Three terminal-based AI coding agents crossed a combined 440,000 GitHub stars by late September 2026, and each one is betting on a different theory of how developers should work with AI. OpenCode, built by SST, refuses to lock you into one model provider. OpenAI’s Codex CLI bets everything on tight integration with GPT-6 Sol, Luna, and Astra. Google’s Gemini CLI leans on a 1-million-token context window and account-level free access. This comparison breaks down the specs, pricing, benchmarks, and real installation commands for all three, so you can pick the right one instead of guessing.
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Three Terminal Agents, One Very Different Bet
A year ago, most developers who wanted an AI coding agent picked between a handful of IDE plugins. By September 2026, the center of gravity has shifted to the terminal. OpenCode, OpenAI Codex CLI, and Google Gemini CLI now run inside a shell, read a repository directly, execute shell commands, edit multiple files in one pass, and connect to external tools through the Model Context Protocol (MCP). Search interest reflects the shift: DataForSEO tracked “ai coding agent” search volume climbing 83% month over month and 1,710% year over year as of August 2026, with low keyword competition, suggesting demand is outpacing the content answering it.
The three tools are not interchangeable. OpenCode is a provider-agnostic harness. Point it at Claude, GPT, Gemini, GLM, or a model running on your own GPU, and it will drive whichever one you choose. Codex CLI and Gemini CLI take the opposite approach: each is built around its own company’s models, with sandboxing, pricing, and feature rollouts tied to that single provider’s roadmap. That single design decision explains almost every other difference in this comparison, from pricing structure to who should actually install each one.
What OpenCode, Codex CLI, and Gemini CLI Actually Are
OpenCode: The Provider-Agnostic Harness
OpenCode is an open-source terminal coding agent maintained by SST under the MIT license. Its core pitch is flexibility: instead of shipping its own model, it acts as a client that can talk to Claude, GPT, Gemini, GLM, or any OpenAI-compatible local endpoint. That makes it the closest thing to a neutral battleground for testing which model actually writes better code for your specific repository, without switching tools every time you want to compare. The project ships a terminal interface and a separate desktop application, and its release cadence is fast enough that independent trackers reported it moving past version 2.0 by late September 2026, even as its official GitHub releases page still listed older point releases from several months earlier. That mismatch is common for fast-moving open-source projects that tag frequent internal builds.
Codex CLI: OpenAI’s Native Terminal Agent
Codex CLI is OpenAI’s own lightweight coding agent, distributed under Apache-2.0 and installed through npm, Homebrew, or a direct install script from chatgpt.com. It ships as a companion to ChatGPT’s paid plans and the OpenAI API, and its release notes move fast: the GitHub repository listed version 0.157.1 as latest on September 26, 2026, up from 0.145.0 just two months earlier. That release added support for GPT-6 Sol and GPT-6 Luna, plus Amazon Bedrock as an alternate hosting backend, along with migration prompts nudging users off older models. Codex is built around sandboxed command execution with approval gates, meaning the agent asks before it runs anything destructive by default.
Gemini CLI: Google’s Free-Tier-First Agent
Gemini CLI is Google’s open-source terminal agent, also Apache-2.0 licensed, built around the Gemini 3 model family. Its headline feature is a context window that stretches up to 1 million tokens on the free tier when authenticated with a personal Google account, a scale that lets it ingest entire mid-size codebases in a single pass. Google ships new stable releases on a weekly cadence, publishing them every Tuesday at 20:00 UTC, and version 0.61.0 landed as the latest stable build on September 23, 2026. The most recent updates emphasized security: preserved versioned Flash model IDs, stronger defenses against indirect prompt injection, and tighter filesystem isolation for the sandbox.
