Claude Opus 4.5 vs GPT-5: Which API Model Should Developers Choose?

GPT-5 (high) is the stronger default for developers because it matches Claude Opus 4.5 on the Artificial Analysis Intelligence Index, leads on the Math Index at 94.3, and costs $3.4375 versus $10 per 1M blended tokens. The performance chart does not establish an output-speed winner because both models have null output-speed values, while their latency is tied at 0.3 seconds. GPT-5 also exposes documented reasoning controls, multimodal input, structured outputs, and tool-calling features through a named API model. Claude Opus 4.5 remains a reasonable candidate for teams already aligned with Anthropic, but the supplied evidence does not establish a Claude-specific capability advantage. Data provided by https://artificialanalysis.ai/

GPT-5 (high) offers the clearer selection case because its documented API surface and measured math result are stronger than the evidence available for Claude Opus 4.5. OpenAI describes GPT-5 as a reasoning model for coding, reasoning, and agentic tasks, with reasoning_effort and verbosity controls documented for developers (GPT-5 for developers). Its model documentation lists a 400,000-token context window, a 128,000-token maximum output, text and image input, and text output (GPT-5 model documentation).

Claude Opus 4.5 has a lower evidence ceiling in this brief. Anthropic’s current model overview does not provide model-specific specifications for Claude Opus 4.5, so the supplied sources cannot verify its context window, output limit, API parameters, or visual capabilities (Anthropic model overview). The pricing page still lists Claude Opus 4.5 at $5 input and $25 output per MTok, but its absence from the current model overview creates a version-support question (Anthropic pricing).

The measured comparison is not universally decisive. The Intelligence Index is tied at 34.7, and the brief provides no comparable Claude coding score. GPT-5’s 37.8 Coding Index therefore describes GPT-5’s measured result, not a verified head-to-head coding win.

GPT-5 (high) has the stronger measured capability signal because its Math Index is 94.3, while Claude Opus 4.5 records 62.7, but the evidence does not prove a universal task winner. The gap is most relevant for workloads where mathematical consistency affects implementation quality, such as formula-heavy transformations, constraint checking, algorithm design, or tool-mediated decisions. It should not be converted into a blanket claim about code quality because the supplied benchmark data does not provide a comparable Claude coding score.

The tied Intelligence Index at 34.7 suggests that a broad capability label does not separate these models. Developers should therefore test their own request mix instead of treating the overall index as a complete product decision. A coding assistant that edits small, well-scoped changes may behave differently from an agent that must understand a large repository, preserve conventions, and execute several tools.

GPT-5’s documented controls make that testing easier to structure. Developers can choose reasoning_effort from minimal, low, medium, or high, and can control verbosity (GPT-5 for developers). GPT-5 also supports function calling, structured outputs, streaming, and custom tools with grammar constraints (GPT-5 model documentation).

Latency is tied at 0.3 seconds, so neither model earns a responsiveness advantage from the supplied data. Output speed remains unresolved because the brief reports null median output tokens per second for both models. The evidence is also insufficient to rank real-world coding speed, UI quality, or complex-repository reliability. A Reddit report describes GPT-5 as useful for small debugging tasks but less complete for full applications, with possible incorrect edits in complex repositories. That report is a single, uncontrolled experience, not a benchmark (Reddit experience report).

GPT-5 (high) is the economic default because its blended price is $3.4375 per 1M tokens, compared with $10 for Claude Opus 4.5, while the supplied latency is tied at 0.3 seconds. That price difference matters most in high-volume assistants, repository indexing, automated evaluations, and agent loops where every additional request compounds operating cost.

Claude Opus 4.5 may still be cheaper for a particular workload if it completes the task in fewer attempts, produces fewer retries, or avoids expensive human review. The supplied evidence does not measure those factors, so the price chart alone cannot establish total cost of ownership. A team should compare successful task completion, retry frequency, tool-call count, and review time on representative prompts.

The input-output mix also matters. Anthropic lists Claude Opus 4.5 at $5 input and $25 output per MTok, while GPT-5 is listed at $1.25 input and $10 output per 1M tokens (Anthropic pricing; GPT-5 model documentation). GPT-5 is therefore less expensive on both sides of the request in the supplied pricing data. Claude’s cache pricing introduces another condition: five-minute cache writes cost $6.25 per MTok, one-hour cache writes cost $10 per MTok, and cache reads cost $0.50 per MTok (Anthropic pricing).

Endpoint choice can also reverse a narrow cost assumption. Anthropic states that regional and multi-region endpoints for the Claude 4.5 series add 10% over the global endpoint (Anthropic pricing). The brief does not provide equivalent endpoint adjustments for GPT-5, so deployment-region economics remain incomplete rather than fully comparable.

GPT-5 (high) is the recommended starting point for most new developer-facing API integrations because it combines the lower $3.4375 blended price with a 94.3 Math Index and a documented tool-oriented API surface. It is the better fit for structured automation, mathematical workloads, multimodal text-and-image applications, and systems that need explicit control over reasoning intensity and response verbosity.

Choose Claude Opus 4.5 when Anthropic compatibility, existing prompt investment, or organizational familiarity carries more value than the measured cost advantage. That choice should begin with a validation plan because the supplied official overview does not expose Claude-specific context, output, parameter, or vision details. The brief also does not provide a verified Claude coding benchmark, output-speed measurement, or reliable community consensus.

Treat model identity as part of the architecture. OpenAI documents gpt-5 as a stable alias and lists gpt-5-2025-08-07 as a fixed snapshot, but the snapshot is marked Deprecated and the page recommends GPT-5.6 (GPT-5 model documentation). Anthropic’s pricing page still lists Claude Opus 4.5, while the model overview no longer lists it (Anthropic model overview; Anthropic pricing). Neither situation supports assuming long-term version stability without checking the provider before deployment.

The practical decision is simple: start with GPT-5 (high), then run a task-level bake-off before committing. Keep Claude Opus 4.5 as a targeted alternative if it demonstrates fewer retries or better review outcomes on the team’s actual workload.

GPT-5 (high) is the safer default for a new integration, but the evidence still leaves important workload-specific questions unanswered. The following checks address the gaps that the supplied benchmarks and official pages cannot resolve.