Claude Opus 4.8 vs GPT-5 mini: Which Model Should Developers Choose?

Claude Opus 4.8 is the stronger documented choice for complex development work, while GPT-5 mini is the cheaper option with a clear mathematics advantage. The supplied data gives Claude Opus 4.8 an Artificial Analysis coding index of 74.3, compared with 15.6 for GPT-5 mini. Claude also leads the Artificial Analysis intelligence index at 55.7 versus 25.3. GPT-5 mini reaches 90.7 on the Artificial Analysis math index, while no Claude score is provided for that evaluation.\n\nThe price gap is substantial. Claude Opus 4.8 costs $10 per 1M blended tokens under the supplied 3-to-1 input-output mix. GPT-5 mini costs $0.6875 for the same measure. Both models have a listed latency of 0.3 seconds, and neither has a supplied median output speed.\n\nThe larger selection risk is documentation asymmetry. Anthropic provides current model, pricing, lifecycle, and behavior documentation for Claude Opus 4.8. The supplied OpenAI sources do not independently document GPT-5 mini or the “high” designation. Developers should treat GPT-5 mini as a potentially attractive low-cost candidate that still requires availability and integration verification.

Claude Opus 4.8 is the safer default for high-consequence coding and agent tasks because its documented capability profile is materially stronger. Anthropic positions Claude Opus 4.8 for complex coding, agent workflows, and professional knowledge work in its release announcement. The same announcement reports an Online-Mind2Web result of 84% and states that the model is about four times less likely to let defects in generated code pass without prompting. Those are vendor claims, not independent validation.\n\nGPT-5 mini is more compelling when unit cost dominates and the task is narrow, repetitive, or easy to verify. The supplied data gives it a 90.7 Artificial Analysis math index, the only mathematics score in the comparison. That result creates a meaningful exception to the broader Claude lead, especially for workloads centered on structured calculations or mathematical answers. It does not establish general coding or agent superiority.\n\nClaude Opus 4.8 also has a clearer production story. The model overview documents text and image input, text output, multilingual capability, visual understanding, and access through the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry. GPT-5 mini’s supplied OpenAI documentation does not confirm its dedicated context window, output limit, API parameters, tool support, or deployment status.\n\nFor developers, the practical comparison is therefore not simply quality against price. It is documented capability and operational confidence against very low token cost. Claude is easier to evaluate as a known product. GPT-5 mini may be economically superior after a successful endpoint and regression-test check.

Claude Opus 4.8 is the stronger documented performer for software engineering, although GPT-5 mini remains the more interesting specialist candidate for mathematics. The Artificial Analysis coding index is 74.3 for Claude Opus 4.8 and 15.6 for GPT-5 mini. That gap is large enough to change workflow design. A coding team using Claude can reasonably ask for repository-level diagnosis, implementation planning, and iterative correction in one model loop. A team using GPT-5 mini should expect to compensate with tighter task boundaries, stronger tests, or more frequent human review.\n\nThe intelligence index points in the same direction, at 55.7 for Claude Opus 4.8 and 25.3 for GPT-5 mini. The numbers do not identify which individual benchmark tasks caused the difference, so they should guide screening rather than replace application-specific tests. They also do not prove that Claude will win every programming language, framework, or prompt style.\n\nGPT-5 mini’s 90.7 math index is the clearest counterweight. If a product mainly generates or checks mathematical solutions, this supplied result deserves a dedicated pilot. The comparison cannot say whether that advantage transfers to code generation, debugging, tool use, or long-horizon planning. No Claude math score is supplied, so there is no direct measured winner for that dimension.\n\nLatency does not separate the models in the supplied snapshot. Both list 0.3 seconds. Neither model has a supplied median output-tokens-per-second value, so the evidence cannot support a claim about streaming speed, total completion time, or throughput under long outputs.\n\nClaude’s reasoning controls add useful policy choices. The model overview documents Adaptive thinking and effort levels of low, medium, high, max, and xhigh. The effort documentation warns that effort is a behavior signal, not a strict token budget. That makes effort useful for quality tuning, but unsuitable as a precise latency or cost ceiling.\n\nCommunity evidence complicates the positive Claude picture. One Reddit user reported better self-correction and more responsive answer length after three days of use, but also reported that multi-step agents could skip explicit steps and reach correct results through messy paths. A commenter reported preferring an older version in a few non-coding A/B tests because Adaptive thinking sometimes underestimated hidden difficulty. Both accounts lack reproducible datasets and quantitative records, so they identify risks rather than establish rates. See the Reddit discussion.