Specs at a Glance: 13-Row Comparison Table
Here is how the three tools stack up across the specs that actually change day-to-day usage, pulled directly from each project’s GitHub repository and official documentation as of late September 2026.
| Spec | OpenCode | Codex CLI | Gemini CLI |
|---|---|---|---|
| Maintainer | SST (open community) | OpenAI | |
| License | MIT | Apache-2.0 | Apache-2.0 |
| GitHub stars (late Sept 2026) | ~210,600 | ~126,900 | ~107,200 |
| Latest stable release | Fast-moving; past v2.0 per third-party trackers | v0.157.1 (Sept 26, 2026) | v0.61.0 (Sept 23, 2026) |
| Model backend | Provider-agnostic (Claude, GPT, Gemini, GLM, local) | OpenAI-native (GPT-6 Sol, Luna, Astra) + Bedrock | Google Gemini 3 family |
| Max context window | Depends on the model you connect | Depends on the selected GPT-6 model | Up to 1M tokens on free tier |
| Free tier | Free software; pay only the provider you choose | Free CLI; limited access via ChatGPT plans | 60 requests/min, 1,000 requests/day (personal account) |
| MCP support | Yes | Yes | Yes |
| Sandboxing | Provider/config-dependent | Built-in, approval-gated command execution | Built-in, plus indirect prompt-injection defenses |
| Plan mode | Yes | Yes | Yes |
| Subagent workflows | Supported in recent releases | Supported in newer releases | Supported via task decomposition |
| Desktop app | Yes, in addition to terminal | Terminal only | Terminal only |
| Install methods | curl script, npm, brew, scoop, choco, pacman, nix | npm, brew, curl/PowerShell script | npx, npm, brew, MacPorts, conda |
The row that matters most for most teams is model backend. If your organization already standardized on Claude for other tools, OpenCode is the only one of the three that plugs into it natively. If you are already paying for ChatGPT Plus or Pro, Codex CLI rides on that same entitlement. If your infrastructure lives on Google Cloud, Gemini CLI’s free-tier rate limits and context window are hard to beat for large-repo work.
GitHub Stars and Momentum: Who’s Actually Winning Adoption
Star counts are a blunt instrument, but they track roughly with developer attention, and the gap here is wide. OpenCode’s repository showed 210,600 stars when checked in late September 2026, ahead of Codex CLI’s 126,900 and Gemini CLI’s 107,200. That puts OpenCode roughly 66% ahead of Codex CLI and nearly double Gemini CLI on raw star count. Part of that gap is structural: OpenCode has been open-source since its earliest releases and benefits from every developer who wants Claude access without paying for a separate Anthropic-specific tool, while Codex CLI and Gemini CLI only attract users who are already inside the OpenAI or Google ecosystem.
Commit velocity tells a similar story. OpenAI’s Codex repository passed 11,400 commits by September 26, 2026, according to a review published by AY Automate, with releases shipping roughly every week and a half. Google ships Gemini CLI on a fixed weekly cadence. OpenCode’s release history is less predictable but arguably faster, with the version-numbering jump from roughly 1.18 to 2.0 happening within a single day in a September 2026 snapshot tracked by Morph. None of the three projects has slowed down, and all three shipped meaningful updates in the same week this article was researched.
Benchmark Results: What the Data (and Its Gaps) Show
No single benchmark run has tested all three agents side by side under identical conditions, so this section pulls from three independent sources instead of pretending one clean number exists.
The first source is the Artificial Analysis Coding Agent Index v1.5, published September 22, 2026 and tracked by outlets including AI/TLDR. It scored Claude Code paired with Claude Fable 5.1 at 62.2, Devin’s Fusion CLI at 61.7, Codex CLI paired with GPT-6 Astra at 61.6, and OpenCode paired with GLM-5.3 at 53.6. Gemini CLI did not appear in that particular index run, which is itself a data point. Independent benchmark trackers have been slower to include it than the two US-based agents. Read the OpenCode score carefully, since it reflects one specific model pairing (GLM-5.3) rather than OpenCode’s ceiling. Swap in Claude or a top-tier GPT model through OpenCode and the score would track that model’s own benchmark, not the 53.6 figure.
The second source is each vendor’s own GitHub release documentation, which functions as a practical benchmark of shipping velocity and security hardening rather than raw coding accuracy. Gemini CLI’s September 2026 changelog specifically called out tighter filesystem isolation and stronger indirect prompt-injection protection, a category of benchmark that matters more for teams running the agent against untrusted repositories or web content pulled in through MCP tools.
