GPT-4o mini vs Mi:dm K 2.5 Pro Preview: The Ultimate Performance & Pricing Comparison

Mi:dm K 2.5 Pro Preview leads the supplied benchmark comparison, while GPT-4o mini remains the more documented and operationally understandable choice. The data shows Mi:dm K 2.5 Pro Preview ahead on MMLU Pro at 0.813 versus GPT-4o mini at 0.648, and ahead on LiveCodeBench at 0.576 versus 0.234. Those results suggest a meaningful advantage for difficult reasoning and coding evaluations, but they do not prove that Mi:dm K 2.5 Pro Preview is easier to deploy.

GPT-4o mini has a documented API alias, a fixed model version, a 128,000-token context window, and a maximum output of 16,384 tokens in the official documentation: GPT-4o mini model documentation. OpenAI describes the model as a small model for frequent, low-cost tasks that accepts text and image input and produces text output: GPT-4o mini announcement.

The supplied research contains no verifiable manufacturer announcement, developer documentation, pricing page, or community discussion for Mi:dm K 2.5 Pro Preview. That gap is central to the decision. Data provided by Artificial Analysis can show the recorded evaluation results, but it cannot establish endpoint availability, authentication requirements, service stability, or the meaning of a recorded $0 price.

Mi:dm K 2.5 Pro Preview is the benchmark winner, but GPT-4o mini is the safer default for teams that need verified product behavior. The comparison is therefore not simply a stronger model versus a weaker model.

Mi:dm K 2.5 Pro Preview scores higher on every directly shared evaluation where the supplied data reports values for both models. The largest practical signal is LiveCodeBench, where Mi:dm K 2.5 Pro Preview records 0.576 and GPT-4o mini records 0.234. The gap suggests that Mi:dm K 2.5 Pro Preview may be more capable on challenging code-generation and code-reasoning tasks. It does not establish performance on a specific repository, language, toolchain, or production workload.

GPT-4o mini offers stronger evidence around how a developer can call and constrain the model. The official model page identifies gpt-4o-mini and gpt-4o-mini-2024-07-18, while documenting a 128,000-token context window and 16,384-token maximum output: GPT-4o mini model documentation. OpenAI’s announcement also reports 87.2% on HumanEval and 82.0% on MMLU at launch, but those figures are release benchmarks rather than guarantees for every application: GPT-4o mini announcement.

The current OpenAI model directory emphasizes the GPT-5 family and does not list GPT-4o mini’s current product positioning: OpenAI model directory. The current pricing page also does not list gpt-4o-mini, so its present direct-call price cannot be confirmed: OpenAI pricing page.

Mi:dm K 2.5 Pro Preview appears stronger on difficult reasoning and coding tests, but the evidence is too incomplete to establish a reliable production performance winner. Its 0.576 LiveCodeBench result versus 0.234 for GPT-4o mini is the clearest signal for developers evaluating code-heavy workloads. The 0.297 versus 0.229 result on SciCode points in the same direction, although the smaller gap suggests that the advantage may vary by task design.

The reasoning gap is also visible in GPQA, where Mi:dm K 2.5 Pro Preview records 0.722 and GPT-4o mini records 0.426. A developer building research assistants, technical analysis tools, or complex code-generation workflows may therefore want to test Mi:dm K 2.5 Pro Preview first. The test should use representative prompts, expected output formats, tool calls, and failure handling rather than relying on benchmark rankings alone.

GPT-4o mini still has useful evidence for more conventional application design. OpenAI reports launch scores of 87.0% on MGSM, 87.2% on HumanEval, and 59.4% on MMMU, while explicitly presenting them as benchmark results from the release announcement: GPT-4o mini announcement. The official model documentation confirms text and image input with text output, but does not establish native audio or video support: GPT-4o mini model documentation.

The supplied data reports 0 median output tokens per second and 0 seconds of latency for both models. That means speed is not a usable differentiator here. No verified community sources were found for either model’s coding experience, response feel, or recurring failure patterns.

Mi:dm K 2.5 Pro Preview has the lower recorded cost, but developers should treat the $0 figure as unverified availability data rather than a confirmed free service. The supplied comparison records $0 for input, output, and blended pricing for Mi:dm K 2.5 Pro Preview. It records GPT-4o mini at $0.15 per 1M input tokens, $0.60 per 1M output tokens, and $0.262 per 1M blended tokens.

That difference matters most for high-volume workloads with predictable request patterns. A recorded $0 price could make Mi:dm K 2.5 Pro Preview attractive for experimentation, batch processing, or internal prototypes. However, the research found no verifiable pricing page or official developer documentation for Mi:dm K 2.5 Pro Preview. The actual cost may depend on access restrictions, an indirect provider, preview limits, regional availability, or a private evaluation environment. The evidence does not identify which explanation applies.

GPT-4o mini’s launch pricing is documented by OpenAI as $0.15 per 1M input tokens and $0.60 per 1M output tokens: GPT-4o mini announcement. Its current direct-call price is still uncertain because the current OpenAI pricing page does not list gpt-4o-mini: OpenAI pricing page.

The cheaper model can become more expensive in engineering time if access, reliability, or migration behavior is unclear. Before choosing Mi:dm K 2.5 Pro Preview for production, confirm the endpoint, billing rules, rate limits, retention policy, and support path with a real account.

GPT-4o mini is the better default for production selection, while Mi:dm K 2.5 Pro Preview deserves a controlled evaluation for demanding reasoning and coding tasks. This recommendation reflects evidence quality as well as benchmark performance.

Choose GPT-4o mini when the team needs a known API identifier, a documented model version, and clear baseline limits. The official documentation identifies gpt-4o-mini-2024-07-18, a 128,000-token context window, and a 16,384-token maximum output. Those details make it easier to design request limits, regression tests, and model pinning: GPT-4o mini model documentation. GPT-4o mini is also a reasonable fit for frequent classification, extraction, summarization, and image-understanding tasks, consistent with OpenAI’s stated positioning: GPT-4o mini announcement.

Test Mi:dm K 2.5 Pro Preview when coding accuracy or hard reasoning is the main product risk. Its supplied results of 0.813 on MMLU Pro, 0.722 on GPQA, and 0.576 on LiveCodeBench are materially stronger than GPT-4o mini’s corresponding values. These results justify a task-specific bake-off, especially for code review, algorithmic reasoning, and technical question answering.

Do not make Mi:dm K 2.5 Pro Preview the default solely because its recorded price is $0. The research provides no verified public source for its API, context limit, output limit, support model, or continued availability. The current OpenAI model directory also shows that GPT-4o mini’s product positioning has shifted as OpenAI emphasizes newer models: OpenAI model directory.

Developers should resolve access and workload-fit questions before turning either benchmark result into a production decision. The available evidence supports a narrow recommendation, not a universal ranking.