Claude Code vs OpenCode: Which Costs Less for a Team in 2026?

There is no universal token-cost winner. A June 2026 independent benchmark gave Claude Code and OpenCode the same two models and the same 12 small Python tasks. Claude Code used about 52,000 to 55,000 tokens per solved task, while OpenCode used about 72,000 to 80,000. Artificial Analysis found the opposite in its same-model Opus 4.7 medium lane: both harnesses scored 45, but OpenCode used 7.6 million tokens per task and cost $2.93 versus Claude Code's 16 million tokens and $5.65.

That disagreement is the buying answer. Harness, model, gateway, cache path, task shape and acceptance tests all change the result. OpenCode gives you broader model choice and more control over the inference route. Claude Code gives you an integrated Anthropic workflow and mature organization controls. The cheaper option is the one that produces an accepted change at lower total cost in your repository, not the winner of one public benchmark.

This guide owns the tool-selection question. For general cost reduction, use our LLM token cost playbook. For workflow economics, use cost per successful agent action.

Claude Code vs OpenCode: the short buying verdict

What do independent Claude Code vs OpenCode benchmarks show?

They show that the winner changes with the test. The June harness-efficiency benchmark controlled the model and task set across two runs. Artificial Analysis separately compares coding-agent performance, cost, execution time and token usage. Together they provide a more useful decision boundary than a single startup-prompt ratio.

These are cumulative metered input tokens, not invoice totals. Cache writes, cache reads, uncached input and output have different prices. Anthropic's current prompt-caching table prices a five-minute cache write at 1.25 times base input, a one-hour write at 2 times and a cache hit at 0.1 times. A raw token ratio therefore cannot be converted directly into a dollar ratio.

The limits matter. The harness-efficiency suite used small Python tasks and found that its Claude Code route received no cache hits because of gateway translation. Artificial Analysis measures pay-per-token API cost, not subscriptions, engineering time or production operations. A 2026 study of agentic coding economics also found that repeated runs of the same task can vary by up to 30 times in total tokens and that higher usage does not reliably improve accuracy. Treat every public number as evidence, not a vendor guarantee.

What creates the hidden coding-agent token tax?

A coding-agent request contains more than your prompt. It can carry a system prompt, tool schemas, repository instructions, conversation history, file contents, tool results, MCP definitions and reasoning output. The useful approximation is:

whole-task input ≈ fixed harness payload × model requests + growing task history

Your invoice then separates that input into uncached tokens, cache writes and cache reads, before adding output tokens. Your business cost adds failed attempts and human review.

Claude's own cost-management documentation confirms the operational levers: clear stale context, choose the right model, reduce MCP overhead, use code intelligence, offload preprocessing to hooks and move optional knowledge from CLAUDE.md into skills. It also reports an average of roughly $150 to $250 per developer per month across enterprise deployments, while warning that codebase size, model choice and automation change the result materially.

Which platform has the lower total cost for a team?

Token spend is only one line in the decision. Use this total-cost model:

monthly TCO = seats + API usage + gateway and observability + setup amortization + review time + failed-task rework + security administration

OpenCode's provider documentation says the client supports more than 75 LLM providers and local models. Its optional Zen gateway advertises pay-as-you-go access, monthly limits and no model markup beyond processing fees. BYOK can be cheaper when you already have negotiated provider pricing, but it can also fragment cost reporting and support.

Claude Code has the stronger documented control plane for a centrally managed rollout. Anthropic documents server-managed settings, policy precedence, audit events and fail-closed startup options for Team and Enterprise plans. OpenCode gives you more infrastructure sovereignty. Its enterprise guidance positions an internal provider or AI gateway as the route for keeping code and data inside your infrastructure. That freedom is valuable, but the gateway, identity layer, policy distribution and support model become your responsibility.

Is OpenCode cheaper if both tools use the same Claude model?

Sometimes, but the independent evidence is mixed. Artificial Analysis' Opus 4.7 medium lane gave both tools an index score of 45 and measured a lower token count, API cost and runtime for OpenCode. The two-model harness-efficiency benchmark gave Claude Code lower raw tokens per solved task on its small Python suite. Using the same model removes one major confounder, but tools, prompts, cache routing, request count and task design still differ.

For a team on Claude Max, Team or Enterprise, the comparison changes again. Included seat usage is not a per-token invoice. An OpenCode API bill can be larger than an already-paid seat even when OpenCode sends fewer tokens. Compare the incremental cash cost, usage-limit interruptions and accepted work, not an abstract token total.

How should a team benchmark Claude Code against OpenCode?

1. Freeze the comparison. Record repository commit, harness version, model ID, provider, region, tools, MCP servers, instruction files, permissions and cache state.
2. Use at least four task classes. Include small edits, bug diagnosis, multi-file features and hard refactors. A one-line reply measures the floor, not developer value.
3. Predefine acceptance. Use hidden tests, lint, type checks, security gates and a human rubric. Do not let either agent grade its own output.
4. Run fresh and warm lanes. Separate cold cache writes from repeated work, and run every task more than once.
5. Capture the whole trace. Record uncached input, cache writes, cache reads, output, requests, tool calls, elapsed time, failures and human correction minutes.
6. Price the accepted result. Failed runs stay in the numerator. Divide total spend and review labor by accepted tasks, not prompts.
7. Test governance. Verify denied paths, secrets, network access, model identity, logs, policy rollout and offboarding before a broad deployment.

A practical pilot uses 20 to 30 representative tasks across two repositories, three repeated runs per lane and one week of real developer use. The decision gates should be cost per accepted task, median completion time, pass rate, serious defects per accepted change and developer intervention minutes. Raw tokens remain a diagnostic metric.

What should procurement ask before choosing?

• Which model and provider actually served each request?
• Can we export uncached input, cache-write, cache-read and output usage by user and repository?
• Can administrators enforce models, permissions, MCP servers and network destinations?
• Where do prompts, code, logs and telemetry travel and persist?
• What happens when a seat limit, provider rate limit or gateway outage occurs?
• Can we reproduce a session after the harness or model changes?
• Who owns incident response across client, gateway and model provider?
• What is the exit path for instructions, agents, skills, logs and usage history?

Our recommendation

Choose Claude Code when the team wants the best-supported Anthropic workflow, already pays for eligible seats and values integrated enterprise controls more than model portability.

Choose OpenCode when provider choice, BYOK, local models or an internal inference gateway are central requirements, and your team can own the extra integration surface.

Do not choose either from one benchmark. Use conflicting public results to justify measurement. Run the same accepted-work benchmark behind the same observability boundary, then buy the lower total cost per successful change.

Sources and date boundary

This article was researched on 19 July 2026. Harness prompts, model behavior, plan limits and pricing change quickly. Comparative evidence comes from the independent harness-efficiency benchmark, Artificial Analysis, SWE-Bench Mobile and the agent-cost study linked above. Product capabilities were checked against the official Claude Code cost docs, Claude Code cache documentation, Claude Code enterprise deployment overview, OpenCode documentation and OpenCode agent controls. Recheck them before signing a contract.