The third source is Terminal-Bench, a benchmark suite specifically built for testing terminal-native agents on multi-step command-line tasks. Terminal-Bench 4.0 launched August 28, 2026, making it new enough that public, apples-to-apples scores for OpenCode, Codex CLI, and Gemini CLI were not yet consistently published across independent sites at the time of writing. Treat any single-source Terminal-Bench number you see for these three tools with the same caution you’d apply to a benchmark run by the vendor itself, and prefer results that disclose the exact model and configuration tested.
Pricing and Cost Breakdown
All three CLIs are free to download and install. The real cost shows up in the model usage behind them, and that’s where the pricing structures diverge sharply.
| Tool | Software cost | Consumer plan options | Free-tier limits | What drives your bill |
|---|---|---|---|---|
| OpenCode | Free, open source | None. You bring your own provider account | Whatever your chosen provider offers (including $0 for local models) | The model you plug in: Claude, GPT, Gemini, GLM, or local compute |
| Codex CLI | Free, open source | ChatGPT Go, Plus, Pro, Business, Enterprise, Edu tiers, or direct API billing | Limited usage on entry-level plans; quotas vary by tier | GPT-6 Sol, Luna, or Astra token consumption per task |
| Gemini CLI | Free, open source | Free personal-account tier, or Google Cloud/API billing | 60 requests/min, 1,000 requests/day on personal accounts | Gemini 3 model usage once you exceed free-tier caps |
Codex CLI’s paid plans, tracked by AY Automate, start with a Go tier around $8 per month, a Plus tier around $20, and a Pro tier around $100, each unlocking higher Codex usage alongside the broader ChatGPT product. On top of the flat subscription, OpenAI’s September 22, 2026 rollout of GPT-6 Sol and GPT-6 Luna to Plus, Pro, Business, Enterprise, and Edu accounts came with a credit-based cost structure for heavier tasks, and OpenAI has said GPT-6 Sol and Luna cost roughly half of GPT-5.6 Sol and Luna on input tokens, meaning the newest models are also the cheaper ones to run inside Codex.
Gemini CLI’s free tier is generous on paper: 1,000 requests a day is enough for most solo developers and small teams to never touch a credit card. Once you exceed that, or once you need Google Cloud-managed API access for a team, billing shifts to Gemini’s standard token rates, and Google’s own documentation doesn’t publish a single flat consumer price table the way OpenAI does for Codex plans. OpenCode has no pricing tier of its own at all. Its total cost of ownership is entirely a function of which model you point it at, which means a developer running OpenCode against a local model can use it for genuinely $0, while a developer routing every request through a frontier model can spend more than either of the other two tools.
Model Support: Claude, GPT, Gemini, and Local Models
This is the section where OpenCode’s design philosophy pays off most visibly. Because it was built as a harness rather than a product wrapped around one model, OpenCode can route requests to Claude through Anthropic or a compatible gateway, to GPT models through OpenAI-compatible APIs, to Gemini through Google or a compatible provider, and to local models exposed through any OpenAI-compatible endpoint. That last option matters for teams with data residency requirements or codebases they don’t want leaving their own infrastructure. Swapping models inside OpenCode is a configuration change, not a tool migration.
Codex CLI is the opposite by design. It is built natively around OpenAI’s own models, and while OpenAI’s September 2026 release added Amazon Bedrock as an alternate hosting layer, that’s still an OpenAI-controlled distribution channel rather than a path to running Claude or Gemini inside Codex. If your team wants to A/B test Claude against GPT-6 on the same codebase, Codex CLI isn’t built for that comparison. Gemini CLI sits in the same single-provider category, built around Google’s Gemini 3 family with no native path to Claude or GPT models. Its strength is depth rather than breadth: full access to Gemini’s newest capabilities, including the 1-million-token context window, without waiting for a third-party integration layer to catch up.
MCP, Sandboxing, and Plan Mode Compared
Model Context Protocol support is table stakes across all three tools by late 2026, letting each agent connect to external data sources and tools, from ticketing systems to internal documentation, without custom integration code. Where they diverge is how carefully each one fences off what the agent can actually touch once it’s running.
