Three tools, three different philosophies. Claude Code (Anthropic), Codex CLI (OpenAI), and OpenCode (independent, multi-provider) have each staked out different ground in the AI coding CLI space. This is a practical guide to choosing between them — and an argument for using all three strategically.
Architecture Differences
Claude Code
Claude Code is tightly integrated with Anthropic's model family. It runs as a persistent session in your terminal, reads your project context on startup, and has deep support for multi-turn conversations. Its killer feature is the MCP (Model Context Protocol) ecosystem — tools that let the model self-serve information about your project without manual copy-paste.
The session model means Claude Code accumulates context across a work session. This is excellent for large refactors but means you need to manage context window usage carefully on long sessions.
Codex CLI
Codex CLI (OpenAI's tool, not the older Codex model) takes a more task-oriented approach. Each invocation is a discrete codex exec "task" command that runs, produces output, and exits. This stateless model is better for scripting and CI/CD pipelines where you want predictable, isolated executions.
Codex also supports multi-agent orchestration natively — you can spawn parallel Codex instances on different subtasks and collect results. This makes it a natural fit for swarm delegation workflows.
OpenCode
OpenCode is model-agnostic by design. It connects to Anthropic, OpenAI, Google, and its own opencode-go inference tier through a unified interface. The same prompt can run against Claude Sonnet 4.6 or Kimi K2.5 with a single flag change.
This makes OpenCode ideal for cost optimization — route cheap tasks to cheaper models — and for teams that want to avoid vendor lock-in. The trade-off is that model-specific features (like Claude's tool use or Codex's workspace isolation) aren't always fully surfaced.
Cost Comparison
| Tool | Model | Cost tier | Best for |
|---|---|---|---|
| claude | Sonnet 4.6 / Opus 4.6 | $$$ | Architecture, long sessions |
| codex | GPT-5.4 / GPT-5.3-codex | $$$ | Scripted tasks, CI/CD, swarm |
| opencode | Mimo v2 Omni (opencode-go) | $ | Fast exploration, cost-sensitive |
| opencode | Qwen 3.6+ Free | Free | Throwaway tasks, experiments |
When to Use Each
Use Claude Code when…
- You're doing deep refactoring across many files
- You need MCP tools (project health checks, swarm status, custom tools)
- The task requires multi-turn reasoning with accumulated context
- You want skills and slash commands for structured workflows
Use Codex when…
- You're scripting repeatable tasks in CI/CD
- You want parallel execution across multiple subtasks
- You need workspace isolation (Codex runs in a sandboxed environment)
- The task is well-defined and benefits from a stateless, predictable execution
Use OpenCode when…
- Cost is a constraint — route to the cheapest capable model
- You want to experiment with non-Anthropic/OpenAI models
- You need a quick one-shot generation without starting a full session
- You're benchmarking models against each other
The Case for Using All Three
The real answer is that these tools are complementary, not competitors. A mature AI development workflow uses each tool where it excels.
- Claude Code for the main interactive session — architecture decisions, code review, complex edits
- Codex as a swarm worker — run 3-5 parallel Codex instances on independent subtasks while Claude Code manages the session
- OpenCodefor cheap exploration — spike a solution with Mimo v2 Omni before committing Claude's context to it
0dai init in your project to generate configs for all five CLI tools with a consistent persona, a shared swarm queue, and automatic cost-aware routing.# Install and initialize
npm install -g @0dai-dev/cli
cd your-project
0dai init
# See delegation policy
cat ai/docs/delegation-policy.mdBottom Line
If you're only using one AI CLI, you're leaving cost efficiency and throughput on the table. Claude Code wins on reasoning depth, Codex wins on parallelism, OpenCode wins on cost per token. Use the right tool for the right task — and let a shared config layer keep all three in sync.