Key Takeaways
- OpenCode leads all open-source coding agents with 172,198 GitHub stars as of mid-2026, ahead of Gemini CLI (105,104) and Codex CLI (89,991). It is MIT-licensed, costs nothing as software, and charges only whatever the model provider you connect charges per token.
- Codex CLI scores 83.4% on Terminal-Bench 2.1 when paired with GPT-5.5, the highest score among the three tools in this comparison. Gemini CLI with Gemini 3.1 Pro scores 70.7% on the same benchmark. OpenCode’s score varies by the model you connect.
- OpenCode connects to 75 or more model providers through Models.dev, including local Ollama models at zero inference cost. Built-in login for GitHub Copilot and ChatGPT Plus/Pro lets an existing subscription drive the agent without a separate API key.
- Gemini CLI’s free tier (60 requests per minute, 1,000 per day on a personal Google account) ended on June 18, 2026 for consumer users, replaced by Antigravity CLI. Developer access to the Gemini API free tier through Google AI Studio remains available.
- Codex CLI reached 2 million weekly active users by March 2026 and supports multi-agent concurrency: up to six parallel sub-agents with distinct roles (explorer, worker, reviewer, tester). Version 0.116.0 added enterprise features including custom CA certificate support and a hooks system.
- All three tools support MCP (Model Context Protocol) for connecting external tools, databases, and APIs. OpenCode adds a dual-agent architecture with a Plan agent (read-only, asks before acting) and a Build agent (reads and executes). Gemini CLI always shows commands and asks for permission before execution.
- For teams with an existing frontier API subscription (Claude, GPT, Gemini), OpenCode’s zero-agent-cost model makes it the most affordable path to a capable terminal coding agent. For teams already invested in the OpenAI ecosystem, Codex CLI’s benchmark performance and enterprise tooling justify staying on that stack.
Terminal coding agents matured quickly in 2025 and 2026. What started as lightweight shell wrappers became full agent harnesses with multi-step planning, parallel execution, MCP integrations, and direct file system access. The three tools in this comparison represent three different philosophies: OpenCode builds a provider-neutral open-source harness and lets you bring the model; Gemini CLI ships Google’s own model and focuses on context breadth and web integration; Codex CLI tightly integrates with OpenAI’s model family and optimizes for benchmark performance and enterprise adoption.
This comparison covers OpenCode, Gemini CLI, and Codex CLI across architecture, model support, performance, pricing, and the specific workflows where each has an advantage.
Quick Comparison: OpenCode vs Gemini CLI vs Codex CLI
| Factor | OpenCode | Gemini CLI | Codex CLI |
|---|---|---|---|
| GitHub stars | 172,198 | 105,104 | 89,991 |
| License | MIT | Apache 2.0 | Apache 2.0 |
| Agent cost | Free (pay model API only) | Free (pay Gemini API) | Free (pay OpenAI API) |
| Model support | 75+ providers + Ollama local | Gemini family (Gemini 3) | OpenAI family (GPT-5.x) |
| Terminal-Bench 2.1 | Varies by model | 70.7% (Gemini 3.1 Pro) | 83.4% (GPT-5.5) |
| Context window | Depends on model | 1M tokens (Gemini 3) | Depends on model |
| Multi-agent | Plan + Build dual agents | Single agent | Up to 6 parallel sub-agents |
| MCP support | Yes | Yes | Yes |
| Local model support | Yes (Ollama) | No | No |
| Desktop app | Beta (macOS, Windows, Linux) | No | No |
What Is OpenCode?
OpenCode is an open-source, MIT-licensed terminal coding agent built as a provider-neutral harness. It connects to 75 or more model providers through Models.dev, accepts local models through Ollama, and adds built-in authentication for GitHub Copilot and ChatGPT Plus/Pro so developers with existing subscriptions can route them directly. The agent costs nothing; the only charges are whatever the model provider charges per token.
OpenCode ships as a terminal TUI (text user interface) with a desktop app in beta across macOS, Windows, and Linux. There is no VS Code extension, no JetBrains plugin, and no web UI by design. The architecture prioritizes terminal-first workflows with optional desktop access through a share link mechanism that lets multiple sessions coexist.
