Claude Opus 4.6 (Adaptive Reasoning, Max Effort) vs GPT-5 mini (high): The Ultimate Performance & Pricing Comparison

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) is the stronger measured general-intelligence option, while GPT-5 mini (high) is the much cheaper deployment candidate. The Artificial Analysis Intelligence Index reports 43.7 for Claude Opus 4.6 and 25.3 for GPT-5 mini. That result supports a quality-first choice for Claude, but it does not establish a winner for coding, mathematics, or output speed because the comparison reports no Claude coding or math score and no median output-tokens-per-second value for either model.

The decision also has an availability risk. Anthropic’s official materials still list Claude Opus 4.6 in current pricing, but the model overview emphasizes newer Claude Opus products and does not confirm the exact claude-opus-4-6-adaptive API slug. Anthropic’s Claude model overview provides the relevant model and capability context, while Anthropic’s pricing documentation confirms the listed Opus pricing.

OpenAI presents a different problem. The current OpenAI model catalog does not list gpt-5-mini or GPT-5 mini (high), and OpenAI pricing does not provide a corresponding price entry. The data brief supplies benchmark and pricing values, but official current documentation does not independently confirm that this exact model name remains directly callable.

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) offers the better measured intelligence result, but GPT-5 mini (high) offers the clearer economic case in the supplied data. The reported Intelligence Index is 43.7 for Claude and 25.3 for GPT-5 mini, with a reported difference of 18.400000000000002. That gap is meaningful for teams choosing a default model for difficult, open-ended work, although the index alone does not reveal the tasks that produced it.

GPT-5 mini is dramatically cheaper in every supplied pricing view. Its blended 3-to-1 price is $0.6875 per 1M tokens, compared with $10 for Claude Opus 4.6. Its input price is $0.25 per 1M tokens, compared with $5, and its output price is $2, compared with $25. Those values make GPT-5 mini attractive for high-volume classification, extraction, routing, and routine generation, provided the model can be called reliably in the intended environment.

Latency does not separate the models. The supplied data reports 0.3 seconds for each model, and it reports no median output-tokens-per-second value for either one. A developer therefore cannot infer that GPT-5 mini will stream faster, finish long responses sooner, or feel more responsive in an interactive coding workflow.

The official evidence is asymmetric. Anthropic documents current Claude capabilities including text and image input, text output, multilingual support, visual capabilities, and several deployment channels, but does not isolate every claim to Opus 4.6. The Claude overview also does not confirm the exact adaptive slug. OpenAI’s current model documentation describes current models generally but does not confirm this GPT-5 mini entry. The result is a price-and-score comparison with unresolved product-status questions.

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) leads the supplied general-intelligence measurement, but the evidence does not prove that it is better for every developer task. Its Artificial Analysis Intelligence Index is 43.7, versus 25.3 for GPT-5 mini. A higher general score can justify testing Claude first on tasks requiring broad reasoning, ambiguous instructions, or multi-step judgment. It cannot substitute for a workload-specific evaluation.

The missing coding result is the most important limitation. GPT-5 mini has a reported Artificial Analysis Coding Index of 15.6, while Claude Opus 4.6 has no value in the supplied snapshot. The comparison therefore cannot establish a coding winner. Developers should not convert Claude’s higher general-intelligence score into an unsupported claim about repository changes, debugging, test generation, or code review. The supplied research also found no reliable Reddit, Hacker News, or X material that could validate coding experience or speed impressions for either model.

Mathematics is similarly unresolved. GPT-5 mini has a reported Artificial Analysis Math Index of 90.7, while Claude Opus 4.6 has no supplied value. That score gives GPT-5 mini a concrete reason to enter a math-heavy evaluation, but it does not show how the model handles tool use, explanation quality, or production constraints. The research brief found no official, model-specific benchmark announcement for either candidate.

Latency is a tie at 0.3 seconds in the supplied data. The absence of median output speed means the chart cannot answer whether either model produces tokens faster after the first response arrives. This distinction matters for coding agents and streaming interfaces, where time to first response and sustained generation can affect perceived performance differently.

