GLM-5-Turbo vs GPT-5 mini (high): The Ultimate Performance & Pricing Comparison

GLM-5-Turbo leads the measured intelligence comparison, while GPT-5 mini (high) is dramatically cheaper and has stronger documented math evidence.

The supplied benchmark data gives GLM-5-Turbo an Artificial Analysis Intelligence Index of 38.1, compared with 25.3 for GPT-5 mini (high). GPT-5 mini (high) has a Math Index of 90.7 and a Coding Index of 15.6, while no corresponding coding or math values are supplied for GLM-5-Turbo. The data therefore supports a narrow intelligence advantage for GLM-5-Turbo, but it does not establish a broad advantage across every developer workload.

GPT-5 mini (high) costs $0.6875 per 1M blended tokens, compared with $15 for GLM-5-Turbo. Both models have a recorded latency of 0.3 seconds. Neither model has a supplied median output speed, so the data does not show that either model generates tokens faster.

The larger selection risk is identity and access. The supplied research could not verify a current official product page, API entry, stable alias, context window, or official benchmark for GLM-5-Turbo. The current OpenAI model directory and pricing page also do not list gpt-5-mini or GPT-5 mini (high), so access must be verified before implementation. OpenAI Models OpenAI Pricing

Data provided by https://artificialanalysis.ai/

GPT-5 mini (high) is the safer cost choice, while GLM-5-Turbo is the measured intelligence choice under incomplete product evidence.

The key distinction is evidence quality, not simply model quality. GLM-5-Turbo has the higher intelligence score, but the supplied research contains no reliable official or community material describing how developers can call it or where it fails. GPT-5 mini (high) has more relevant OpenAI documentation references, yet the current official pages do not independently confirm the model name or its high setting. OpenAI Models OpenAI Pricing

A developer choosing for math-heavy workflows can point to the 90.7 Math Index for GPT-5 mini (high). A developer choosing for a broader measured intelligence signal can point to GLM-5-Turbo's 38.1 score. Neither score proves production reliability, because the brief supplies no task-level methodology, context-window evidence, output-limit evidence, or verified failure analysis for the two named configurations.

The practical conclusion is conditional: validate access and behavior first, then choose based on whether intelligence score, math evidence, or token economics dominates the workload.

GLM-5-Turbo has the stronger supplied general intelligence score, but GPT-5 mini (high) is the only model with supplied coding and math results.

GLM-5-Turbo's Intelligence Index is 38.1, while GPT-5 mini (high) records 25.3. That result supports GLM-5-Turbo as the measured leader on the available general index. The difference does not identify which model is better at repository changes, tool calling, debugging, structured extraction, or long-context work. The supplied research found no reliable public testing record for GLM-5-Turbo, so no qualitative failure pattern can be attached to the score.

GPT-5 mini (high) has a Math Index of 90.7 and a Coding Index of 15.6. These values give developers concrete signals for math-oriented evaluation and a limited coding signal. They do not provide a direct coding comparison because no GLM-5-Turbo coding value is supplied. They also do not establish that GPT-5 mini (high) is preferable for every coding task.

Latency does not separate the models in the supplied data. GLM-5-Turbo and GPT-5 mini (high) are both recorded at 0.3 seconds. Median output speed is null for both models, so a developer cannot infer streaming throughput, time to completion, or token generation behavior from this snapshot.

The unresolved performance questions are substantial. Neither model has a supplied context window. The research also does not verify output limits, tool support, multimodal behavior, API parameters, or stable model identifiers. For GPT-5 mini (high), the current OpenAI directory describes current models in general terms, but it does not confirm that those capabilities apply to this historical or displayed configuration. OpenAI Models

The score-based decision should therefore be narrow. Use GLM-5-Turbo as the candidate for broader measured intelligence, and use GPT-5 mini (high) as the candidate with supplied math and coding evidence. Run task-specific tests before treating either result as a production performance conclusion.

GPT-5 mini (high) is the clear cost leader, but its advantage matters only if the model can be accessed under a stable, usable API arrangement.

The supplied data lists GPT-5 mini (high) at $0.25 per 1M input tokens and $2 per 1M output tokens. GLM-5-Turbo is listed at $10 per 1M input tokens and $30 per 1M output tokens. The blended 3-to-1 figure is $0.6875 for GPT-5 mini (high) and $15 for GLM-5-Turbo. The chart makes the price difference visible, but the operational implication is more important: workloads with frequent prompts, repeated retries, large outputs, or automated agent steps will expose the price gap quickly.

Token price alone does not determine total cost. A cheaper model can become more expensive if it requires additional retries, produces unusable code, needs a stronger fallback, or cannot support the required context and tools. The supplied research provides no verified failure rates, coding anecdotes, context limits, or tool-support details for either model. Evidence is therefore insufficient to estimate cost per successful task.

The output price deserves special attention for developer agents. GPT-5 mini (high) has a listed output price of $2 per 1M tokens, while GLM-5-Turbo has a listed output price of $30. Long answers, generated patches, test explanations, and repeated planning steps are all output-heavy activities. The data supports a strong economic preference for GPT-5 mini (high) in those workloads, subject to access verification.

The official OpenAI pricing page does not list gpt-5-mini in the supplied research, so the benchmark price should not be treated as a confirmed current OpenAI tariff for the named API configuration. OpenAI Pricing The supplied research contains no official GLM-5-Turbo pricing page or API entry either.

The right cost test is successful-task cost. Measure useful completion, retry frequency, latency under load, and fallback usage. The current data supports GPT-5 mini (high) as the economic default, but it cannot prove which model has the lower cost after quality failures.

GPT-5 mini (high) is the default recommendation for cost-sensitive development, while GLM-5-Turbo deserves a controlled trial for workloads aligned with its higher intelligence score.

Choose GPT-5 mini (high) when token economics are the primary constraint. Its blended price is $0.6875 per 1M tokens, and the supplied data includes a Math Index of 90.7 plus a Coding Index of 15.6. Those signals make it the more defensible starting point for experiments involving mathematical reasoning or coding evaluation, although the coding comparison remains incomplete because GLM-5-Turbo has no supplied coding value.

Choose GLM-5-Turbo when your evaluation is centered on the supplied general intelligence signal. Its Intelligence Index of 38.1 is higher than GPT-5 mini (high)'s 25.3. That evidence justifies a benchmark track for broad reasoning tasks, but it does not justify assuming better code editing, tool use, context handling, or production stability.

Use a pilot rather than a direct production commitment when model identity is uncertain. The research found no verifiable official release announcement, developer documentation, product page, API entry, stable alias, or community testing record for GLM-5-Turbo. No source in the brief confirms its context window, output limit, parameters, multimodal support, or failure modes.

GPT-5 mini (high) also requires an access check. The current OpenAI model directory does not list gpt-5-mini or GPT-5 mini (high), and the pricing page does not list its standard, Batch, Flex, or Fast mode prices. OpenAI Models OpenAI Pricing The name high is not confirmed as an independent model identifier or as an API reasoning parameter value in the supplied official material.

A sensible selection gate is simple: verify the callable model ID, confirm context and output limits, replay representative tasks, record successful completion cost, and compare fallback rates. The current evidence selects GPT-5 mini (high) for economics and supplied math evidence, and GLM-5-Turbo for the general intelligence hypothesis. It does not establish a universal winner.

GPT-5 mini (high) is the stronger first candidate for developers who need a low listed token price and supplied math evidence.

The supplied research does not confirm that either named configuration is currently callable under the exact displayed name. Verify access before building integration assumptions.