The dual-agent architecture is one of OpenCode’s most practical features. The Plan agent is read-only: it inspects the codebase, asks clarifying questions, and produces a plan before touching anything. The Build agent executes changes, runs commands, and modifies files once you approve the plan. This separation reduces surprise edits in unfamiliar codebases. OpenCode also supports MCP integrations for connecting external tools, databases, and APIs, and includes session management so long-running tasks can be paused and resumed.
By mid-2026, OpenCode had accumulated 172,198 GitHub stars, the highest count of any open-source coding agent, with releases shipping roughly every few days. An optional OpenCode Go subscription provides flat-rate access to a curated set of hosted open models for developers who do not want to manage individual provider API keys.
What Is Gemini CLI?
Gemini CLI is Google’s open-source terminal agent, released under Apache 2.0 and built around the Gemini model family. Google open-sourced Gemini CLI in mid-2025. By mid-2026 it had accumulated 105,104 GitHub stars. The tool ships the Gemini 3 model with a 1 million token context window, which is the largest native context of the three tools in this comparison when using the hosted Gemini 3 model rather than a smaller variant.
Gemini CLI operates as an agentic assistant in the terminal. It reads and writes files, greps across the project tree, runs shell commands, and fetches web pages. In agent mode it can run commands from git, npm, kubectl, and similar CLI tools. The tool always shows you the proposed command and requires explicit permission before execution, a safety-first default that is less configurable than OpenCode or Codex CLI.
Gemini CLI includes full MCP support for connecting external tools, GitHub integrations, Google Search, databases, and custom APIs. Its 1 million token context window means it can ingest an entire monorepo in a single context, which is particularly useful for cross-file refactoring and large-scale code searches that would overflow smaller context windows.
The free tier that gave personal Google account users 60 requests per minute and 1,000 per day ended for consumer users on June 18, 2026, replaced by Antigravity CLI on the consumer side. Developer access to Gemini API through Google AI Studio maintains a separate free tier for API usage.
What Is Codex CLI?
Codex CLI is OpenAI’s open-source, terminal-first coding agent, released under Apache 2.0. It runs directly in the shell, reads an existing codebase, proposes multi-file changes, and executes commands in a sandboxed environment. By March 2026 it had reached 2 million weekly active users and 89,991 GitHub stars.
The headline performance result for Codex CLI is 83.4% on Terminal-Bench 2.1 when paired with GPT-5.5, the highest benchmark score among the three tools in this comparison. Multi-agent concurrency is Codex CLI’s most technically distinctive feature: it supports up to six parallel sub-agents with distinct roles (explorer, worker, reviewer, tester), which allows complex codebases to be processed in parallel streams rather than sequentially.
Version 0.116.0 (released March 19, 2026) expanded Codex CLI’s enterprise capabilities: custom CA certificate support for corporate proxies, a hooks system including a user prompt hook, CI-friendly sandbox configurations, remote test workflows, and a Python SDK for programmatic access. A second variant called Spark was introduced in early 2026 for latency-critical workflows, co-engineered with Cerebras on the WSE-3 wafer-scale chip.
Codex CLI’s main constraint is model lock-in. Unlike OpenCode: it is tightly coupled to the OpenAI model family. Teams that want to use Claude, Gemini, or local models cannot do so through Codex CLI without workarounds. For teams already using GPT models across their stack: this is not a practical limitation. For teams that want model flexibility: it is a significant one.
OpenCode vs Gemini CLI vs Codex CLI: Feature-by-Feature Breakdown
Model Flexibility
OpenCode is the clear leader on model flexibility. Supporting 75 or more providers through Models.dev, plus Ollama for local models, means a developer can run the same agent harness against Claude Sonnet 5, DeepSeek V4 Flash, Mistral, Llama 4, or a locally quantized model without changing tools. For cost optimization, this matters: swapping from a frontier API to a local model drops inference cost to zero while keeping the agent interface identical.
