Claude Opus 4.7 (Adaptive Reasoning, Max Effort) vs GPT-5 mini (high): The Ultimate Performance & Pricing Comparison

Claude Opus 4.7 is the safer choice for demanding software engineering, while GPT-5 mini is the cheaper choice when its current availability can be verified. The Artificial Analysis data gives Claude Opus 4.7 a coding index of 73.6, compared with 15.6 for GPT-5 mini, and an intelligence index of 53.5, compared with 25.3. GPT-5 mini leads on the listed math index at 90.7, but Claude Opus 4.7 has no corresponding value in the supplied dataset. The commercial gap is much larger than the latency gap: GPT-5 mini costs $0.6875 per 1M blended tokens, while Claude Opus 4.7 costs $10, and both models show 0.3 seconds of latency. The central qualification is model identity. Anthropic documents a stable claude-opus-4-7 API model, while the cited OpenAI Models page does not independently list gpt-5-mini or the “high” configuration. Developers should therefore treat this as a capability and cost comparison with an availability check still required.

Claude Opus 4.7 offers the stronger documented engineering profile, while GPT-5 mini offers a radically lower listed token price. Anthropic positions Claude Opus 4.7 for complex, long-running software engineering and agent tasks, with strict instruction following, sustained execution, and output verification described in its launch announcement. That positioning matches the supplied coding index difference, where Claude Opus 4.7 leads GPT-5 mini by 57.99999999999999 points. The intelligence index also favors Claude Opus 4.7 by 28.2 points. GPT-5 mini remains attractive for high-volume workloads because its blended price is $0.6875 instead of $10. The data also gives GPT-5 mini a math index of 90.7, which makes it the model to test for math-heavy workloads rather than dismissing it as simply weaker. The evidence does not establish whether GPT-5 mini is currently callable, what API identifier maps to “high,” or whether its listed price remains active. OpenAI Pricing does not list GPT-5 mini in the supplied research. That uncertainty is operationally important because a low theoretical price has no value if a production integration cannot reliably select the model.

Claude Opus 4.7 is the better default for code generation and multi-step engineering, based on the supplied coding and intelligence indices. A coding index of 73.6 versus 15.6 is large enough to change workflow design, not merely benchmark rankings. Teams can reasonably test Claude Opus 4.7 first for repository-wide changes, debugging across files, implementation planning, and agent loops that require the model to continue acting after its first response. Anthropic specifically describes the model as suited to complex, long-running software engineering and agent tasks in its official announcement. That does not prove equal performance on every codebase, since the supplied benchmark construction and task mix are not fully exposed.

GPT-5 mini is the more interesting specialist candidate for math-oriented workloads because the supplied math index is 90.7, while no Claude value is available for that metric. Developers should validate whether that advantage transfers to their own symbolic, numerical, or verification tasks. The available evidence does not provide output-token speed for either model, so no speed winner can be claimed beyond the latency tie at 0.3 seconds. Higher effort settings can also increase reasoning and output consumption for Claude Opus 4.7, according to the migration guide. Community feedback is split: one Reddit discussion reports verbosity, meta-commentary, and delayed execution, while other commenters describe stable coding and planning. These are personal reports without controlled methods, so they should shape testing, not settle the decision.

GPT-5 mini has the clear listed price advantage, but Claude Opus 4.7 can still be cheaper for workflows where better first-pass output reduces retries and human review. The supplied blended price is $0.6875 for GPT-5 mini versus $10 for Claude Opus 4.7. That difference strongly favors GPT-5 mini for classification, routing, lightweight transformations, and other tasks where quality thresholds are modest and the model can be invoked at scale. The input price also favors GPT-5 mini at $0.25 versus Claude Opus 4.7 at $5, while output price favors GPT-5 mini at $2 versus $25.

Nominal prices do not capture the full engineering bill. Anthropic states that Claude 4.7 models use a newer tokenizer and that the same text usually produces about 30% more tokens, as described on the pricing page. The supplied research also warns that high effort can consume additional reasoning and output tokens. A Claude workflow may therefore cost more than its displayed rates suggest, especially with long code context and maximal reasoning. GPT-5 mini's practical cost cannot be confirmed until its current model entry and price are verified through the cited OpenAI Pricing page or an account-level API response. The evidence does not show retry rates, review time, tool-call counts, or production error costs for either model. Those missing variables can reverse the cheapest-model decision, so a representative workload trial matters more than the blended-token chart alone.

Claude Opus 4.7 should be the primary candidate for high-stakes engineering agents, while GPT-5 mini should be the first candidate for low-cost math or high-volume inference after availability checks. Choose Claude Opus 4.7 when the task involves repository-wide edits, long-running implementation, strict adherence to detailed instructions, or a high cost of incorrect code. Its documented 1M token context window and synchronous Messages API output limit of 128k tokens make it suitable for large working sets, although capacity should not be confused with stable retrieval quality. A Hacker News discussion cites retrieval results of 59.2% at 128k–256k context and 32.2% at 524k–1024k context, but those figures are community interpretations of official material rather than independent replication.

Choose GPT-5 mini when the workload is dominated by inexpensive requests, math evaluation, or tasks where a lower capability ceiling is acceptable. Its listed math index of 90.7 deserves a focused trial, while its coding index of 15.6 argues against using it as the default autonomous software engineer without strong external tests. GPT-5 mini is not a safe procurement assumption yet because the cited OpenAI Models page does not confirm its model entry, API parameters, context window, or tool support. For Claude, teams must account for stricter instruction literalism, unsupported legacy configurations, restricted sampling parameters, and possible blocking of high-risk cybersecurity requests, as documented in Anthropic's launch announcement and migration guide. The practical selection path is to verify availability first, then run coding, math, retrieval, and retry-cost tests on representative tasks.

Claude Opus 4.7 is the stronger starting point for production software engineering because its documented positioning and supplied coding index support complex implementation and agent workflows. Developers should still test prompt literalism, verbosity, and long-context retrieval before standardizing it.