OpenCode vs Claude Code: A Same-Model Comparison

#Why the usual OpenCode vs Claude Code comparison is broken

Nearly every "opencode vs claude code" comparison runs each tool on its own default model. That tangles harness quality with model quality, and the verdict ends up untestable.

What you're really choosing between is the harness and its business model, not raw model IQ. Both are terminal-first agentic coding tools that read a codebase, edit files, and run commands. Where they diverge is how they plan and manage context, which models and providers they reach, and how you pay.

The fix is a control. Pin both harnesses to the same callable model so only harness behavior differs, then compare the complete products on native defaults. Both can run one catalog model (glm-5.2) through the Fast CLI, which no ranking competitor does.

#Quick verdict by user type

It comes down to one question: do you want the deepest Anthropic integration, or the freedom to point at any provider?

#Choose Claude Code if you want the deepest integration

Pick Claude Code for Anthropic-first depth across terminal, VS Code, JetBrains, a desktop app, and the web. It ships parallel subagents, hooks, packaged skills, plan mode, scheduled routines, and an Agent SDK, and CLAUDE.md files, settings, and MCP servers carry across every surface. It is metered against a Claude subscription (Pro/Max) or an Anthropic Console account.

#Choose OpenCode if you want provider freedom

Pick OpenCode for an open-source, model-agnostic harness. Maintained by Anomaly, it reaches 75+ LLM providers plus local models and two first-party gateways. It has Plan and Build modes (Tab to switch), /undo and /redo, /share links, LSP integration, MCP, plugins, and an SDK.

#What claude code vs opencode actually compares: model, harness, provider, meter

OpenCode vs Claude Code is four separate variables that most comparisons collapse into one: the harness (planning, tools, context, permissions, UI), the model (the LLM, such as glm-5.2 or Claude Sonnet 5), the provider (who serves the model), and the meter (a native subscription versus a per-token balance).

Figure 1: Harness, model, provider, and meter are four separate variables.

First decide which model both should run, since you can choose the catalog model both harnesses run. That leaves the harness as the only variable.

Last verified: August 22, 2026.

#Dual-method benchmark: same Fast model, then native defaults

Run two lanes. Lane A isolates the harness: pin both harnesses to glm-5.2 through Fast, so model, provider, and meter are identical and only harness behavior differs. Lane B compares the products: run each on its native default and disclose the model, provider, and meter it uses. You bring the task, starting commit, and acceptance test. The method brings the rig and the metrics, not result numbers.

Install and authenticate the Fast CLI once, then reuse it across both adapters (install-and-authenticate steps).

# 1. Install the Fast CLI (use the package manager you already have)
npm install -g @inference/fast
# or: pnpm add -g @inference/fast
# or: bun add -g @inference/fast
# or: yarn global add @inference/fast

# 2. Sign in through the browser approval flow.
#    This selects the first project in your personal workspace and creates one
#    machine-scoped key on first agent connection (never printed, never passed
#    as an argument).
fast login

# 3. Confirm the account, project, machine key, endpoints, and agent states.
fast status

For Lane A, fast claude on --model glm-5.2 maps all six Claude Code slots to one model, and fast opencode on --model glm-5.2 registers the inference-net provider as default and small model. Restart each harness after routing and confirm the request in the usage dashboard. Setup guides: route Claude Code and register OpenCode's provider.

# Lane A (Claude Code): pin the harness to the shared model.
# Maps all six Claude Code slots to one callable catalog model, writes
# ~/.claude/settings.json, and snapshots the original under ~/.inference-fast/backups.
fast claude on --model glm-5.2

# Confirm the route before you restart.
fast claude status

# Restart Claude Code, then run your task and its acceptance test.
# (Restart is required for the new settings to take effect.)

# Verify the request lands under Usage with the selected model:
#   https://fast.inference.net/dashboard/usage

# Restore the original settings.json byte-for-byte and remove the backup.
# Restart Claude Code again after restoring.
fast claude off

# Lane A (OpenCode): pin the harness to the same shared model.
# Registers Fast Inference as the OpenAI-compatible `inference-net` provider in
# ~/.config/opencode/opencode.json, sets the model as default and small model,
# and preserves unrelated providers/settings. The original file is snapshotted.
fast opencode on --model glm-5.2

# Confirm the route before you restart.
fast opencode status

# Restart OpenCode, select the inference-net model, then run the SAME task and
# acceptance test you ran in Claude Code.

# Verify the request lands under Usage with the selected model:
#   https://fast.inference.net/dashboard/usage

# Restore the original opencode.json byte-for-byte. Restart OpenCode after.
fast opencode off

The scorecard below defines what to record per task; setup and config time count as part of the cost.

Methodology template — record your own values; no result numbers are supplied.

#Results, usage, pricing, and total cost per successful task

Cost per successful task is the one denominator that survives model and meter differences. Compute it per harness from your own scorecard:

(input_tokens × input_rate + output_tokens × output_rate) ÷ successful_tasks

Supply your token counts and success count; anchor the same-model lane on the dated GLM 5.2 rate below so both harnesses price apples-to-apples.

Source: OpenCode Zen. Zen auto-reloads $20 when the balance drops below $5. Last verified: August 22, 2026.

The meters are genuinely different billing models and must not be conflated; the table below lists all six. The Fast route reads against the separate per-token balance; check the listed model rates for cost per task.

Last verified: August 22, 2026.

Setup burden counts as part of total cost. OpenCode's 75+ providers are real freedom, but each one is another set of credentials and config to keep working. The commands below give the Lane B baseline.

# Lane B setup-burden baseline: install each product on its native default.

# OpenCode (open-source, maintained by Anomaly).
# Canonical install; also available via npm/pnpm/bun/yarn, Homebrew tap,
# Arch, Chocolatey/Scoop, and Docker.
curl -fsSL https://opencode.ai/install | bash

# Claude Code (Anthropic).
# Canonical terminal install; also available via Homebrew cask and WinGet.
curl -fsSL https://claude.ai/install.sh | bash

If your workload leans on a native Claude plan, hand plan-meter detail to its owner and understand Claude usage limits before you commit.

#Setup, operations, and migration decision guide

Start with the correctness question people search for most: a Claude Pro or Max subscription can no longer drive OpenCode. OpenCode 1.3.0 removed the bundled plugins that allowed it, and Anthropic explicitly prohibits it. The honest multi-provider paths are API keys or a gateway (Zen, Go, Fast, or OpenRouter).

Migration is a config job, not just an install. Claude Code's CLAUDE.md memory and ~/.claude/settings.json map to OpenCode's AGENTS.md (created by /init) and ~/.config/opencode/opencode.json. MCP servers carry across both. Each adapter needs parseable config before it can connect or restore, and a restart after every route change.

Run a low-risk, reversible pilot to decide: route either harness with fast claude on or fast opencode on, test on real work, then restore the original config byte-for-byte with fast <agent> off, or undo everything with fast logout. Nothing is edited permanently. For the broader switching decision, see the full alternatives guide.

#Conclusion

OpenCode vs Claude Code is a harness-and-meter decision, not a model-IQ contest. If the harness fits but the model or meter doesn't, run a reversible same-model pilot and let cost per successful task settle it. Otherwise, pick by the user-type verdict above.

#Related Reading

• Run Claude Code on Fast Inference — the same-model Claude Code conversion sibling.
• Codex Usage and Limits — native meters and limits beyond a plan.
• Inference Economics: What are your options? — the per-token versus dedicated cost reasoning.