GPT-5 mini is the clear price winner, but Claude Opus 4.8 can still be cheaper at the project level when it prevents rework. Under the supplied blended 3-to-1 input-output measure, GPT-5 mini costs $0.6875 per 1M tokens, compared with $10 for Claude Opus 4.8. Input pricing is $0.25 versus $5 per 1M tokens, and output pricing is $2 versus $25. The page chart shows the full comparison, so the important question is what those prices buy in a real development loop.\n\nGPT-5 mini is economically attractive for high-volume classification, extraction, lightweight transformation, and mathematical workloads that can be validated cheaply. Its low output price also makes experimentation less expensive when prompts produce verbose responses or when an application needs several candidate answers. The supplied sources do not confirm whether this model is currently callable, so the cost case remains conditional until an endpoint, model ID, and billing behavior are verified.\n\nClaude Opus 4.8 becomes more defensible when a failed answer creates expensive downstream work. A coding agent that misses a requirement, skips a migration step, or needs repeated repair cycles can consume developer time that token pricing does not capture. The supplied coding index favors Claude, but the data does not quantify defect rates, completion rates, or total engineering hours. Teams should therefore measure cost per accepted change, not only cost per token.\n\nClaude’s official pricing page lists prompt caching at $6.25 per 1M tokens for 5-minute writes, $10 for 1-hour writes, and $0.50 for cache hits. Caching can alter repeated-context economics, especially for repository instructions or long reference material. It does not erase Claude’s standard input and output price difference.\n\nThe cost conclusion can flip under two conditions. GPT-5 mini is not cheaper if it is unavailable, lacks required controls, or needs enough retries and human correction to offset its token advantage. Claude Opus 4.8 is not better value if the task is simple, easily checked, and generated at large volume. The supplied brief does not provide batch pricing, retry rates, throughput, or total-cost measurements for either model.

Claude Opus 4.8 is the recommended primary model for teams building complex coding agents or high-value engineering workflows. Its documented positioning covers complex coding and agent work, and its supplied coding index is 74.3. Use it when the model must inspect a large repository, preserve constraints across multiple steps, explain tradeoffs, and produce changes that are reviewed as a coherent unit. Keep tests, diffs, and approval gates in the loop because community reports still describe skipped steps and unverified guesses.\n\nGPT-5 mini is the recommended first experiment for cost-sensitive workloads with strong external verification. Its $0.6875 blended price and 90.7 math index make it worth testing for mathematical reasoning, structured generation, batch-like transformations, and simple developer utilities. The recommendation is conditional because the supplied OpenAI Models page does not list a dedicated GPT-5 mini entry, and the supplied OpenAI Pricing page does not list its pricing or operational modes.\n\nChoose Claude Opus 4.8 when failure costs more than tokens. Choose GPT-5 mini when volume costs more than occasional correction and the application can reject bad outputs automatically. A two-model architecture is also reasonable: use GPT-5 mini for cheap candidate generation or mathematical subproblems, then route ambiguous coding or agent decisions to Claude. The supplied evidence does not prove that this routing pattern improves total cost, so it needs an application-specific pilot.\n\nBefore production selection, verify GPT-5 mini’s exact API identifier, availability, context limit, output limit, tool behavior, and billing. Anthropic’s model lifecycle documentation lists Claude Opus 4.8 as Active and gives a retirement date no earlier than 2027-05-28. That gives Claude a clearer documented lifecycle position, though it does not guarantee uninterrupted service or future performance.

GPT-5 mini has the largest evidence gap in this comparison because the supplied official pages do not independently confirm its current model entry, API behavior, pricing, or limits. The data snapshot supplies benchmark and price values, but the research brief does not connect those values to a verified public endpoint.\n\nClaude Opus 4.8 has better documentation, but its evidence is not complete either. The official benchmark claims come from Anthropic, and community reports are subjective. No supplied source establishes real-world coding throughput, total completion time, defect rate, tool-call reliability, or cost per accepted code change.\n\nDevelopers should read the comparison as a screening decision, not a universal ranking. Claude is the better-supported choice for complex coding. GPT-5 mini is the lower-cost and stronger supplied mathematics candidate. The unresolved operational facts should be tested before either model becomes a production dependency.