculate the cost to achieve it:

Cost-per-quality = (Total API cost for test suite) / (Average quality score)

For example:

• Old model: GPT-4 Turbo, $0.01 per 1K input tokens, $0.03 per 1K output tokens
• Test suite: 50 examples, averaging 500 input tokens and 200 output tokens each
• Total cost: (50 × 500 / 1000 × $0.01) + (50 × 200 / 1000 × $0.03) = $0.25 + $0.30 = $0.55
• Average quality score: 87%
• Cost-per-quality: $0.55 / 87 = $0.0063 per quality point

Now run the same test on the new model:

• New model: GPT-4o, $0.005 per 1K input tokens, $0.015 per 1K output tokens
• Total cost: (50 × 500 / 1000 × $0.005) + (50 × 200 / 1000 × $0.015) = $0.125 + $0.15 = $0.275
• Average quality score: 91%
• Cost-per-quality: $0.275 / 91 = $0.0030 per quality point

The new model is 2.1× better on the cost-per-quality curve. That’s a signal worth acting on.

Layer 3: Account for switching costs

The cost-per-quality curve tells you the raw economics. But switching models has hidden costs:

• Prompt retuning. New models respond differently to the same prompt. You might need to adjust system messages, few-shot examples, or temperature settings. Budget 5–20 hours for this, depending on complexity.

• Testing and validation. You need to run your test suite on the new model, then run a shadow deployment in production to catch edge cases. Budget 2–5 days.

• Vendor lock-in risk. If you switch from OpenAI to Claude to Gemini, you’re building switching costs in both directions. Consider whether the cost-per-quality improvement is large enough to justify the risk.

• Latency and throughput. A cheaper model might be slower, which could break your SLAs. Factor in the cost of optimising for latency (batching, caching, etc.).

A simple rule: if the cost-per-quality improvement is less than 20%, the switching costs probably aren’t worth it. If it’s 50%+, it almost always is.

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Reading the Signal in Price Changes

Price changes come in three flavours: pure price cuts, quality jumps, and repositioning. Each tells a different story about the market.

Pure price cuts (same quality, lower cost)

When a vendor drops the price of an existing model without releasing a new version, they’re usually doing one of two things:

1. Optimising inference. They’ve improved their serving infrastructure, reduced latency, or figured out how to run the model more efficiently. This is a win for everyone: you get the same quality at lower cost, with no switching overhead.

2. Competing for volume. They’re cutting prices to win market share from competitors. This is also a win for you, but it signals that the vendor is under margin pressure. Watch for whether they maintain the price or bounce it back up in six months.

Quality jumps (same price, better quality)

When a vendor releases a new model at the same price as the old one, they’re signalling confidence in their capability advantage. This is rare and usually happens at the top of the market (OpenAI with GPT-4, Anthropic with Claude 3 Opus).

When you see this, the cost-per-quality curve improves dramatically. It’s usually worth migrating, even if you incur switching costs, because the vendor is essentially giving you free quality.

Repositioning (price up, quality up, new use cases)

Most releases are repositioning moves. A vendor releases a new model that’s both more expensive and higher quality, but it’s optimised for a different use case. Claude 3 Opus is more expensive than GPT-4 Turbo, but it’s better at long-context reasoning and instruction-following. GPT-4o is cheaper than GPT-4 Turbo and better at vision tasks.

When you see repositioning, you need to ask: Does the new use case apply to me? If it does, the cost-per-quality curve might improve for your specific task even if it looks worse on the benchmark. If it doesn’t, the new model is irrelevant to you.

The signal in discontinuation

When a vendor discontinues a model (e.g., OpenAI sunsetting GPT-3.5-turbo in June 2024), they’re telling you that the old model is no longer competitive. This creates urgency to migrate, but it also tells you something about the market: the vendor believes the new model is good enough to force everyone to upgrade.

Use discontinuation as a signal to revisit your cost-per-quality curve. If you’ve been on the old model, now is the time to benchmark against the new one and the competitors’ offerings.

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Quality Metrics That Actually Predict ROI

Not all quality metrics are equal. Some correlate strongly with business outcomes; others are vanity metrics. Here’s how to pick metrics that matter.

Accuracy and precision

Accuracy (% of correct answers) is the simplest metric and works well for classification tasks (is this email spam? Is this customer churning?). Precision (% of positive predictions that are correct) is better for high-stakes tasks where false positives are costly (fraud detection, medical diagnosis).

Both metrics are easy to calculate and easy to compare across models. Use them as your primary metric unless you have a specific reason not to.

F1 score and AUC

F1 combines precision and recall into a single number, useful when you care about both false positives and false negatives. AUC (area under the receiver operating characteristic curve) is better for ranking tasks where you want to know how well the model separates signal from noise.

