Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks

Evaluating performance and efficiency of the GitHub Copilot agentic harness across models and tasks

Explore how the GitHub Copilot agentic harness delivers strong results across multiple benchmarks and leading token efficiency, while maintaining flexibility to choose among more than 20 models.

While the model provides the raw intelligence, the harness shapes how effectively that intelligence is applied. The GitHub Copilot agentic harness is a single shared component of the GitHub Copilot SDK, which powers the GitHub Copilot CLI, GitHub Copilot app, and Copilot code review, along with a wide variety of experiences across GitHub and Microsoft. Improve the harness, and every surface benefits.

The tools, context, and workflow are orchestrated by the harness. A harness should be fast, token-efficient, and predictable for developers. That’s what we designed GitHub Copilot’s agentic harness to do. In this post, we’ll present data showing the efficiency and performance of the GitHub Copilot agentic harness across a wide range of agentic software engineering tasks.

How we iterate with benchmarks

We continuously evaluate the capability and efficiency of the GitHub Copilot agentic harness through a combination of public and internally developed benchmarks. Our public benchmarks include industry standards, while several internal benchmarks are derived from large codebases inside GitHub and Microsoft. We complement this with real-world metrics and online experiments to ensure we understand the harness’s performance in controlled environments and its practical impact on agentic problem solving and task completion.

We control as many variables as possible to evaluate the performance of GitHub Copilot’s harness compared to the model provider’s harness: use the same model, the same benchmark task, normalized on context window, reasoning efforts, tool selection, and MCP servers.

Below we report our latest results for a subset of the benchmarks we track, across four leading models: Claude Sonnet 4.6, Claude Opus 4.7, GPT‑5.4, and GPT‑5.5:

Throughout, we compare GitHub Copilot CLI against the model-vendor harnesses that ship those models natively: Claude Code for Sonnet 4.6 and Opus 4.7, and Codex CLI for GPT‑5.4 and GPT‑5.5.

Token efficiency

Holding the model and task fixed, across multiple benchmark results, the GitHub Copilot harness achieves task completion rates on par with other model-vendor harnesses, while showing lower token consumption across most configurations.

Task resolution

Token efficiency only matters if the work actually gets done. Task resolution rates for the GitHub Copilot agentic harness across these benchmarks are on-par with model-vendor harnesses when used with a fixed model and benchmark task. This ensures that the full potential of the underlying model is available, along with multi-model flexibility, token efficiency, and memory and context capabilities.

These results reflect effective parity, since the differences in either direction are within the variance due to the stochastic nature of the models, making the cross-harness performance on-par.

TerminalBench: Token efficiency, task completion, and variance

To continuously improve the GitHub Copilot agentic harness on task completion and token efficiency, we regularly perform thorough analyses across benchmarks. Below is an example of variance analysis on TerminalBench 2.0, which not only highlights GitHub Copilot’s strength on task completion and token efficiency, but also shows the run-to-run variance intrinsic to this kind of benchmark.

Every marker is one agent-and-model configuration on TerminalBench 2.0, with resolution rate on the vertical axis and dollar cost per task on the horizontal axis. The shaded ellipse around each point shows the ±1σ run-to-run spread, displaying how much each configuration varies between runs.

Three things stand out:

• GitHub Copilot’s agentic harness is on par with or ahead of other agents on task completion and cost per task across the configurations we evaluated. Purple (Copilot) markers and their same-model competitors sit within overlapping ellipses on both axes for nearly every model-the differences are inside run-to-run variance. Copilot is never below a competitor on completion or to the right on cost.
• Run-to-run variability. We ran each agent-model combination at least five times. The ellipse marks the 1σ spread of those runs; a tighter ellipse in the chart means more reproducible results, while a wider one shows results swinging further from run to run on both cost and task completion.
• The benefit of GitHub Copilot’s model choice: The chart shows a real trade-off: GPT models (left) deliver the best value: strong resolution at the lowest cost. Claude Opus (upper right) reaches the highest resolution at a premium. GitHub Copilot puts both on the table, so you can pick efficiency or peak quality per task.

One harness, many models

The GitHub Copilot agentic harness supports 20+ frontier models across the GPT, Claude, Gemini, and MAI families, plus bring your own key for open‑source and local models. You can choose the right model for the capability and cost profile of each task, or let Auto model selection choose for you, balancing task intent and model health to optimize token efficiency.

A multi‑model architecture also unlocks harness‑level capabilities a model-vendor harness simply can’t offer. Rubber Duck, for example, uses cross‑model‑family critique, where one model reviews another’s work to improve outcomes beyond what any single model produces alone.

Conclusion

Benchmarks are just one signal among several. We are constantly working to improve quality across benchmarks, real-world usage metrics, and online experiments, while pushing to efficiently make the most out of every token.

GitHub Copilot delivers task‑resolution on par with leading model-vendor harnesses while using fewer tokens across several configurations, without locking you into a single model through its multi‑model architecture. For developers, this means you can get comparable task completion with lower token cost, while still choosing the model that best fits your task.

Try it yourself

Try GitHub Copilot with the model of your choice, compare approaches on the tasks you run every day, and see how different models and agent strategies perform in your environment.

Learn more about:

• The same agentic harness powers these experiences. We’re continuing to improve its quality, efficiency, and flexibility.

Methodology

To make the comparison as controlled and reproducible as possible, we run each agent with equivalent settings across models, tasks, and environments. All runs have a two-hour timeout. All agents run non-interactively single-turn, with web-tools disabled, and all tools allowed.

TerminalBench2 analysis: Default settings enabled for agents with reasoning effort set to medium (e.g. tool search is enabled for Claude Code and Copilot CLI uses github-mcp-server). Codex and Claude Code use direct Anthropic and OpenAI endpoints. To ensure complete and reliable results, any missing data or infrastructure-related failures were re-run until all 89 TerminalBench2 tasks produced results. Model-generated errors were retained and not excluded from the analysis. Each model was evaluated across five independent runs, and Copilot was tested in two separate evaluation batches to enable comparison with Claude Code and Codex.

All benchmarks: All agent model pairs normalized to same context window size, same prompt token limits, reasoning effort (medium) and settings-no tool search, no MCP servers. Keeping the harness’s default built-in tools. Infrastructure-related anomalies and network-access effects are excluded across all agents for a benchmark to ensure fair comparisons. To reduce the impact of run-to-run variability on smaller benchmarks (<100 instances), five independent runs were conducted, and the best scored run is reported. All metrics are presented as pass@1.

These normalizations mean results differ from public benchmark submissions, which typically use higher reasoning effort and other tuned settings.