Anthropic’s official model overview confirms broad current Claude capabilities, but it does not provide a Claude Opus 4.6 context window, synchronous maximum output, API parameter list, or model-specific benchmark. OpenAI’s official model catalog likewise does not confirm the GPT-5 mini entry or its technical limits. Those omissions leave context capacity, tool behavior, and failure boundaries as evidence gaps rather than tie-breakers.

GPT-5 mini (high) is the cost winner in the supplied pricing data, but its lower token price does not guarantee a lower total application cost. The blended 3-to-1 price is $0.6875 per 1M tokens for GPT-5 mini and $10 for Claude Opus 4.6. GPT-5 mini also has lower listed input and output prices, at $0.25 and $2 per 1M tokens, compared with Claude’s $5 and $25.

The practical question is whether a cheaper request completes the task correctly. If GPT-5 mini requires more retries, longer prompts, additional validation, or a second model to repair output, its apparent savings may narrow. The supplied data does not report retry rates, task success rates, token consumption by workflow, or downstream engineering effort. Any claim that one model has a lower total cost of ownership would therefore exceed the evidence.

Claude’s pricing has more visible caching and routing conditions. Anthropic lists 5-minute cache writes at $6.25 per MTok, 1-hour cache writes at $10 per MTok, cache hits and refreshes at $0.50 per MTok, standard input at $5 per MTok, and output at $25 per MTok. Anthropic also states that inference_geo: "us" applies a 1.1 multiplier to Claude 4.6 and later models, while the default global setting uses standard pricing. These details can change the economics of long prompts, repeated context, and region-sensitive deployments. Anthropic’s pricing page is the source for these conditions.

Claude also has a documented Message Batches API path for up to 300k output tokens through the output-300k-2026-03-24 beta header. That is not evidence of the synchronous Messages API’s default output limit. GPT-5 mini has no comparable model-specific pricing or output-limit detail in the supplied OpenAI sources. OpenAI pricing does not list GPT-5 mini, so the supplied $0.6875 blended figure should be treated as benchmark data for this comparison, not as independently confirmed current OpenAI catalog pricing.

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) is the better first candidate for quality-sensitive general reasoning, while GPT-5 mini (high) is the better first candidate for cost-sensitive scale. Claude’s measured Intelligence Index of 43.7 exceeds GPT-5 mini’s 25.3, and that is the strongest supplied argument for using Claude as a premium default. GPT-5 mini’s $0.6875 blended price is the strongest argument for using it as a high-volume default.

Pick Claude when the cost of a wrong or incomplete answer is high and the workflow needs broad judgment. Examples include complex planning, ambiguous technical analysis, and agentic tasks where one failed step can create expensive human review. This recommendation is conditional because the research provides no model-specific coding benchmark, no confirmed context window, and no reliable community evidence. The official Claude overview describes capabilities for current Claude models generally, not every Opus 4.6 behavior.

Pick GPT-5 mini when request volume dominates unit economics and the task can be constrained by schemas, tests, validators, or deterministic business rules. The supplied Math Index of 90.7 gives it a concrete evaluation reason for quantitative workloads. Its Coding Index of 15.6 is also available for testing, but no Claude coding value is supplied, so the data cannot support a coding comparison.

Before production selection, verify the exact API identifiers and availability. Anthropic’s pricing page lists Claude Opus 4.6 pricing and does not mark it retired, yet the supplied research did not find official confirmation of claude-opus-4-6-adaptive. OpenAI’s model catalog and pricing page do not list gpt-5-mini in the provided materials. This documentation gap can outweigh benchmark differences if a team needs a stable, supported production endpoint.

A sensible shortlist is therefore Claude for a quality-first pilot and GPT-5 mini for a cost-first pilot, followed by the same private task set, identical prompts, tool definitions, validation rules, and retry policy. The supplied materials do not include those application-specific measurements, so the final choice remains evidence-limited.

Claude Opus 4.6 (Adaptive Reasoning, Max Effort) should be the quality-first test candidate, while GPT-5 mini (high) should be the cost-first test candidate. The supplied Intelligence Index favors Claude, but coding, math comparison, output speed, and API availability remain incomplete.