Gemini CLI is locked to the Gemini family, and Codex CLI is locked to the OpenAI family. Both choices simplify setup (one API key, one pricing relationship) at the cost of portability. Teams with a strong preference for one model provider may find the single-provider tools more convenient; teams that benchmark models or want to hedge against pricing changes will prefer OpenCode’s flexibility.
Benchmark Performance
On Terminal-Bench 2.1, the clearest available benchmark for terminal coding agents, Codex CLI with GPT-5.5 scores 83.4%, the strongest result of the three. Gemini CLI with Gemini 3.1 Pro scores 70.7%, a 12.7-point gap. OpenCode’s score is not published as a standalone number because it depends on which model you connect: pair OpenCode with Claude Opus 4.8 and it operates at roughly Claude Code’s benchmark level; pair it with a local model and performance drops substantially.
The benchmark gap between Codex CLI and Gemini CLI is meaningful for complex agentic tasks. For straightforward coding tasks (adding a function, writing a test, fixing a specific bug), the gap is less practically significant because both tools complete them reliably. The gap shows up most on tasks requiring multi-step reasoning, codebase navigation, and unsupervised execution over longer task horizons.
Context Window
Gemini CLI ships the Gemini 3 model’s 1 million token context window natively. This is the single largest context available among the three tools by default. For projects where context breadth is the primary constraint (large monorepos, extensive documentation, multi-file cross-references), Gemini CLI has a structural advantage that does not depend on benchmark scores.
OpenCode and Codex CLI both depend on the context window of the model they are using. OpenCode connected to Gemini 3 or Claude’s 1M context models gets equivalent context; connected to a smaller model, it gets less. Codex CLI connected to GPT-5.6 Sol gets that model’s context window.
Parallel and Multi-Agent Execution
Codex CLI’s support for up to six parallel sub-agents with distinct roles (explorer, worker, reviewer, tester) is the most sophisticated multi-agent architecture of the three. For large codebases where different agents can work on separate components simultaneously, this reduces wall-clock time on complex tasks. The reviewer and tester roles add built-in quality checks that single-agent tools lack by default.
OpenCode’s Plan/Build dual-agent structure is simpler but practically useful: separating read-only planning from execution-capable building reduces the risk of unintended changes on first contact with an unfamiliar codebase. Gemini CLI operates as a single agent that requires explicit permission before each command execution, a safer default for developers who want fine-grained control over every action.
Pricing
All three tools are free as software. The cost difference is in model inference. OpenCode users pay whatever their chosen model provider charges, with the option to go to zero by using local Ollama models. Gemini CLI users pay Google’s Gemini API rates (or get free tier access through Google AI Studio). Codex CLI users pay OpenAI’s API rates for GPT models.
For a developer running moderate daily usage: connecting OpenCode to DeepSeek V4 Flash at $0.28 per million output tokens would cost a fraction of connecting it to GPT-5.6 Sol. Gemini CLI’s free developer tier through Google AI Studio makes it the cheapest managed option for moderate volumes. Codex CLI’s inclusion with certain paid OpenAI plans reduces effective marginal cost for existing subscribers.
Enterprise and CI/CD Readiness
Codex CLI leads on enterprise readiness in 2026. Version 0.116.0 added custom CA certificate support for corporate proxies, a hooks system for pipeline integration, CI-friendly sandbox configurations, remote test workflows, and a Python SDK. These features directly address the requirements of engineering teams running CI/CD pipelines, security reviews, and corporate network constraints.
OpenCode’s MCP support and session management make it deployable in team workflows, but dedicated enterprise features (audit logging, SSO, policy controls) are less developed than Codex CLI’s as of mid-2026. Gemini CLI’s enterprise readiness sits between the two: MCP support and a large context window are team-friendly, but purpose-built enterprise controls are not as mature.
Who Should Use Which?
OpenCode suits developers who want model flexibility, do not want vendor lock-in, and are comfortable managing API keys across providers. It is the best choice for teams already paying for a frontier model subscription (Claude, GPT, Gemini) who want to use that model in the terminal without paying a second agent fee. Teams with GPU infrastructure who want zero-cost inference on local models get the most from OpenCode’s Ollama integration.
