Claude Opus 5 (Adaptive Reasoning, Max Effort) vs Claude Opus 5 (Adaptive Reasoning, Medium Effort): The Ultimate Performance & Pricing Comparison

Claude Opus 5 (Adaptive Reasoning, Max Effort) is the stronger default for developer workloads because it leads the supplied quality and speed measures at the same listed price.

The comparison is less about choosing between model generations and more about choosing a reasoning policy. Anthropic positions Claude Opus 5 for complex agentic coding and enterprise work in its launch announcement. The official model overview and Opus 5 update notes document effort as a setting on claude-opus-5, rather than presenting claude-opus-5-medium as an independent API model or alias.

Both comparison rows share the release date 2026-07-24 and the same underlying API identity. Medium Effort should therefore be treated as an evaluation label and request policy, not as a separate vendor lifecycle. The underlying model is listed as Active in Anthropic's model deprecations documentation.

Data provided by https://artificialanalysis.ai/ (Artificial Analysis) supplies the numeric comparison used here. The evidence favors Max Effort, but the practical choice depends on whether deeper reasoning reduces rework or mainly increases output overhead in your workflow.

Claude Opus 5 (Adaptive Reasoning, Max Effort) leads the supplied intelligence and coding comparison while keeping the same listed price as Medium Effort.

The snapshot is clear on ranking, but less clear on why the ranking appears in individual developer workflows. The relevant values are:

Max Effort leads both quality indices and the supplied output-speed measure. Medium Effort does not compensate with a lower listed price or lower reported latency. That makes it a policy choice for controlling reasoning behavior, not a budget tier.

The official documentation describes adaptive thinking and selectable effort levels for the same model through Claude's model overview and Opus 5's update notes. Anthropic also claims strong results across agentic coding, reasoning, and computer-use evaluations in its official announcement.

Evidence remains insufficient on the most practical question: the supplied materials do not reveal whether Medium Effort uses fewer total tokens, makes fewer tool calls, or produces fewer retries on a real codebase. The indices should guide the shortlist, not replace a task-level acceptance test.

Claude Opus 5 (Adaptive Reasoning, Max Effort) leads the supplied performance comparison in output speed while tying on the reported latency measure.

The output-speed result favors Max Effort at 60.088 median output tokens per second, compared with 54.838 for Medium Effort. The reported latency is 0.3 seconds for both settings. Lower effort therefore does not buy a latency win in this snapshot, and it does not buy an output-speed win either.

For streaming code patches, detailed explanations, and agent responses that produce substantial text, the speed difference can shorten the visible tail of a response. That can improve perceived progress even when the initial response begins at the same latency. For short prompts, however, the latency tie may matter more than token throughput. A developer asking for a small transformation may see little practical benefit from the faster median generation rate.

Quality changes can matter more than speed when the task contains ambiguous requirements, cross-file dependencies, or costly review. Max Effort's stronger supplied indices suggest a better starting point for those tasks. Medium Effort remains plausible for bounded operations where the prompt, tools, and expected output are tightly constrained.

Community reports do not form a consistent speed narrative. One user describes sustained success on complex, long-running coding work in a long-task feedback comment. Other users report slow complex-task experiences, while a separate comment reports faster behavior in a different setup](https://old.reddit.com/r/ClaudeCode/comments/1va445h/opus_5_feedback_megathread/p1r6dxp/). Reports of overplanning and overtesting further show that user-perceived performance includes interaction cost, not just generation speed.

The evidence does not establish whether effort changes success rate, tool-call efficiency, or review burden on your repository. Measure completed-task rate and rework in a controlled evaluation before treating the benchmark gap as a guaranteed product advantage.

Claude Opus 5 (Adaptive Reasoning, Max Effort) is not cheaper than Medium Effort because both settings carry identical listed API prices.

The supplied comparison gives both settings a blended price of $10 per 1M tokens, with $5 input pricing and $25 output pricing. The official pricing page does not present Medium Effort as a discounted product tier. Choosing Medium solely to reduce the invoice is therefore unsupported by the available evidence.

Production economics can still diverge even when the rate card is identical. Max Effort may be cheaper per completed task if it resolves ambiguous requirements in a pass and avoids retries, manual fixes, or repeated tool calls. Medium Effort may be cheaper in practice if it handles a high-volume set of bounded tasks without producing unnecessary reasoning or verbose output. Those are workflow hypotheses, not conclusions shown by the supplied snapshot.

Prompt caching can also change effective economics for repeated context. Anthropic documents caching behavior and related controls in its Opus 5 update notes and pricing documentation. The materials do not provide cache hit rates, retry rates, tool-call counts, or cost per accepted task for either effort setting.

The right cost metric is therefore completed work, not token price alone. Track accepted pull requests, successful tool workflows, review time, retries, and total spend for the same task set. Until those measurements exist, neither setting can be called the cheaper production choice.

Claude Opus 5 (Adaptive Reasoning, Max Effort) is the safer default when wrong code or missed requirements cost more than extra reasoning.

Use the same claude-opus-5 API model and set effort explicitly for the experiment. The official model documentation and Opus 5 update notes support this interpretation. Do not build deployment logic around a separately versioned claude-opus-5-medium endpoint unless Anthropic documents one.

Keep thinking enabled when tool reliability matters. Anthropic warns that disabling thinking can cause tool calls to appear as ordinary text or expose internal XML-like material in visible responses, as described in the update notes. The same documentation also notes longer responses, more progress narration, and more autonomous verification behavior. Add output limits, tool permissions, diff checks, and tests around either setting.

Max Effort is not risk-free. Community users report instruction drift and unrequested changes in feedback about ignored instructions. Another long-context failure report describes contradictory reasoning but does not provide a complete prompt or reproducible test set. These reports justify guardrails, not a broad claim about failure frequency.

The missing decision evidence is workload-specific. The supplied materials do not show whether Medium Effort lowers total spend, whether Max Effort improves acceptance rate, or how either setting behaves under your tools and repository constraints. Start with Max for high-consequence work, then promote Medium only when your own task results justify the tradeoff.

Claude Opus 5 (Adaptive Reasoning, Medium Effort) is a configuration of the same API model, so deployment decisions should begin with an effort policy.

The official materials establish model-level support for text and image input, adaptive thinking, and selectable effort through the model overview and Opus 5 update notes. They do not establish a separate capability boundary for Medium Effort. Developers should therefore avoid assuming that Medium changes the model's available modalities or platform integrations.

The most important unresolved issue is reliability on real software tasks. Community feedback includes strong reports of extended coding success, complaints about overplanning, and conflicting speed impressions. Those reports are useful for designing tests, but they do not replace controlled evaluation. The supplied comparison also lacks evidence about retries, tool-call efficiency, instruction adherence, and total cost per accepted task.

Use the following answers as deployment guidance, then validate them against representative prompts, tools, and review standards.