Codex CLI’s sandboxing is arguably its most opinionated feature. Command execution runs inside a sandbox by default, and the agent pauses for explicit approval before taking actions that could modify files outside the expected scope or run potentially destructive shell commands. That design trades some autonomy for predictability, which matters more in shared or production-adjacent environments. Gemini CLI’s September 2026 updates pushed hardest on this front of any of the three, adding stronger protection against indirect prompt injection (attacks where malicious instructions are hidden inside a file, webpage, or tool output the agent reads) alongside tighter filesystem isolation. For teams whose agents ingest content from outside their own repository, that’s a meaningful differentiator.
OpenCode’s sandboxing behavior depends heavily on the provider and configuration you choose, since the harness itself doesn’t own the execution environment the way a single-vendor tool does. Plan mode, where the agent proposes a multi-step approach before touching any files, is available across all three, though the exact workflow (how much detail the plan shows, whether you can edit it before execution) varies by version and by which model is doing the planning in OpenCode’s case.
Subagents and Autonomous Workflows
Subagents, where a primary agent spins up specialized child agents to handle discrete parts of a task, have become a standard feature across all three tools’ newer releases, though the maturity level differs. Codex CLI’s subagent support ships alongside its broader agentic task execution, letting one Codex session delegate research or verification work to a secondary process while the primary agent continues editing. Gemini CLI implements similar behavior through its task decomposition tooling, breaking large requests into smaller units that can run with different context or permission scopes.
OpenCode’s subagent implementation inherits the flexibility of its provider-agnostic design: a subagent can run on a different model than its parent, which opens up workflows like using a cheaper, faster model for straightforward file edits while reserving a frontier model for architecture decisions or code review. That’s a genuinely distinct capability among the three, since Codex CLI and Gemini CLI subagents stay within their respective single-vendor model families. The tradeoff is complexity. Configuring multi-model subagent chains in OpenCode takes more setup than accepting the default behavior in a single-vendor tool.
Where These Three Fit in the Broader AI Coding Tools Market
OpenCode, Codex CLI, and Gemini CLI don’t compete in a vacuum. They sit alongside the wider set of AI coding tools tracked in our AI coding tools guide, and developers already weigh them against each other, and the boundaries between categories are blurrier than the marketing suggests. Editor-native assistants like the ones compared in our GitHub Copilot vs Cursor vs Claude Code breakdown live inside an IDE and lean on tight integration with a project’s open files. Terminal agents take the opposite approach, running headless against a whole repository and shell environment, which is why teams often run one of each rather than picking a single tool for everything.
The provider-agnostic model behind OpenCode also shows up in adjacent infrastructure. Cloud sandbox platforms compared in our E2B vs Modal vs Daytona piece exist precisely so agentic tools, including all three terminal agents in this comparison, have somewhere safe to execute code without touching a developer’s actual machine. Teams running Codex CLI or Gemini CLI at scale frequently wire one of those sandbox providers underneath the CLI for exactly that reason, since neither vendor’s own sandboxing was originally designed for high-volume, unattended automation.
On the model side, anyone connecting OpenCode to Claude should read through our Claude Fable 5.1 API setup guide before wiring up credentials, since the authentication steps differ from the consumer ChatGPT or Google account flows used by Codex CLI and Gemini CLI respectively. The same applies on the Google side: our Gemini 3.8 Flash API setup tutorial covers the API-key path that Gemini CLI users need once they outgrow the 1,000-request daily free tier. Teams that want to avoid picking a single model at all, routing requests across Claude, GPT, and Gemini automatically based on cost or availability, should also look at our guide to building a multi-model AI router with fallback logic, which solves a similar problem to OpenCode’s provider flexibility but at the infrastructure layer instead of inside a single CLI.
