Claude Opus 4.8 (Adaptive Reasoning, Max Effort) vs GPT-5 (high): The Ultimate Performance & Pricing Comparison

Claude Opus 4.8 is the stronger choice for difficult software work, while GPT-5 is the stronger value choice for cost-sensitive applications. The Artificial Analysis snapshot gives Claude Opus 4.8 a coding index of 74.3, compared with 37.8 for GPT-5, while GPT-5 costs $3.4375 versus $10 per 1M blended tokens. Data provided by https://artificialanalysis.ai/

Claude Opus 4.8 is positioned for complex coding, agent workflows, and professional knowledge work in Anthropic’s release announcement. GPT-5 is positioned by OpenAI as a reasoning model for coding, reasoning, and agentic tasks in GPT-5 for developers.

The practical decision is not simply quality versus price. Claude offers a much stronger coding result in the supplied comparison, but GPT-5 has a strong published math result and a substantially lower API cost. Lifecycle status also matters: Claude Opus 4.8 remains Active, while the fixed GPT-5 snapshot is Deprecated, even though the gpt-5 alias remains listed in current documentation.

Claude Opus 4.8 is the better default for high-risk coding and agent workflows when review time costs more than model tokens. The comparison shows a 36.5-point coding-index advantage for Claude Opus 4.8 and a 21-point advantage on the Artificial Analysis intelligence index. Data provided by https://artificialanalysis.ai/

GPT-5 is the better default for high-volume workloads where unit economics dominate. Its blended price is $3.4375 per 1M tokens, compared with $10 for Claude Opus 4.8. GPT-5 also costs $1.25 per 1M input tokens and $10 per 1M output tokens, compared with $5 and $25 for Claude. Those differences affect every retry, evaluation pass, and background agent step.

The models do not have the same evidence profile. Claude leads the supplied coding and intelligence indices, while GPT-5 records a 94.3 math index and Claude has no corresponding value in the data snapshot. That does not prove GPT-5 is better at all mathematical work, because the available materials do not establish that the indices are directly comparable across every task type.

Claude’s official materials document a 1M-token context window, adaptive thinking, and effort levels through xhigh in the model overview and the effort documentation. GPT-5 documents a 400,000-token context window, image input, structured outputs, and tool calling in its model documentation.

Claude Opus 4.8 is the stronger evidence-backed option for repository-scale coding, but the available data cannot establish a universal production winner. Claude’s coding index is 74.3 versus GPT-5 at 37.8, a large enough gap to justify testing Claude first for implementation, refactoring, and multi-file debugging workflows. Data provided by https://artificialanalysis.ai/

That score difference should be interpreted as a prioritization signal, not a guarantee that every patch will be correct. Anthropic reports 84% on Online-Mind2Web and says Claude is less likely to let defects pass without prompting in its release announcement. Those are vendor-reported results and claims, so they should support a pilot design rather than replace your own acceptance tests.

Claude’s adaptive thinking may help on tasks whose difficulty is unclear. Its documented effort setting can range from low to xhigh, which gives developers a way to trade response depth against resource use. The effort documentation also states that effort is a behavioral signal, not a strict token budget. A low setting therefore cannot be treated as a precise latency ceiling.

GPT-5 remains attractive for targeted debugging and tool-driven changes. OpenAI reports 74.9% on SWE-bench Verified, 88% on Aider polyglot, 96.7% on τ²-bench telecom, and 69.6% on Scale MultiChallenge in GPT-5 for developers. These figures use different tasks and evaluation conditions, so they cannot be merged into a single ranking against Claude’s coding index.

Community evidence complicates the clean score-based story. One Claude user reports better self-correction and improved answer-length control, but also skipped steps in multi-step agents. Another commenter reports that adaptive thinking sometimes misses hidden difficulty. The Reddit discussion does not provide a reproducible test set. GPT-5 users report useful small fixes but possible hallucinations and incorrect edits in complex existing codebases. The GPT-5 Reddit thread also lacks controlled measurements.

The evidence gap is important: the materials provide no reliable, directly comparable production measurements for output speed, throughput, or stability. The supplied latency value is 0.3 seconds for each model, so latency is a tie in this snapshot, not a reason to choose either model.

GPT-5 is the lower-cost option, but Claude Opus 4.8 can still be cheaper at the workflow level when it reduces retries, review, or failed agent steps. GPT-5’s blended price is $3.4375 per 1M tokens, compared with $10 for Claude Opus 4.8. Data provided by https://artificialanalysis.ai/

The price gap matters most in repetitive workloads. Classification, extraction, short transformations, and routine tool calls can accumulate large token volumes without needing Claude’s higher coding score. GPT-5’s $1.25 input price also makes it attractive when prompts contain large repeated repositories, instructions, or retrieved documents.

The calculation changes when output quality controls downstream labor. A failed patch can trigger another model call, a human review cycle, a test run, and a rollback. The data snapshot does not quantify those operational costs, so no exact break-even point can be claimed. Developers should measure accepted patches per dollar, not token price alone.

Claude supports prompt caching, with a cache-hit price of $0.50 per 1M tokens, according to Anthropic’s pricing documentation. GPT-5 documents cached input at $0.125 per 1M tokens in its model documentation. GPT-5 remains cheaper on the listed token prices, but cache behavior, prompt reuse, cache lifetime, and implementation details determine the realized bill.

Adaptive effort adds another cost-control variable for Claude, but it is not a strict budget mechanism. A team that needs predictable spend should enforce application-level token limits, routing rules, and retry policies around either model.

Claude Opus 4.8 is the recommended first pilot for complex code changes where correctness and repository understanding matter most. Its 74.3 coding index versus GPT-5 at 37.8 makes it the stronger candidate for architectural changes, broad refactors, difficult debugging, and long-running agent tasks. Data provided by https://artificialanalysis.ai/

Choose GPT-5 when the workload is price-sensitive, repetitive, or centered on compact tool calls. Its lower blended price of $3.4375 per 1M tokens makes it a sensible choice for high-volume automation, routine bug fixes, content transformations, and applications where a human or deterministic test suite checks every result.

Choose Claude when context breadth is a major constraint. Anthropic documents a 1M-token context window in the model overview, while OpenAI documents 400,000 tokens for GPT-5 in the GPT-5 model page. Context size alone does not establish better retrieval or reasoning, but it can simplify repository and document workflows.

Choose GPT-5 for math-heavy workloads only after validating the task distribution. GPT-5 has a 94.3 math index in the supplied data, while Claude has no corresponding value. That is a meaningful signal, but the materials do not show the benchmark composition or establish how the math result transfers to your domain.

Treat lifecycle risk as part of the architecture. Claude Opus 4.8 is listed as Active with retirement no earlier than 2027-05-28 in Anthropic’s lifecycle documentation. The fixed GPT-5 snapshot is Deprecated in OpenAI’s documentation, although the gpt-5 alias remains available. Pin versions, monitor notices, and keep a migration test suite before committing deeply to either provider.

Claude Opus 4.8 is the safer starting hypothesis for demanding coding work, but a short task-specific bake-off remains necessary. The available research contains useful official claims and anecdotal reports, yet no directly comparable production test of correctness, speed, or stability.

Your evaluation should include accepted patch rate, test-pass rate, reviewer minutes, retry frequency, tool-call compliance, and cost per completed task. The comparison data supplies model scores, prices, and latency, but it does not supply those operational measures. GPT-5’s deprecated fixed snapshot also makes migration testing a release requirement.