Researchers from Microsoft and the University of California, Berkeley, have introduced a free tool called TokenEconomizer that can reduce the cost of using AI coding assistants by up to 31%.
The tool works with popular AI coding assistants, including Claude Code, OpenAI Codex, GitHub Copilot CLI, and Pi.
In tests involving 277 coding tasks, TokenEconomizer reduced costs by 18.4% to 30.9% while maintaining similar or better results in most cases.
The researchers have made the tool publicly available, allowing developers to use it without paying for the software itself.
How Does TokenEconomizer Work?
AI coding assistants use tokens to understand instructions, read code, and complete tasks. Developers pay for these tokens when using paid AI services.
However, researchers found that these assistants often waste tokens by repeating work, using unnecessary tools, and trying the same failed solutions multiple times.
For example, an AI assistant might repeatedly search for the same file or instructions even though it found them during an earlier task.
TokenEconomizer helps avoid this problem by remembering useful information from previous sessions.
It uses three methods to reduce unnecessary work.
First, it remembers important details about a project, such as the commands needed to test the code. This prevents the AI assistant from searching for the same information again.
Second, it removes tools that the AI assistant does not need for a particular task. This reduces the amount of information the AI must process.
Third, it detects when the assistant keeps making the same mistakes and encourages it to try a different approach or stop wasting resources.
The tool works alongside existing AI assistants, so developers do not need to change the AI models themselves.
AI Coding Costs Drop by Up to 31%
The researchers tested TokenEconomizer on 277 software development tasks using different AI models.
The results showed that the tool consistently reduced costs.
| AI Coding Assistant | Cost Reduction | Success Rate Before | Success Rate After |
|---|---|---|---|
| Claude Code + Sonnet 5 | 23.6% | 74.7% | 76.0% |
| Claude Code + Opus 4.8 | 30.9% | 74.0% | 73.0% |
| Codex + GPT-5.6 Terra | 24.7% | 73.8% | 74.7% |
| Codex + GPT-5.6 Sol | 26.1% | 80.1% | 81.7% |
| Copilot CLI + Sonnet 5 | 18.4% | 75.0% | 77.6% |
Source: TokenEconomizer research paper, Table 1. Success rates measure tasks completed correctly on the first attempt.
Claude Code running Opus 4.8 recorded the largest cost reduction at 30.9%, followed by Codex with GPT-5.6 Sol at 26.1%.
The researchers also checked whether spending fewer tokens affected the quality of the AI’s work.
In four of the five tested setups, the AI assistants completed slightly more tasks successfully on their first attempt.
However, Claude Code with Opus 4.8 experienced a small decline in its success rate, dropping from 74% to 73%.
This suggests that developers can reduce AI usage costs without significantly affecting performance, although results may vary.
Tool Gets Better With Continued Use
TokenEconomizer becomes more useful as developers continue working on the same project.
During the first few tasks, it collects information about the project and how the AI assistant works.
It then uses that information to avoid unnecessary steps in future tasks.
The researchers found that some projects initially cost slightly more because the tool needed time to learn.
However, by the sixth task, all six tested projects became cheaper to work on.
Removing unused tools produced the largest individual savings, reducing costs by 13.2% to 17.7%.
Remembering useful project information reduced costs by 11.8% to 14.3%, while preventing repeated failed attempts saved another 5.4% to 7.1% when tested separately.
How Does It Compare With Other AI Cost-Saving Tools?
TokenEconomizer is not the only tool designed to reduce how much AI coding assistants cost.
Other tools, including RTK, Serena, and Aider RepoMap, also try to make coding assistants more efficient.
However, the researchers found that these tools did not consistently reduce costs across every tested project.
| Feature | TokenEconomizer | RTK | Serena | Aider RepoMap |
|---|---|---|---|---|
| Main purpose | Learns from past tasks to reduce unnecessary work | Reduces the size of command outputs | Helps AI understand and navigate code | Provides a summary of project code |
| Learns from previous tasks | Yes | No | No | No |
| Supported AI tools | Claude Code, Codex, Copilot CLI, Pi | 18 AI coding tools | AI tools supporting MCP | Aider |
| Average cost change in research tests | 18.4%–30.9% lower | 8.9% lower to 4.5% higher | Not included in main tests | 6.1% lower to 6.5% higher |
| Cases where costs increased | 0 out of 30 | 12 out of 30 | Not specified | 15 out of 30 |
Source: TokenEconomizer research paper and project documentation. The cost comparisons reflect the study’s tested configurations, not every possible workload.
TokenEconomizer reduced costs across all 30 tested project and model combinations.
By comparison, RTK increased costs in 12 cases, while Aider RepoMap increased costs in 15 cases.
The researchers found that TokenEconomizer performed better overall because it adjusts its approach based on what it learns from each project instead of applying the same method everywhere.
Free to Use With Popular AI Coding Assistants
TokenEconomizer supports Claude Code, OpenAI Codex, GitHub Copilot CLI, and Pi.
It requires Python 3.10 or newer and can run on a regular computer without a dedicated graphics card.
The tool also does not require a separate API key, although developers may still need paid access to the AI coding assistants they use.
TokenEconomizer stores the information it learns about each project locally on the user’s computer.
Developers can download the software from GitHub, and its open-source license allows them to use and modify it.
The researchers believe the tool could be particularly useful for developers who regularly work on large projects using AI coding assistants.
While the tests showed cost reductions of up to 30.9%, actual savings will depend on the AI assistant, the type of project, and how frequently the tool is used.
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