Managing Multiple Terminal Agents Across an Engineering Team
Rolling out any one of these three tools to a single developer is a five-minute decision. Rolling one out across a team of 20 or 200 engineers raises questions the individual comparison doesn’t answer. Cost visibility is the first one. Because OpenCode has no billing layer of its own, a team that lets every engineer choose their own model provider can end up with usage spread across five different vendor dashboards, making it hard to answer a simple question like “what did AI coding tools cost us last month.” Standardizing on a small, approved list of models inside OpenCode, even though the tool technically supports far more, keeps that reporting sane without giving up the flexibility that makes OpenCode worth adopting in the first place.
Codex CLI and Gemini CLI are easier to budget for precisely because they’re single-vendor. A team on ChatGPT Enterprise can see Codex usage inside the same admin console that already tracks every other OpenAI product the company uses, and a team on Google Workspace or Google Cloud gets the same visibility for Gemini CLI. That’s a real operational advantage that doesn’t show up in a feature-by-feature spec comparison, but it matters enormously once more than a handful of people are running an agent against production code every day.
Access control is the second question worth settling before a team-wide rollout. Codex CLI’s approval-gated sandbox and Gemini CLI’s filesystem isolation both give administrators a default posture that limits what an agent can do without a human confirming it, which is a safer starting point for a large team than OpenCode’s more configuration-dependent behavior. Teams that do standardize on OpenCode for its model flexibility should pair it with an external sandbox layer, rather than relying on default settings, before letting it run unsupervised against anything beyond a personal branch. None of this is a reason to avoid any of the three tools. It’s a reason to treat the rollout plan as seriously as the tool selection itself.
Installation Walkthrough for All Three
Each tool takes under five minutes to install from a terminal. Here are the official commands as published in each project’s repository.
# OpenCode: npm install (also works with bun, pnpm, yarn)
npm i -g opencode-ai@latest
# OpenCode: one-line install script
curl -fsSL https://opencode.ai/install | bash
# OpenCode: Homebrew (macOS/Linux)
brew install opencode# Codex CLI: npm install
npm install -g @openai/codex
# Codex CLI: Homebrew
brew install --cask codex
# Codex CLI: Mac/Linux install script
curl -fsSL https://chatgpt.com/codex/install.sh | sh# Gemini CLI: run without installing
npx @google/gemini-cli
# Gemini CLI: npm global install
npm install -g @google/gemini-cli
# Gemini CLI: Homebrew
brew install gemini-cliAfter installation, each tool prompts for authentication on first launch. OpenCode asks which provider you want to connect (Anthropic, OpenAI, Google, or a custom endpoint) and stores the credentials locally. Codex CLI signs in with your OpenAI account or an API key, matching whichever plan you’re on. Gemini CLI offers the fastest path to a working free tier: sign in with a personal Google account and you’re immediately inside the 60-requests-per-minute, 1,000-requests-per-day allowance with no credit card required. That zero-friction free tier is a real advantage for developers who want to try an agent today without committing to a paid plan first.
Real-World Use Cases: Who Should Pick What
Specs only tell part of the story. Here’s how the tradeoffs play out in scenarios teams actually run into.
- A solo developer bootstrapping a side project on a tight budget gets the most mileage from OpenCode wired to a local model through an OpenAI-compatible endpoint, avoiding token costs entirely while still getting multi-file editing and MCP support.
- An engineering team already on a ChatGPT Enterprise contract can roll out Codex CLI without negotiating a new vendor relationship, since usage rides on the same account structure already in place for the rest of the company.
- A team refactoring a large monorepo benefits from Gemini CLI’s 1-million-token context window, which can hold far more of a codebase in a single session than most competing context limits allow.
- A platform team building internal developer tooling that needs to pull from Jira, Confluence, or an internal API through MCP can wire any of the three agents into that infrastructure, but OpenCode’s flexibility lets different squads use their preferred model without splitting the tooling.
- A security-conscious team in a regulated industry working with code that ingests untrusted external content should weigh Gemini CLI’s September 2026 indirect prompt-injection hardening, or Codex CLI’s approval-gated sandbox, over configurations that grant broader default autonomy.
- An open-source maintainer comparing model quality for a specific language or framework can use OpenCode to swap between Claude, GPT, and Gemini on the same repository without reinstalling anything, turning what used to be a multi-tool evaluation into a single config change.