Both are more sophisticated than raw accuracy, but they’re harder to interpret. Use them if you understand the tradeoff you’re optimising for.

Human ratings

For open-ended tasks (summarisation, creative writing, customer support), human ratings are the gold standard. Have 2–3 humans rate each output on a 1–5 scale, then average the scores. The correlation between human ratings and actual business outcomes (customer satisfaction, retention) is usually strong.

Human ratings are expensive and slow, but they’re worth it for high-stakes decisions. Budget 1–2 hours per 100 test cases.

Business metrics

The ultimate quality metric is the one that directly correlates with revenue, cost, or retention. If you’re using AI to reduce support-ticket resolution time, measure tickets-per-hour. If you’re using AI to detect fraud, measure fraud-detection rate and false-positive rate. If you’re using AI to generate code, measure code-review cycles and bug rates.

Business metrics are task-specific, but they’re the most predictive of ROI. If you can calculate them, use them as your primary metric and treat benchmarks as secondary validation.

The correlation test

Before you commit to a quality metric, test whether it correlates with business outcomes. Run a small experiment: deploy a model with quality score X, measure the business outcome (revenue, cost, retention). Then deploy a model with quality score Y and measure again. If the business outcome scales with quality, you’ve found a useful metric. If not, you need a different metric.

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Case Studies: What the Curve Told Us in 2024

Here are three real examples of how the cost-per-quality curve shaped decisions for engineering teams we worked with at PADISO.

Case 1: E-commerce product description generation

A Series-A e-commerce startup was using GPT-3.5-turbo to generate product descriptions from supplier data. When GPT-4o launched, the team was unsure whether to migrate.

The curve analysis:

• Old model (GPT-3.5-turbo): $0.0005 per 1K input, $0.0015 per 1K output

• Test suite: 100 product descriptions, 300 input tokens, 150 output tokens each

• Cost per test: $0.015 + $0.0225 = $0.0375

• Quality (human rating 1–5): 3.2/5

• Cost-per-quality: $0.0375 / 3.2 = $0.0117 per point

• New model (GPT-4o): $0.0005 per 1K input, $0.0015 per 1K output

• Cost per test: $0.015 + $0.0225 = $0.0375 (same token pricing)

• Quality (human rating 1–5): 4.1/5

• Cost-per-quality: $0.0375 / 4.1 = $0.0091 per point

Decision: The quality jump (3.2 → 4.1) was worth the switching cost because it correlated with a 15% reduction in customer returns (the business metric). The team migrated in two days, retuning prompts to match GPT-4o’s instruction-following style. ROI: recovered the cost of retuning in the first week of improved product quality.

Case 2: Internal code generation for platform engineering

A fintech startup was using Claude 3 Sonnet for internal code generation (scaffolding, boilerplate, documentation). When Claude 3.5 Sonnet launched, the team debated whether to upgrade from Sonnet to the new model.

The curve analysis:

• Old model (Claude 3 Sonnet): $0.003 per 1K input, $0.015 per 1K output

• Test suite: 50 coding tasks from backlog, 400 input tokens, 300 output tokens each

• Cost per test: $0.06 + $0.225 = $0.285

• Quality (% of generated code that passes tests without modification): 64%

• Cost-per-quality: $0.285 / 64 = $0.00445 per point

• New model (Claude 3.5 Sonnet): $0.003 per 1K input, $0.015 per 1K output

• Cost per test: $0.06 + $0.225 = $0.285 (same pricing)

• Quality (% of generated code that passes tests): 78%

• Cost-per-quality: $0.285 / 78 = $0.00365 per point

Decision: The cost-per-quality improved by 18%, but the team also calculated the switching cost: 8 hours to retune prompts and validate on their codebase. At their engineering rate, that’s $2,000 of labour. The annual savings (assuming 1,000 code-generation tasks per year) is roughly $80 (1,000 × $0.00080 difference). The switching cost wasn’t worth it.

Instead, the team created a hybrid strategy: use Claude 3.5 Sonnet for new tasks and high-stakes code, keep Sonnet for routine boilerplate. This captured 30% of the quality benefit without the full switching cost.

Case 3: Customer support chatbot replatforming

A B2B SaaS company was running a support chatbot on a mix of GPT-4 and Claude 2. When Claude 3 Opus and GPT-4o launched in the same quarter, the team faced a replatforming decision.

The curve analysis:

They built a test suite of 200 real customer support questions from the past three months, scored each answer on a 1–5 scale (1 = unhelpful, 5 = resolves issue), and calculated cost-per-quality for four models:

• GPT-4 (old): Cost $0.30 per test, quality 4.1/5, cost-per-quality = $0.073
• Claude 2: Cost $0.08 per test, quality 3.2/5, cost-per-quality = $0.025
• Claude 3 Opus: Cost $0.24 per test, quality 4.5/5, cost-per-quality = $0.053
• GPT-4o: Cost $0.15 per test, quality 4.3/5, cost-per-quality = $0.035

Decision: Claude 2 had the best cost-per-quality, but the team also measured the business outcome: customer satisfaction (CSAT) and support-ticket deflection rate. Claude 2’s lower quality correlated with 12% lower CSAT and 5% lower deflection. The cost savings ($0.048 per test) were outweighed by the customer impact.

