Legal/Medical AI Costs: GPT vs Claude vs Gemini (Long Text)

Disclaimer: This article is only a reference example of a technical cost estimate and does not constitute legal or medical advice. Before actually deploying the scenarios above, qualified professionals must still review AI outputs and confirm compliance with applicable regulations and internal institutional policies.

Why cost estimation matters especially for long-text tasks

Tasks such as court judgment summaries and medical record summaries share one characteristic: the input is long and the output is relatively short. A full court judgment can easily run to tens of thousands of characters, and a single medical record may contain a thousand or more characters describing the course of treatment, but what you actually need is often only a summary of a few hundred characters. This usage pattern, where input consumes a lot and output is small, means the model's input unit price affects your total cost more directly than its output unit price. This differs from the cost structure of ordinary chat applications, where input and output volumes are similar, so it is worth calculating before choosing a model.

Cost estimates for two scenarios

The estimates below use the live prices read from the BazaarLink model catalog page on September 8, 2026 (including the promotional discounts shown at the time). They are for rough reference only; please refer to the prices shown on the models page at the time you check, since AI model pricing changes frequently and promotional discounts are time-limited.

Scenario assumptions (demonstration numbers set by this article, not results of a statistical survey):

• Court judgment summary: about 15,000 input tokens (full text of the judgment), about 800 output tokens (summary)
• Medical record summary: about 3,000 input tokens (a single outpatient progress note), about 300 output tokens (summary)

Each document looks cheap on its own, but the difference becomes noticeable at scale. If you process 10,000 judgment summaries per month, the monthly bills land at about $40 for GPT-5.6 Luna, about $140 for Gemini 3.7 Flash, and about $570 for Claude Sonnet 4.6, a gap of more than 10 times. This does not mean any model is "worse." It only shows that for summarization work with large input volumes and relatively clear tasks, the positioning and cost differences between models are real. It is worth testing quality with a small batch of real documents before deciding which model to use in production.

How much long context and caching can save

If your system repeatedly includes the same system prompt, format examples, or knowledge base snippets (for example, fixed summary format guidelines), "cache read" is usually much cheaper than regular input tokens. Taking the three models above as examples, the cache read unit price is roughly 1/10 of the regular input unit price (calculated from the 2026-09-08 catalog prices for those three models). If your summary tasks involve a large amount of repeated fixed context (for example, the same set of review rules applied repeatedly across different cases), designing your calls to hit the cache is a more direct way to save money than switching models.

Why multi-model failover matters for high-availability needs

AI systems for legal and medical teams are usually not "toy projects" but part of an existing workflow. If an upstream provider has a temporary problem (timeouts, 5xx errors, rate limiting) and there is no fallback, the entire batch of pending cases or medical records gets stuck. This is where the value of a multi-model relay service lies: when the primary model's provider fails, the system can automatically retry with the backup model you specify, without manual intervention or code changes. This is especially meaningful for batch processing work that "cannot stop."

How to write data processing terms into a contract

When processing sensitive documents such as court judgments and medical records, data retention and training-use terms should not be just verbal promises. They should be clearly written into the contract or service terms. Taking BazaarLink as an example, its terms of service clearly state that it does not log, store, or inspect users' API prompts or model responses, and only records the metadata needed for billing (model name, token count, timestamp). Conversation content is not used to train any model.

However, an important boundary needs to be noted: the relay layer having zero data retention does not mean every upstream model provider you choose follows the same policy. Upstream providers (OpenAI, Anthropic, Google, etc.) handle requests under their own privacy policies, and some may retain inputs and outputs for training or improvement. Before choosing a specific model, please review that provider's current terms and privacy policy yourself, and do not assume all models follow the same rules. If your institution has stricter data retention requirements for certain case types, it is recommended to list them explicitly at the procurement stage and ask the provider to confirm in writing.

Conclusion

The cost of long-text tasks depends largely on the unit price of the model you choose and the ratio of input to output, rather than on a vague question like "is AI expensive?" The recommended approach is: first test quality on a small batch of real documents (after de-identification) to confirm the outputs meet your review standards, then estimate monthly costs using actual token usage. At the same time, clarify questions about "whether the system is stable and whether the data is safe," such as data retention terms and failover, rather than discovering after launch that they were never asked.

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