AI Coding Agent Index
Compare capabilities, task accuracy, speed, cost, and developer benchmarks for top-tier autonomous coding agents.
Full Rankings Table
| Rank | Agent / Framework | Agent Index | Cost / Task | Time / Task | Tokens / Task | Action |
|---|---|---|---|---|---|---|
| 1 | Claude Code - Fable 5 (max) (with fallback)Fable 5 (max) | 77% | $11.80 | 23.5m | 8.7M | Details |
| 2 | Codex - GPT-5.5 (xhigh)GPT-5.5 (xhigh) | 76% | $5.07 | 10.1m | 6.8M | Details |
| 3 | Claude Code - Opus 4.8 (max)Opus 4.8 (max) | 73% | $7.70 | 23.1m | 12.6M | Details |
| 4 | Codex - GPT-5.5 (medium)GPT-5.5 (medium) | 71% | $2.75 | 6.4m | 3.8M | Details |
| 5 | Claude Code - Opus 4.8 (medium)Opus 4.8 (medium) | 67% | $3.26 | 12.4m | 4.6M | Details |
| 6 | Opencode - Opus 4.7 (medium)Opus 4.7 (medium) | 65% | $2.93 | 13.5m | 3.7M | Website |
| 7 | Cursor CLI - GPT-5.5 (medium)GPT-5.5 (medium) | 62% | $2.01 | 6.6m | 1.9M | Details |
| 8 | Cursor CLI - Opus 4.7 (medium)Opus 4.7 (medium) | 60% | $2.68 | 11.2m | 2.7M | Details |
| 9 | Claude Code - GLM-5.2GLM-5.2 | 58% | $6.47 | 25.2m | 5.6M | Details |
| 10 | Claude Code - Opus 4.7 (medium)Opus 4.7 (medium) | 57% | $1.68 | 14.5m | 2.2M | Details |
| 11 | Claude Code - GLM-5.1GLM-5.1 | 52% | $4.33 | 15.0m | 4.3M | Details |
| 12 | Cursor CLI - Composer 2.5 FastComposer 2.5 Fast | 52% | $0.55 | 6.8m | 2.1M | Details |
| 13 | Claude Code - DeepSeek V4 Pro (high)DeepSeek V4 Pro (high) | 47% | $0.27 | 17.9m | 5.1M | Details |
| 14 | Claude Code - Kimi K2.6Kimi K2.6 | 47% | $1.18 | 41.2m | 6.0M | Details |
| 15 | Gemini CLI - Gemini 3.1 Pro (high)Gemini 3.1 Pro (high) | 43% | $2.00 | 16.5m | 2.2M | Details |
Theoretical Trends in Agentic Software Engineering
From Chat to Search: The paradigm of software development is shifting from dialogue-based copilots to agentic code search systems. Instead of prompting for single functions, developers run agents like Claude Code that execute Monte Carlo Tree Search (MCTS) to explore codebases, locate bugs, and generate verified patches.
The Compute-Accuracy Tradeoff: There is a direct theoretical correlation between inference compute and task success. Agentic loops trade API cost for accuracy; running verification tests, executing compile steps, and generating multiple candidate solutions increases cost per task ($1.50-$4.00) but drastically boosts pass rate.
IDE Integration and Context Management: Storing the user's workspace context is key to copilot efficiency. Extensions like Cursor manage local syntax trees and file-change listeners to feed the model only the most relevant imports, optimizing cache hit ratios and reducing token overhead.
Theoretical Mechanics of the Coding Agent Index
The Coding Agent Index is not a simple benchmark aggregator; it represents a comprehensive evaluation of autonomous software engineering systems under complex execution environments. In classical computer science, code generation was treated as a single-turn token prediction task. Modern agentic systems, however, treat software engineering as a search and refinement problem over an execution graph. The index aggregates performance across three challenging dimensions: DeepSWE, Terminal-Bench v2, and SWE-Atlas-QnA. DeepSWE presents agents with real-world issues pulled from active production repositories, requiring them to localise bugs, edit files, and verify changes. Terminal-Bench v2 measures their capability in interactive shell environments, testing whether they can handle long-lived terminal sessions, parse stdout, and recover from failing compilation steps. Finally, SWE-Atlas-QnA checks their architectural comprehension by querying them on repository-wide design patterns. Performance is measured using pass@1 accuracy over multiple stochastic runs, demonstrating the system's baseline reliability when faced with non-deterministic environments.
- Repository-Scale Bug Localization: The capability to digest a repository's structural schema, trace imports, and locate the exact file and lines responsible for a bug without exhausting the context window.
- Interactive Terminal Feedback Loops: Tracing stdout/stderr outputs from compiler runs, linters, and test suites, and dynamically mapping these outputs to corresponding file-editing commands to correct errors.
- Deterministic Output Verification: Running unit tests locally within a sandboxed virtual container to prove the functional correctness of edits before generating a pull request or patch file.
Harness Architecture & Execution Sandboxing
In the theory of autonomous agents, a 'harness' represents the interface boundary between the core model (the planner) and the external operating system (the environment). The model itself is fundamentally a token predictor that cannot directly run commands. The harness intercepts model actions (formatted as special tool calls or JSON schemas), translates them into system calls (e.g., executing commands via bash, editing file lines, or querying directories), and feeds the system response back into the model's context window. To prevent infinite loops and destructive actions, the harness must execute inside a strictly isolated, sandboxed virtual machine or Docker container with limited resources, CPU throttling, and network policies. The core logic of the harness maintains the agent's memory loop (storing tool execution history, current file-system changes, and the git state), allowing the agent to backtrack when a planned sequence of actions leads to build failures or test regressions.
- Stateful Memory Tracking: Maintaining the delta of file changes, active terminal processes, and environment variables across multiple asynchronous steps in the planning loop.
- Sandboxed Execution Isolation: Running arbitrary user-space binaries and tests in restricted environments to protect the host machine from security risks and runtime side-effects.
- Self-Correction & Exception Handling: Parsing stack traces, compile errors, and runtime crashes, and appending them to the prompt history as structured logs to guide the next iteration.
Frequently Asked Questions
How do CLI-based agents differ from IDE-based copilots in execution theory?
IDE-based copilots (e.g., Cursor) are designed for low-latency, inline code completions and interactive editing, relying heavily on active editor context. CLI-based agents (e.g., Claude Code, Devin) operate as autonomous task runners; they receive a high-level goal, execute commands, run tests, and search files inside a sandbox, operating independently until the goal is accomplished or blocked.
Why does cost per task scale quadratically with context length in agentic loops?
Most transformer architectures rely on dense attention mechanisms where memory and compute requirements scale quadratically (O(N^2)) with token length. In agentic loops, every tool execution, shell output, and file edit is appended to the context. As the context grows, the token count of each successive turn increases, causing costs to compound rapidly.