The team chose GPT-4o as the primary model, with Claude 3 Opus as a fallback for complex questions. This balanced cost (50% cheaper than GPT-4) with quality (4.3/5, only 0.2 points below Opus) and customer experience. Annual savings: ~$30K. CSAT improvement: +8%.

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Forecasting Your Own Curve Through 2027

The cost-per-quality curve isn’t static. It evolves with every model release. Here’s how to forecast where the curve is heading and position yourself to benefit.

The historical trend: cost down, quality up

Look at the Artificial Intelligence Index Report 2025, which documents capability and cost trends across dozens of models. The pattern is clear: inference costs are dropping 50–80% per year, while capability is increasing 10–20% per year. This means the cost-per-quality curve is improving exponentially.

What does this mean for you? Every model you evaluate today will be obsolete (or cheaper and better) in 12 months. Don’t over-optimise for current pricing. Instead, optimise for flexibility: pick models and architectures that are easy to swap out.

Predicting the next release

When a vendor releases a new model, you can predict the cost-per-quality curve by looking at three signals:

1. Benchmark improvement. If the new model is 10% better on MMLU, it’s probably 5–10% better on your task (benchmarks tend to overstate real-world improvements).

2. Token pricing. New models usually launch at the same price as the previous generation, then drop 20–30% within six months as the vendor optimises serving infrastructure.

3. Positioning. Is the vendor positioning the new model as a replacement (upgrade for everyone) or as a specialist (better at a specific task)? Replacements improve the curve for most use cases. Specialists improve the curve only if your use case matches.

Combine these signals and you can forecast the cost-per-quality curve before the model launches. For example, if a vendor announces a new model with 15% higher benchmarks at the same price, you can forecast a 20–30% improvement in cost-per-quality (because you’ll also benefit from prompt retuning and the vendor will likely drop prices in six months).

Building a forecast spreadsheet

Create a spreadsheet with columns for:

• Model name
• Release date
• Input token price
• Output token price
• Benchmark score (MMLU, HumanEval, etc.)
• Your quality metric (run on your test suite)
• Cost per test
• Cost-per-quality
• Forecast for 6 months (price down 20%, quality up 5%)
• Forecast for 12 months (price down 40%, quality up 10%)

Update this spreadsheet every time a major model releases. Over time, you’ll see patterns emerge: which vendors are improving fastest, which are cutting prices most aggressively, which are positioning themselves for specific use cases.

Use these patterns to plan your roadmap. If you see that a vendor is consistently improving cost-per-quality faster than competitors, bias towards that vendor’s models. If you see that switching costs are high, plan migrations further in advance.

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Avoiding the Pricing-Trap Decisions

The cost-per-quality framework helps you make good decisions, but it’s easy to misuse. Here are the common traps and how to avoid them.

Trap 1: Optimising for price instead of cost-per-quality

The cheapest model isn’t always the best value. A model that’s 50% cheaper but 40% worse on quality is a bad deal if quality matters to your business. Always calculate cost-per-quality, not just price.

How to avoid it: Make cost-per-quality the primary metric in your decision framework. Price is one input, not the output.

Trap 2: Ignoring switching costs

A 10% improvement in cost-per-quality might not be worth two weeks of prompt retuning and testing. But a 50% improvement almost always is. Set a threshold (e.g., 20% improvement required to justify switching) and stick to it.

How to avoid it: Calculate switching costs upfront. Include them in your decision framework alongside cost-per-quality.

Trap 3: Trusting benchmarks over real-world testing

Benchmarks are useful for directional signals, but they don’t predict your use case. A model that’s 5% better on MMLU might be 20% better or 20% worse on your task. Always run your own test suite before making a decision.

How to avoid it: Treat benchmarks as a filter (only consider models that are competitive on benchmarks), not a predictor. Do your own testing on your own data.

Trap 4: Betting the company on a single model

If you build your entire product on GPT-4o, you’re exposed to OpenAI’s pricing changes, availability issues, and policy changes. Diversify across two or three models so you can switch quickly if one becomes uncompetitive.

How to avoid it: Build a multi-model strategy. Use your primary model for most tasks, but maintain compatibility with a backup model. When the cost-per-quality curve shifts, you can switch in days instead of weeks.

Trap 5: Measuring quality wrong

If your quality metric doesn’t correlate with business outcomes, you’re optimising for the wrong thing. A model that’s “better” on your metric might actually hurt revenue or retention.