Migration Guide: Switching Between Agents
Moving from one terminal agent to another is lower-stakes than migrating a production service, but a few steps keep it from becoming a wasted afternoon.
- Audit your current setup first. Note which model provider, API keys, and MCP servers your existing agent uses before touching anything, so you can replicate the same access on the new tool.
- Install the new CLI alongside the old one. All three tools install cleanly side by side since they use separate binaries and config directories, so there’s no need to uninstall anything until you’re confident in the switch.
- Authenticate with the same provider account where possible. If you’re moving from Codex CLI to OpenCode, for example, you can point OpenCode at the same OpenAI API key rather than starting a new billing relationship.
- Re-register your MCP servers. Each tool stores MCP configuration separately, so external tool connections (ticketing systems, internal APIs, documentation search) need to be added again rather than carried over automatically.
- Test on a scratch branch, not your main workflow. Run the new agent through a low-stakes task, a documentation update or a small bug fix, before trusting it with anything that touches a shared branch.
- Compare plan-mode output before enabling full autonomy. Each tool’s plan mode surfaces its intended approach differently. Reviewing a handful of plans side by side before letting either agent execute unsupervised catches surprises early.
- Track cost for the first week. If you’re switching because of pricing, actually watch your provider’s usage dashboard rather than assuming the new tool is cheaper based on its sticker price alone, since agentic tasks with heavy tool-calling can burn tokens faster than expected regardless of which CLI is driving them.
- Roll out to the team only after the above checks pass. A single developer’s positive experience over a few days is a reasonable signal, but it’s not the same as validating the tool against your team’s actual repository size and workflow patterns.
Pros and Cons
OpenCode
Pros:
- Provider flexibility across Claude, GPT, Gemini, GLM, and local models
- MIT license with no vendor lock-in
- Largest GitHub star count of the three at roughly 210,600
- Desktop app in addition to the terminal client
- Can run at $0 cost when paired with a local model
Cons:
- No single owner, so sandboxing and safety behavior depend on your own configuration rather than a consistent built-in default
- Version numbering has been inconsistent between official releases and third-party trackers
- Setup requires more upfront decisions (which provider, which model, which credentials) than a single-vendor tool
Codex CLI
Pros:
- Tight integration with GPT-6 Sol, Luna, and Astra
- Approval-gated sandbox is a sensible default for teams new to autonomous agents
- Billing rides on existing ChatGPT plans for teams already paying for OpenAI products
- Fast release cadence, with more than 11,400 commits by late September 2026
Cons:
- No native path to Claude or Gemini models
- Consumer plan pricing (roughly $8 to $100 a month) adds cost on top of API usage for heavier workloads
- Approval gates that improve safety can also interrupt fully autonomous batch workflows
Gemini CLI
Pros:
- Generous free tier at 1,000 requests a day with no credit card required
- Context window up to 1 million tokens on that same free tier
- Strongest publicly documented security hardening of the three as of September 2026, including indirect prompt-injection defenses
- Weekly release cadence makes updates predictable
Cons:
- Smallest GitHub star count of the three at roughly 107,200
- No native access to Claude or GPT models
- Missing from at least one major third-party benchmark index (Artificial Analysis) as of late September 2026, making independent performance comparisons harder to find
- No simple flat consumer pricing table once you exceed free-tier limits
Limitations and Criticisms
None of these tools is a finished product, and the gaps mostly track back to how young the terminal-agent category still is. OpenCode’s biggest structural risk is that its usefulness depends entirely on the provider you connect. A misconfigured or under-powered model choice will make OpenCode look worse than either single-vendor competitor, even though the harness itself isn’t the bottleneck. Its rapid, sometimes inconsistent version numbering also makes it harder for teams to pin a specific release for reproducibility, a real concern for anyone trying to standardize a workflow across a team.