Gemini CLI suits developers already working within the Google ecosystem who want the largest default context window and a familiar permission-before-execution safety model. After the end of the consumer free tier, its cost profile depends on Google AI Studio or Gemini API pricing, which is competitive but not free for sustained use.
Codex CLI suits teams already invested in the OpenAI stack who need the highest available benchmark performance, parallel multi-agent execution on complex codebases, and enterprise features for corporate deployment. Its 83.4% Terminal-Bench 2.1 score makes it the strongest performer of the three when paired with GPT-5.5, and its enterprise tooling in v0.116.0 makes it the most deployment-ready for engineering teams with corporate compliance requirements.
Verdict
OpenCode wins on breadth and flexibility. Codex CLI wins on benchmark performance and enterprise tooling. Gemini CLI wins on context window and Google ecosystem integration. None of the three is universally better because they serve different developer profiles and workflow priorities.
The practical recommendation for most individual developers is OpenCode: zero agent cost, model flexibility, and the ability to switch providers without changing tools covers the most ground. For engineering teams deploying a standardized agent across a development organization with corporate security requirements, Codex CLI’s enterprise features in v0.116.0 make the stronger case. For context-heavy workflows in the Google ecosystem, Gemini CLI’s 1M token window remains a concrete advantage.
Frequently Asked Questions
Is OpenCode really free?
OpenCode is free as software under the MIT license. You pay only the model provider you connect to it. If you use a local Ollama model, inference cost is zero. If you connect to Claude, GPT, or Gemini API, you pay that provider’s standard per-token rates with no markup from OpenCode. An optional OpenCode Go subscription provides flat-rate access to a curated set of hosted open models for developers who prefer not to manage individual provider keys.
What ended Gemini CLI’s free tier?
The consumer free tier giving personal Google account users 60 requests per minute and 1,000 per day ended June 18, 2026. Google replaced it with Antigravity CLI on the consumer side. Developer access through Google AI Studio and the Gemini API maintains a separate free tier for API usage, so developers building on the Gemini API can still access free quota through that channel rather than the consumer Gemini CLI endpoint.
How does Codex CLI’s multi-agent feature work?
Codex CLI supports up to six parallel sub-agents running simultaneously, each with a distinct role: explorer (navigates the codebase), worker (makes changes), reviewer (checks correctness), and tester (runs tests). For complex tasks spanning multiple files or modules, parallel sub-agents reduce total execution time by working on separate components concurrently rather than sequentially. This feature is particularly useful for large refactoring tasks, test suite generation across multiple modules, and codebase migrations.
Can OpenCode use local models?
Yes. OpenCode integrates with Ollama, which allows running locally hosted models on your own hardware. This means inference cost is zero when using a local model, and no data leaves your machine. The trade-off is that local model quality is generally below frontier API models, and you need sufficient hardware (GPU RAM) to run the model. OpenCode can switch between local and hosted models without configuration changes beyond updating the model selection.
Which tool scores highest on Terminal-Bench 2.1?
Codex CLI paired with GPT-5.5 scores 83.4% on Terminal-Bench 2.1, the highest of the three tools in this comparison. For reference, Claude Code paired with Opus 4.8 scores 78.9% on the same benchmark, and Gemini CLI paired with Gemini 3.1 Pro scores 70.7%. OpenCode does not have a standalone published score because performance depends on the model connected to it.
What is the OpenCode dual-agent (Plan/Build) system?
OpenCode ships two agent modes. The Plan agent is read-only: it inspects files, asks clarifying questions, and produces a step-by-step plan without modifying anything. The Build agent executes changes, creates and modifies files, and runs commands once you approve the plan. Using Plan mode before Build mode reduces unintended edits, particularly when working on an unfamiliar codebase for the first time. Both agents support MCP integrations for external tool access.
Does Gemini CLI work with non-Gemini models?
No. Gemini CLI is built around the Gemini model family and does not natively support other model providers. To use Claude, GPT, or local models in the terminal, you would need OpenCode or a similar provider-agnostic agent. Gemini CLI’s advantage is the depth of integration with the Gemini 3 model, including the 1 million token context window, which would not be available if it supported arbitrary providers.
