GPT-5 mini (high) vs Grok 4.5 (high): The Ultimate Performance & Pricing Comparison

Grok 4.5 (high) is the stronger developer choice for coding and agentic work, while GPT-5 mini is the safer cost-first option. Artificial Analysis records a Coding Index of 72.4 for Grok 4.5 (high) and 15.6 for GPT-5 mini, with an Intelligence Index of 53.8 versus 25.3. Artificial Analysis identifies the measured Grok variant as high, while the current OpenAI model directory does not list gpt-5-mini as an independent current model entry. That difference matters before any benchmark score enters the discussion. Grok 4.5 has a documented API model name, current availability, tool support, and pricing. GPT-5 mini has attractive measured economics, but its present API identity and support status are not confirmed by the cited OpenAI pages. The practical decision is therefore not simply capability versus price. It is capability and operational clarity versus low measured cost and unresolved availability.

Grok 4.5 (high) offers the broader documented developer surface, while GPT-5 mini offers the lower measured price and a stronger math result in the available data. xAI’s developer documentation documents grok-4.5 as the API model and describes high as a reasoning_effort value rather than a separate model. The same documentation lists Function Calling, Structured Outputs, Web Search, X Search, and Code Execution. xAI’s model page lists text and image input, text output, a 500,000-token context window, and a stable model entry. By contrast, OpenAI’s model directory does not independently confirm GPT-5 mini’s context window, output limit, tools, or API parameters.

The measured data favors Grok 4.5 for general capability and coding. The pricing data favors GPT-5 mini decisively. The documentation favors Grok 4.5 because its current model name and integration features are explicit. The evidence does not establish whether GPT-5 mini’s lower price remains actionable in a new production integration.

Grok 4.5 (high) is the better-supported performance choice for software engineering and multi-step agent tasks. Artificial Analysis reports a Coding Index of 72.4 for Grok 4.5 (high), compared with 15.6 for GPT-5 mini, and an Intelligence Index of 53.8 compared with 25.3. Artificial Analysis is the cited provider benchmark source for the Grok variant and the comparison dataset supplied for this article.

The coding gap suggests a meaningful difference in task selection, especially for repository changes, debugging loops, terminal interactions, and tool-mediated implementation. It does not prove that Grok will win every code prompt. Benchmark indexes compress many task types into one score, so a team should still validate its own language mix, repository size, test discipline, and tool orchestration. The result is strongest as a routing signal: Grok deserves the first evaluation slot for complex engineering work, while GPT-5 mini deserves testing where prompts are short, repetitive, or highly cost-sensitive.

Grok 4.5 also has a documented service speed of about 80 tokens per second in xAI’s announcement, while the supplied Artificial Analysis snapshot records 61.802 median output tokens per second. GPT-5 mini has no corresponding output-speed value in the data snapshot. Both models show 0.3 seconds of latency in the supplied data, so time to first response does not separate them here. The evidence is insufficient to establish GPT-5 mini’s sustained generation speed, streaming behavior, or long-agent stability.

GPT-5 mini is the clear token-price choice, but Grok 4.5 (high) can be economically rational when stronger first-pass performance reduces retries and tool loops. The supplied data places GPT-5 mini at $0.6875 per 1M blended tokens, compared with $3 for Grok 4.5 (high). Its input price is $0.25 per 1M tokens versus $2, and its output price is $2 versus $6. xAI’s model page confirms the Grok list prices and notes that longer-context requests use a higher pricing tier. The exact higher-tier amount is not provided by the research material.

For classification, extraction, routing, summarization, and other high-volume calls, GPT-5 mini’s price advantage is difficult to ignore if the model is actually available under a stable API identity. Input-heavy workloads benefit especially from the $0.25 input rate. Output-heavy workloads still favor GPT-5 mini, but the economic advantage narrows relative to input processing because both models charge more for generated tokens.

The cost conclusion can reverse in agentic coding. A weaker first attempt may trigger additional calls, repairs, tests, and context transfer. The supplied data does not quantify retry rates, total tokens per completed task, or cost per successful change, so it cannot prove that Grok is cheaper per outcome. Teams should measure completed-task cost, not only token price. Grok’s documentation also recommends setting prompt_cache_key; without it, cache misses can cause full-price input billing. That operational detail is described in xAI’s developer documentation.

Grok 4.5 (high) should be the default candidate for complex coding agents, while GPT-5 mini should be the candidate for low-cost high-volume automation. Grok has the stronger supplied Coding Index, a documented model identifier, and explicit support for Function Calling, Structured Outputs, Web Search, X Search, and Code Execution. These capabilities are listed in xAI’s developer documentation. Its official announcement also positions the model for coding, agents, engineering, and knowledge work.Introducing Grok 4.5

Choose Grok 4.5 when a failed first attempt is costly, the workflow depends on tool calls, or the application needs a clearly documented current API surface. Its higher price is easier to justify when success per task matters more than raw token expenditure. Teams should include prompt caching and context compaction in the implementation design because xAI documents both concerns.

Choose GPT-5 mini when unit economics dominate, the workload is predictable, and a controlled test confirms that the model meets the application’s quality threshold. Its supplied Math Index of 90.7 is a notable reason to test it for mathematical workloads. However, OpenAI’s pricing page does not currently list GPT-5 mini, and OpenAI’s model directory does not confirm the model’s current API status. That evidence gap is material. A team should not commit production architecture around GPT-5 mini until it verifies an invocable model ID, current pricing, context limits, and tool behavior.

The best rollout is staged: benchmark both models on completed tasks, then compare successful-task cost and operational reliability. The supplied materials do not provide those production-level measurements, so the final winner remains workload-dependent.

Grok 4.5 (high) has the clearer documented production path, while GPT-5 mini requires direct availability and integration checks. OpenRouter also lists Grok 4.5 as x-ai/grok-4.5, but third-party availability does not replace validation with the intended provider. The following questions address the selection risks that the supplied benchmark values cannot answer alone.