Claude Opus 5 (Adaptive Reasoning, Max Effort) vs GPT-4o mini: The Ultimate Performance & Pricing Comparison

Claude Opus 5 is the stronger choice for demanding software work, while GPT-4o mini remains the cost-first option for high-volume, lower-complexity tasks. The Artificial Analysis data shows a coding index of 78 for Claude Opus 5 and 11.4 for GPT-4o mini, alongside a blended-token price of $10 versus $0.2625. Artificial Analysis provides the comparison data.\n\nThe two models also differ in product posture. Anthropic presents Claude Opus 5 as a model for complex agentic coding and enterprise work. Anthropic’s announcement supports that positioning. OpenAI introduced GPT-4o mini as a small, cost-efficient model for frequent workloads. OpenAI’s launch announcement supports the lower-cost positioning.\n\nThe most important selection question is therefore not which model is universally better. It is whether your workload loses more money through model mistakes and supervision than it saves through lower token prices. The supplied material does not provide a controlled, head-to-head task study, a matched success rate, or a reliable GPT-4o mini output-speed measurement. Those gaps limit how confidently benchmark differences can predict production outcomes.

Claude Opus 5 offers the larger capability margin, while GPT-4o mini offers the clearer economic margin. Claude Opus 5 leads the Artificial Analysis intelligence index at 60.7 versus 6.9 and the coding index at 78 versus 11.4. Artificial Analysis reports both comparisons.\n\n| Decision factor | Better fit | Why it matters |\n|---|---|---|\n| Complex coding | Claude Opus 5 | The coding-index gap suggests a materially different ceiling for repository-scale work, debugging, and multi-step implementation. |\n| High-volume classification | GPT-4o mini | The blended price is $0.2625 instead of $10 per 1M blended tokens. |\n| Interactive generation | Claude Opus 5, based on available data | Its median output speed is 60.088 tokens per second, while no comparable GPT-4o mini value is supplied. |\n| Predictable lifecycle | Claude Opus 5, based on documented evidence | Anthropic lists it as Active and gives no deprecation date. Model deprecations |\n| Current OpenAI availability | Evidence insufficient | OpenAI’s current model directory emphasizes newer families, but the supplied material does not clearly state whether GPT-4o mini remains directly callable. OpenAI model directory |\n\nGPT-4o mini should not be dismissed simply because its aggregate scores are lower. A small model can be the rational choice for routing, extraction, short transformations, and other tasks where errors are cheap and review is automatic. The research material provides no verified community evidence for GPT-4o mini’s coding style, speed, or failure patterns, so production testing is especially important before generalizing from its launch benchmarks.\n\nClaude Opus 5 also carries meaningful operational caveats. Anthropic documents longer default responses, more progress narration, more active delegation in multi-agent settings, and repeated validation behavior. What’s new in Claude Opus 5 describes these behavior changes. The community reports similar concerns, but the Reddit discussions do not disclose standardized tests. ClaudeCode discussion and ClaudeAI discussion should therefore be treated as anecdotal evidence.

Claude Opus 5 is the safer performance bet for tasks where the model must plan, modify, verify, and recover across several steps. The available Artificial Analysis scores show Claude Opus 5 at 78 for coding and 60.7 for intelligence, compared with 11.4 and 6.9 for GPT-4o mini. Artificial Analysis supplies those measurements.\n\nThe practical meaning is not that Claude Opus 5 will produce a specific percentage of correct pull requests. The supplied data does not include a shared task set, pass rate, confidence interval, or human-review cost. The scores instead support a directional conclusion: Claude Opus 5 is designed and measured closer to difficult reasoning and coding workloads, while GPT-4o mini is better understood as a lightweight general-purpose component.\n\nFor a developer, the distinction appears in task shape. A repository migration may require reading unfamiliar files, preserving constraints, selecting a safe edit sequence, running checks, interpreting failures, and revising the patch. Every additional step creates another opportunity for a low-capability model to need supervision. A model with a higher coding score may reduce that supervision, even if its per-token price is higher. The evidence does not quantify that reduction, so teams should measure successful task completion rather than assume it.\n\nClaude Opus 5’s available output-speed figure is 60.088 median output tokens per second, and its reported latency is 0.3 seconds. GPT-4o mini also has reported latency of 0.3 seconds, but the comparison supplies no median output-speed value for it. Artificial Analysis therefore supports a latency tie and a one-sided output-speed observation, not a complete speed ranking.\n\nAnthropic’s official materials add capabilities relevant to agent builders. Claude Opus 5 supports adaptive thinking, configurable effort levels, and beta session tool changes. Model overview and What’s new in Claude Opus 5 document those controls. The same documentation warns that disabling thinking can cause malformed tool behavior or visible internal markup.\n\nGPT-4o mini’s official launch material reports MMLU at 82.0%, MGSM at 87.0%, HumanEval at 87.2%, and MMMU at 59.4%. OpenAI’s launch announcement gives those release-time results. They demonstrate useful capability, but they are not a matched comparison with the Artificial Analysis indices. Treating them as directly interchangeable scores would overstate the evidence.

