The most cost-effective way is usually:

1) Use the smallest model that still meets quality

For summarization at scale, a cheaper “mini”/small chat model is often enough. Reserve larger models for:

• long or nuanced documents
• high-stakes summaries
• QA/verification passes

A common pattern is:

• small model for first-pass summaries
• larger model only for escalation on low-confidence or important cases

2) Keep prompts very short and consistent

Cost is mostly driven by tokens, so:

• use a compact system prompt
• avoid long chat histories
• send only the source text plus minimal instructions
• use a fixed output schema

Example instruction:

• “Summarize the following in 5 bullet points. Preserve names, numbers, and decisions. Output JSON with fields: summary, key_points.”

3) Control output length aggressively

Set:

• a low max_output_tokens
• a target summary length
• clear formatting constraints

Summaries get expensive when the model rambles.

4) Chunk long inputs, then summarize hierarchically

For large documents:

1. split into chunks
2. summarize each chunk
3. merge those summaries into a final summary

This is usually cheaper and more reliable than sending a huge document to one call, especially if the context window is large but expensive.

A good hierarchy is:

• chunk summaries: very short
• final synthesis: concise
• optional final compression pass

5) Deduplicate and preprocess before calling the model

Before API calls:

• remove boilerplate
• strip repeated headers/footers
• collapse duplicate content
• normalize whitespace
• detect near-duplicate documents

This can save a lot of tokens.

6) Cache aggressively

If inputs repeat or overlap:

• cache summaries by content hash
• reuse chunk summaries
• cache common boilerplate summaries
• consider semantic caching for near-duplicates

At scale, caching often saves more than model choice.

7) Use structured outputs to reduce retries

If the model returns inconsistent formatting, you end up paying for retries. Use:

• JSON schema / structured output
• explicit field requirements
• deterministic settings when possible

8) Batch requests when latency allows

If your provider supports batching or async bulk jobs, use it for:

• lower overhead
• easier throughput management
• potentially lower cost per request

9) Tune for determinism

For summarization, keep temperature low:

• improves consistency
• reduces accidental verbosity
• can reduce retries and cleanup

10) Only use tool calling or RAG if needed

If the task is just summarization, don’t add retrieval, agents, or extra tool calls unless they materially improve accuracy. Extra steps usually increase cost.

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Practical cost-effective architecture

Cheapest common setup

• preprocess text
• chunk by token count
• summarize chunks with a small model
• combine chunk summaries
• final compression with the same small model
• escalate only bad cases

When to use a larger model

Use a larger model only if:

• the content is highly technical/legal/medical
• coherence across the full document matters a lot
• the small model produces poor factual retention
• you need high-quality executive summaries

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Simple rule of thumb

If you’re optimizing for cost:

1. reduce input tokens
2. reduce output tokens
3. use the smallest acceptable model
4. cache
5. summarize hierarchically
6. escalate selectively

If you want, I can sketch a concrete API design for high-volume summarization, including chunking strategy, prompt template, and caching scheme.