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

There is no universal 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. The current Artificial Analysis Opus 4.7 medium lane is not an equal-quality comparison: OpenCode scores 51 versus Claude Code's 42, while Claude Code uses fewer tokens, costs less and finishes sooner in that lane.

That quality-versus-usage tradeoff 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 documented 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. For a historical example of evaluating a temporary free stealth-model route in OpenCode, use the Ox Alpha privacy and buyer guide.

Claude Code vs OpenCode: the short buying verdict

What do independent Claude Code vs OpenCode benchmarks show?

They show that cost cannot be separated from achieved quality. The June harness-efficiency benchmark held the task set constant across two model pairings. 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

Claude's current US Team list price is $20 per Standard seat or $100 per Premium seat monthly when billed annually, with higher month-to-month prices and taxes excluded. Both tiers include per-member usage limits, and optional usage credits are billed at standard API rates. Current Enterprise costs $20 per seat per month billed annually, but that fee covers access only and all usage is separately metered at API rates. Legacy seat-based Enterprise contracts retain different allowances until migration, so confirm the contract generation as well as the seat name.

OpenCode's provider documentation says the client supports more than 75 LLM providers and local models. Its optional Zen gateway charges per request with zero model markup, adds card-processing fees when buying credit, supports monthly limits and hosts all listed models in the United States. BYOK can be cheaper when you already have negotiated provider pricing, but it can also fragment cost reporting and support. The client source is available under the MIT License; that does not replace the terms of the selected model or provider.

Data-use terms follow the account and route, not just the client name. Anthropic says inputs and outputs from commercial Team, Enterprise and API products are not used for model training by default, except when a customer submits feedback or explicitly opts in. Consumer Free, Pro and Max accounts using Claude Code follow separate consumer controls and safety-review exceptions. Anthropic also documents a 30-day safety-retention rule for designated Covered Models on otherwise zero-data-retention commercial routes. OpenCode says the client does not store code or context by default, but the chosen provider or gateway still processes it and optional /share sends the conversation to OpenCode's hosted share service. Zen advertises zero retention and no training subject to listed provider exceptions. Procurement should verify the selected model, provider, account type and sharing configuration together.

Claude Code documents a centralized control plane for managed rollouts. Anthropic describes server-managed settings, policy precedence, audit events and an optional fail-closed startup for Team and Enterprise plans. Those server-delivered settings require a direct Anthropic connection and are bypassed by third-party model routes; Anthropic describes endpoint-managed settings as the stronger option on managed devices. OpenCode gives you more infrastructure sovereignty. Its enterprise guidance describes central configuration, SSO and an internal gateway that can disable other providers. 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 current public evidence does not isolate one answer. In Artificial Analysis' current Opus 4.7 medium lane, OpenCode scores 51 versus Claude Code's 42, but Claude Code uses 4.6 million versus 7.6 million tokens, costs $1.80 versus $2.94 and runs in 6.7 versus 12.5 minutes. Because quality differs, this is not an equal-outcome cost comparison. The two-model harness-efficiency benchmark also 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 a legacy seat-based Enterprise plan, included usage is not a per-token invoice. Current usage-based Enterprise is different: its access seat includes no token allowance, so Claude Code consumption is billed at API rates from the first token. Compare incremental cash cost, usage-limit interruptions and accepted work under the exact contract, not an abstract token total.

If one person already owns several paid Claude accounts and the problem is manual failover across private machines, our claude-rotate setup and risk guide covers quota-aware rotation separately. It is not the team procurement path evaluated here.

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 Anthropic's first-party 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 fully reviewed on 2 September 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 claims were checked against Anthropic's official cost, cache, deployment, pricing and privacy documentation, plus OpenCode's official provider, Zen, enterprise and licensing sources linked above. Recheck the dynamic benchmark and commercial terms before signing a contract.