Codex CLI’s dependence on OpenAI’s own models and account entitlements is a limitation as much as a feature. Usage limits on the lower consumer tiers can be difficult to predict for genuinely long-running autonomous tasks, and the token consumption of agentic work, where the model repeatedly reads files, runs commands, and revises its plan, can grow faster than a simple per-message chat interaction would suggest. Migration prompts pushing users off older GPT models, introduced in the 0.157.0 release, are a reasonable practice but do mean configurations can’t be frozen indefinitely without active maintenance.
Gemini CLI’s main limitation is ecosystem breadth rather than capability. Teams that need Claude or GPT access alongside Gemini will need a second tool regardless of how strong Gemini CLI’s own security posture is. Its absence from the Artificial Analysis Coding Agent Index as of the September 22, 2026 snapshot also means prospective users have fewer independent, apples-to-apples benchmark numbers to lean on compared to Codex CLI, which does appear in that index.
The Verdict: Which Terminal Agent Wins in 2026
There isn’t a single winner here, because the three tools are answering different questions. If the question is “which agent gives me the most model choice for the lowest possible cost,” OpenCode wins on data: it’s MIT-licensed, free to run, and its 210,600 GitHub stars put it roughly 66% ahead of Codex CLI and nearly double Gemini CLI, a gap that reflects how many developers value not being locked into one company’s model roadmap.
If the question is “which agent should a team already paying for ChatGPT Enterprise standardize on,” Codex CLI wins by default, since it rides on existing billing and scored 61.6 on the Artificial Analysis Coding Agent Index when paired with GPT-6 Astra, the second-highest score among agents that index tracked in its September 22, 2026 run.
If the question is “which agent lets me try a serious coding assistant today for free, on a large codebase, with the strongest security posture,” Gemini CLI wins. A 1-million-token context window and 1,000 free requests a day, combined with the most recently documented indirect prompt-injection defenses of the three, make it the safest and cheapest entry point for teams that haven’t committed to a paid AI coding budget yet. The honest answer for most engineering organizations in late 2026 is to run more than one: OpenCode for flexibility and cost control, Codex CLI or Gemini CLI for whichever single-vendor ecosystem the rest of the company’s tooling already lives in.
Frequently Asked Questions
Is OpenCode actually free to use?
The OpenCode software itself is free and MIT-licensed. Your actual cost depends entirely on which model provider you connect to it. Wire it to a local model and it can run at $0. Wire it to a frontier API model and you’ll pay that provider’s standard token rates.
Can Codex CLI use Claude or Gemini models?
No. Codex CLI is built natively around OpenAI’s own GPT-6 model family, with Amazon Bedrock added in September 2026 as an alternate hosting layer, but that remains an OpenAI-controlled distribution channel rather than a route to running competing models inside the tool.
What’s the Gemini CLI free tier limit?
Signing in with a personal Google account gives you 60 requests per minute and 1,000 requests per day, with access to Gemini 3 models and up to a 1-million-token context window, all without entering payment details.
Which of the three has the most GitHub stars?
OpenCode, with roughly 210,600 stars as of late September 2026, ahead of Codex CLI’s 126,900 and Gemini CLI’s 107,200.
Do all three support MCP (Model Context Protocol)?
Yes. OpenCode, Codex CLI, and Gemini CLI all support MCP, letting each agent connect to external tools and data sources like ticketing systems, internal APIs, or documentation search without custom integration code.
Can I run OpenCode with a local, self-hosted model?
Yes, through any OpenAI-compatible local endpoint. This is one of the clearest differentiators between OpenCode and the other two tools, since neither Codex CLI nor Gemini CLI is designed around local-model hosting as a first-party workflow.
Which tool has the best sandboxing for untrusted code?
Based on public documentation as of September 2026, Gemini CLI has the most recently hardened security posture, with its latest release adding stronger indirect prompt-injection defenses and tighter filesystem isolation. Codex CLI’s approval-gated sandbox is also a strong default for teams that want a human checkpoint before any command executes.
Do I need to pick just one of these tools?
No. All three install independently without conflicting, and many teams run more than one, using OpenCode for flexible model comparisons alongside Codex CLI or Gemini CLI for whichever single-vendor ecosystem their broader toolchain already depends on.
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