GPT-4o mini is dramatically cheaper at the token level, but Claude Opus 5 can be cheaper at the workflow level when it prevents supervision and rework. The blended price is $0.2625 for GPT-4o mini and $10 for Claude Opus 5, according to Artificial Analysis.\n\nThe price difference favors GPT-4o mini for workloads with stable prompts, short outputs, automatic validation, and low consequences from occasional mistakes. Examples include tagging, routing, simple extraction, lightweight rewriting, and first-pass enrichment. In those cases, paying for a more capable model may add little value because the surrounding system already constrains the task.\n\nThe economics can reverse when a task requires repeated retries, human review, manual correction, or expensive downstream execution. A low-cost model that creates a faulty code change, incorrect migration, or incomplete analysis can consume more engineering time than the token bill suggests. The supplied research does not provide failure rates or review-time measurements, so no break-even point can be calculated responsibly. Teams should record successful completion, retries, review minutes, and downstream incidents for their own workload.\n\nClaude Opus 5’s standard API price is $5 per 1M input tokens and $25 per 1M output tokens. Anthropic pricing also documents prompt caching, with a minimum cacheable prompt length of 512 tokens in the supplied research. Caching can change the economics of repeated repository context, but the comparison does not provide cache-hit rates or a cache-adjusted blended price.\n\nGPT-4o mini’s launch price was $0.15 per 1M input tokens and $0.6 per 1M output tokens. OpenAI’s launch announcement reports those values. The current OpenAI pricing page does not list GPT-4o mini in the supplied research. OpenAI pricing therefore creates a procurement risk: the historical price is clear, but current direct-call pricing and availability are not confirmed.\n\nFast mode for Claude Opus 5 costs $10 per 1M input tokens and $50 per 1M output tokens, and it is currently unavailable on Amazon Bedrock, Google Cloud, and Microsoft Foundry according to What’s new in Claude Opus 5. That option can improve responsiveness, but it can also weaken the cost case and complicate a multi-provider deployment.

Claude Opus 5 is the default recommendation for high-consequence coding and autonomous engineering workflows, while GPT-4o mini is the default recommendation for cheap, bounded operations. Claude Opus 5’s coding index is 78 versus 11.4 for GPT-4o mini, and its blended price is $10 versus $0.2625. Artificial Analysis provides the underlying comparison.\n\nChoose Claude Opus 5 when the model must handle repository-scale changes, complex debugging, agentic tool use, long-running plans, or work where a human reviewer is expensive. Anthropic explicitly positions it for complex agentic coding and enterprise work. Claude Opus 5 announcement supports that use case. Its support across the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry is documented in the model overview.\n\nChoose GPT-4o mini when the task is narrow, repetitive, easy to validate, and dominated by volume. Its official positioning is cost-efficient intelligence for frequent use, and the available blended price is far lower. OpenAI’s launch announcement supports that interpretation. Keep the model behind a router or validator if the task can be classified by difficulty.\n\nUse a two-tier design when the application contains both task types. Send routine requests to GPT-4o mini. Escalate ambiguous, failed, high-impact, or multi-step requests to Claude Opus 5. This architecture preserves the cheaper model’s volume advantage while reserving the stronger model for cases where its capability can pay for itself. The supplied evidence does not prove the routing threshold, so the threshold must come from application telemetry.\n\nDo not select GPT-4o mini solely because its historical price is known. The current OpenAI pricing page does not list it in the supplied research, and the material does not clearly confirm current direct-call availability. Do not select Claude Opus 5 solely from benchmark leadership either. Anthropic’s system-card page could not be read during the research, and community complaints about verbosity, overthinking, and instruction drift lack reproducible test methods. Hacker News discussion adds a similar anecdotal concern about pursuing an incorrect visual-processing path.\n\nThe strongest practical conclusion is conditional. Claude Opus 5 has the better evidence for difficult development work. GPT-4o mini has the better evidence for low-cost scale. A small pilot using representative tasks remains necessary because the supplied materials do not reveal production success rates, total engineering cost, or current GPT-4o mini availability.

Claude Opus 5 is the model to test first when a deployment depends on reliable multi-step coding behavior. The available evidence supports that priority, but the final decision still depends on workload telemetry and confirmed commercial availability.