Inference Effort and LLM Cost Efficiency: How to Choose the Right Reasoning Level

The Counterintuitive Reality: Lower Prices Can Produce Higher Bills

A familiar pattern has emerged in the AI industry: a new flagship model reduces its API prices by roughly 20%, yet an independent evaluator reruns the same benchmark and finds that the average cost per question at the highest reasoning level has actually doubled.

Some observers conclude that next-generation models are simply too expensive for ordinary users. That conclusion, however, focuses on only one part of the problem.

To understand what is happening, it is important to look at how flagship models are billed. Many newer models introduce the concept of thinking tokens. Before producing a final answer, the model generates an internal reasoning process, and those tokens are also counted toward output usage. The model’s reasoning effort level essentially controls the maximum budget available for this internal process.

The basic cost relationship is:

Total cost = input tokens × input price + output tokens × output price

A price reduction does not guarantee a lower total bill. If the model uses a higher effort level and generates significantly more output tokens—for example, increasing from the low hundreds of thousands to the high hundreds of thousands—the additional usage can outweigh the lower unit price.

In other words, a price cut is a change in unit economics, while increased token consumption is a change in usage behavior. These two factors can cancel each other out or even reverse the expected result.

Breaking Down the Effort Levels: Cost Efficiency Is Not Linear

Looking only at the maximum effort level leads to an incomplete conclusion. When benchmark scores and per-question costs are compared across levels, the results typically form a curve with diminishing marginal returns.

The following is a representative third-party evaluation. The score is a composite index, and the cost is a weighted average per question. Results will vary by task and benchmark.

Effort level	Composite score	Cost per question
Max	65	$26.54
Xhigh	62	$14.83
High	62	$9.08
Medium	56	$3.80
Low	54	$3.10

Several important patterns emerge from these figures.

1. The middle-to-high range contains a clear sweet spot

The High level achieves the same score as Xhigh while costing approximately 60% less. Moving from High to Xhigh therefore adds substantial cost without producing a measurable capability improvement in this evaluation.

The difference is even more pronounced at the top end. Max costs nearly three times as much as High but improves the score by only three points.

2. Medium often delivers the largest generational improvement

A same-level comparison between model generations shows that the new model reaches a score of 56 at Medium, with a per-question cost of $3.80. The previous-generation model scored only 43 at the same level and cost $5.56 per question.

That represents a significant score improvement alongside a cost reduction of roughly 30%—the kind of price-performance improvement that matters in practice.

Many coding assistants use Medium as their default effort level. For most everyday development tasks, this setting is already more than sufficient.

3. The highest level represents a different budget category

The most eye-catching demonstrations in model launch presentations often involve scenarios such as autonomous operation lasting dozens of hours or thousands of agents collaborating on a large engineering project. These results are generally produced at the highest effort level and with large-scale parallel execution.

The budget model for such workloads is closer to enterprise batch processing than to a developer opening an IDE session to fix a bug or write a function. These are fundamentally different usage patterns.

Using the cost of the former to judge the practicality of the latter will inevitably produce a distorted conclusion.

Read Official Benchmark Tables Critically

Third-party evaluations are not the only source that requires careful interpretation. Vendor-published benchmark tables should also be examined with attention to methodology.

There are three common pitfalls.

• Mismatched effort levels: A vendor may evaluate its own model at the maximum or second-highest level while reporting a competing model’s result at Medium. This is not an apples-to-apples comparison. Check the effort setting in the footnotes before comparing scores.
• Selective reporting: A new model may lead on some benchmarks while trailing competitors on others. The weaker results are not always included in the main table. Independent evaluations can help provide a broader view.
• Internal benchmarks: Some unusually large improvements—such as an apparent doubling of performance—may come from internal tests. Because internal benchmarks are difficult to compare independently, they should be treated as directional rather than conclusive evidence.

For coding benchmarks, a vendor may claim that the new model at Medium or High achieves a similar problem-solving rate to the previous flagship model at its maximum level, while costing roughly half as much per task.

Vendor-reported figures should naturally be treated with some caution. However, if independent evaluations point in the same direction, the overall conclusion becomes more credible.

Practical Guidance: Choosing the Most Cost-Effective Effort Level

The following strategy can help balance capability and spending.

1. Use Medium for routine development work

Code completion, bug fixes, unit-test generation, and documentation are good candidates for the default Medium level. Its cost-performance ratio is usually the strongest, and many development tools are configured around this type of workload.

2. Increase the level for complex architecture and difficult debugging

For cross-module refactoring, complex system design, or algorithms that require deeper reasoning, try High first.

If High and Xhigh produce similar results—as they do in the example above—there is little justification for paying roughly 60% more for Xhigh.

3. Reserve Max for genuinely difficult problems

Mathematical proofs, formal verification, and long-running autonomous tasks may benefit from Max’s additional reasoning budget. Users should also be prepared for a substantial increase in cost.

4. Implement dynamic effort routing at the application layer

A common engineering practice is to route requests through a gateway or application layer. A low-cost model or effort level first estimates task difficulty, and only harder tasks are escalated.

For example:

def route(task):
    difficulty = estimate_difficulty(task)  # Estimate difficulty with a lightweight model
    if difficulty < 0.4:
        return call_llm(task, effort="low")
    elif difficulty < 0.7:
        return call_llm(task, effort="medium")
    else:
        return call_llm(task, effort="high")

This approach avoids spending the maximum reasoning budget on every request while preserving access to deeper reasoning when it is actually needed.

5. Monitor thinking-token usage

After deployment, track thinking-token consumption separately in your observability system. This metric provides a practical indication of whether effort levels are configured appropriately.

For teams using managed model platforms such as Baidu AI Cloud’s Qianfan service, token-level usage dashboards can likewise be configured through the platform’s monitoring, alerting, and logging capabilities. The exact availability of these features depends on the service environment and should be verified in the relevant documentation.

Two Practical Constraints That Are Easy to Overlook

Finally, two considerations are unrelated to benchmark scores but may matter more for some users.

Safety and compliance boundaries

New flagship models commonly apply stricter safety classification. Requests involving penetration testing, vulnerability exploitation, binary analysis, and similar topics may be blocked or routed to an older model.

Security research teams should confirm in advance whether their existing workflows will be affected.

Be cautious immediately after launch

During the first few days after a model launch, public testing usually consists of isolated examples and small-scale reports. Independent, systematic evaluations often take one to two weeks to mature.

Production upgrade decisions should therefore wait for more reliable third-party data when possible. Teams should also use staged rollouts and A/B testing against real workloads before making a full migration.

Conclusion

A lower model price does not automatically translate into lower usage costs. The key cost-control lever is often the choice of reasoning effort.

By understanding the interaction between unit price and token consumption, avoiding the diminishing returns of the highest levels, and using the middle effort range as the default sweet spot, teams can achieve a better balance between capability and budget.

Dynamic routing at the application layer and continuous token monitoring can further improve that balance, helping each workload use the amount of reasoning it actually requires.