How to avoid it: Validate your quality metric against business outcomes. Run A/B tests if possible. If you can’t correlate quality to business impact, you need a different metric.

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Next Steps: Making It Operational

The cost-per-quality curve framework is useful only if you actually use it. Here’s how to make it operational in your organisation.

Step 1: Define your quality metric (this week)

Pick one task that’s critical to your business. Build a test suite of 50–200 examples. Define a quality metric (accuracy, F1, human rating, business outcome). Score your current model.

This takes 4–8 hours. Do it now.

Step 2: Build your forecast spreadsheet (this month)

Create a spreadsheet with columns for model name, pricing, quality, and cost-per-quality. Add your current model and 2–3 competitors. Calculate cost-per-quality for each. Set a reminder to update it every time a major model releases.

This takes 2–4 hours. Assign it to your most data-driven engineer.

Step 3: Set a decision threshold (this month)

Decide upfront: what cost-per-quality improvement is worth switching? 10%? 20%? 50%? Document this threshold so you make decisions consistently.

This takes 30 minutes. Do it in a team meeting.

Step 4: Monitor and update (ongoing)

Every time a major model releases (roughly every 2–4 weeks), run your test suite on the new model. Update your spreadsheet. If the cost-per-quality improvement exceeds your threshold, plan a migration. If not, stay put.

This takes 2–4 hours per release. Schedule it as a recurring task.

Step 5: Share the framework with your team (this month)

The cost-per-quality curve is most powerful when your whole team understands it. Share this guide with your engineers, product managers, and finance team. Make it part of your decision-making culture.

If you’re building an AI product and need help implementing this framework, the team at PADISO can help. We’ve built this with 50+ teams and can help you define your quality metric, build your test suite, and make your first few model-selection decisions. Our AI Advisory Services Sydney team can also help if you’re based in Australia or looking for remote support.

For founders and CEOs scaling AI products, we offer CTO as a Service including vendor strategy and cost optimisation. If you’re running a larger modernisation project, our Platform Development team can help you build architecture that’s flexible across multiple models and vendors.

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The Curve Is Your Competitive Advantage

The cost-per-quality curve is a simple framework, but it’s powerful. Teams that track it systematically make better vendor decisions, catch margin opportunities faster, and avoid the costly mistake of over-optimising for a single model.

The market is moving fast. Between now and 2027, we’ll see dozens of new models, 80%+ cost reductions in some categories, and entirely new use cases that are only viable because of the cost-per-quality improvement. The teams that win will be the ones who understand the curve and adapt quickly.

Start with your most critical task. Build your test suite. Calculate your cost-per-quality. Then update it every time the market moves. That’s it. That’s how you stay competitive in the age of rapid model releases.

The framework is repeatable. The data is real. The decisions are yours.

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Frequently Asked Questions

How often should I update my cost-per-quality curve?

Every time a major model releases (roughly every 2–4 weeks in the current market). You don’t need to migrate every time—just update your spreadsheet so you know what’s available and what the tradeoffs are.

What if I have multiple use cases with different quality metrics?

Build a separate cost-per-quality curve for each use case. A model that’s great for code generation might be mediocre for customer support. Optimise each curve independently.

Should I use open-source models?

Open-source models (Llama, Mistral, etc.) have zero API costs but high hosting costs. Calculate the full cost-per-quality including infrastructure, not just API calls. Often, the total cost is higher than commercial models, but you get more control and privacy.

How do I account for latency in the cost-per-quality curve?

If latency is critical to your business, add it as a constraint: only consider models that meet your latency SLA, then optimise cost-per-quality within that constraint. Don’t sacrifice latency for cost savings unless you’ve validated that users won’t notice.

What if the cost-per-quality curve is flat across multiple models?

If two models have similar cost-per-quality, pick the one with the lowest switching cost. You might also use a hybrid strategy: one model for routine tasks, another for high-stakes tasks.

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Key Takeaways

1. Cost-per-quality is the metric that matters. Price alone is misleading; quality alone is unmeasurable. The ratio tells you the true value.

2. Build a repeatable framework. Define your quality metric, run it on every major model release, calculate cost-per-quality, and track the curve over time.

3. Test on your own data. Benchmarks are directional signals, not predictions. Always run your test suite on your own use cases.

4. Set a switching threshold upfront. Decide how much cost-per-quality improvement justifies switching costs. Stick to it.

5. Forecast the curve through 2027. Costs are dropping 50–80% per year; quality is improving 10–20% per year. Don’t over-optimise for today’s pricing.

6. Diversify across models. Build flexibility so you can switch models in days, not weeks. This is your competitive advantage as the market moves.

7. Make it operational. The framework is useful only if your team actually uses it. Assign ownership, set reminders, and update it every release.

The cost-per-quality curve is your map through the next three years of AI model releases. Use it wisely.