GPT-4o (Nov '24) vs Mi:dm K 2.5 Pro Preview: The Ultimate Performance & Pricing Comparison

Mi:dm K 2.5 Pro Preview leads the available benchmark snapshot, but GPT-4o (Nov '24) is the safer documented platform choice only in a limited sense.

The comparison is difficult because the evidence is uneven. The data snapshot records Mi:dm K 2.5 Pro Preview with stronger results on several reasoning, instruction-following, and coding-oriented evaluations. It also records a blended price of $0 per 1M tokens, compared with $4.375 for GPT-4o (Nov '24). However, the research brief found no verifiable official documentation or community testing for Mi:dm K 2.5 Pro Preview.

GPT-4o (Nov '24) has a clearer vendor identity and sits within the OpenAI product family. OpenAI’s current model documentation describes broad platform capabilities such as text and image input, text output, multilingual support, the Responses API, and official SDK access, but it does not provide version-specific evidence for GPT-4o (Nov '24) in the supplied material. OpenAI Models

Developers should therefore treat this as a decision under incomplete information. The benchmark leader is not automatically the deployable winner. A model that cannot be verified, called reliably, or governed in production may cost more engineering time than its listed token price suggests.

Data provided by https://artificialanalysis.ai/

Mi:dm K 2.5 Pro Preview looks stronger on the recorded tests, while GPT-4o (Nov '24) has more identifiable platform ownership but unresolved current-status questions.

The clearest performance signal favors Mi:dm K 2.5 Pro Preview. It scores 0.813 on MMLU Pro versus 0.748 for GPT-4o (Nov '24), 0.722 on GPQA versus 0.543, and 0.576 on LiveCodeBench versus 0.309. The same direction appears on AIME 2025, where Mi:dm K 2.5 Pro Preview records 0.786666666666667 and GPT-4o (Nov '24) records 0.06. These results suggest a meaningful advantage for difficult reasoning and coding tasks in this snapshot.

The result is not universal. GPT-4o (Nov '24) records 0.333 on SciCode versus 0.297 for Mi:dm K 2.5 Pro Preview, and 0.0833333333333333 on TerminalBench Hard versus 0.0303030303030303. Those two results warn against describing Mi:dm K 2.5 Pro Preview as better for every developer workflow.

The commercial comparison also appears simple but is not fully settled. The snapshot records Mi:dm K 2.5 Pro Preview at $0 for input, output, and blended pricing. The research brief found no verifiable pricing page, so the meaning of that $0 value is not confirmed. OpenAI’s current pricing page does not list GPT-4o, leaving its current official price unresolved. OpenAI Pricing

For a real product decision, benchmark fit should come first, then access verification, then a task-specific pilot. The supplied evidence does not prove that either model is currently available through a stable production API.

Mi:dm K 2.5 Pro Preview shows the stronger recorded general benchmark profile, but GPT-4o (Nov '24) retains narrow advantages that matter for tool-driven development.

The largest visible separation is in the Artificial Analysis math index, where Mi:dm K 2.5 Pro Preview records 78.7 and GPT-4o (Nov '24) records 6. The same broad pattern appears in GPQA, MMLU Pro, AIME 2025, and LiveCodeBench. For developers, that combination points toward better performance on hard questions, mathematical reasoning, and coding tasks represented by those evaluations.

The practical meaning depends on the workload. A higher coding benchmark can help with code generation, debugging explanations, and algorithmic tasks, but it does not establish repository-level reliability. The research brief found no verified test method or community evidence for Mi:dm K 2.5 Pro Preview’s coding experience, speed, or failure patterns. It also found no official failure-mode documentation for GPT-4o (Nov '24). Neither model therefore has a documented safety margin for production code changes.

Mi:dm K 2.5 Pro Preview also records 0.494152046783626 on tau2 versus 0.251461988304094 for GPT-4o (Nov '24). If the target product involves multi-step tool use, that gap is relevant because it suggests a stronger result on the recorded interaction evaluation. The evidence still does not tell us whether the model supports the required tools, API format, authentication, or structured-output behavior.

GPT-4o (Nov '24) leads on SciCode at 0.333 versus 0.297 and TerminalBench Hard at 0.0833333333333333 versus 0.0303030303030303. Those counterexamples matter for developers working with scientific code or terminal-style tasks. The correct conclusion is a workload split: Mi:dm K 2.5 Pro Preview has the stronger broad snapshot, while GPT-4o (Nov '24) remains competitive on selected execution-oriented tests.

Both models are recorded at 0 median output tokens per second and 0 latency seconds. That is a tie in the supplied data, not proof that real requests complete instantly. The snapshot does not provide usable speed evidence for choosing between them.

Mi:dm K 2.5 Pro Preview is cheaper in the supplied snapshot, but its recorded $0 price cannot yet support a production budget.

The data records Mi:dm K 2.5 Pro Preview at $0 per 1M input tokens, $0 per 1M output tokens, and $0 per 1M blended tokens. GPT-4o (Nov '24) is recorded at $2.5 per 1M input tokens, $10 per 1M output tokens, and $4.375 per 1M blended tokens. On the numbers alone, Mi:dm K 2.5 Pro Preview is the clear cost winner.

The missing piece is commercial meaning. The research brief found no verifiable official pricing page, developer documentation, or release announcement for Mi:dm K 2.5 Pro Preview. A zero in an aggregator snapshot could represent a free offering, unavailable pricing, a preview with no listed charge, or missing commercial data. The supplied evidence does not identify which explanation is correct.

GPT-4o (Nov '24) has the opposite problem. OpenAI’s current pricing directory does not list gpt-4o, so the supplied official page cannot confirm a current price or continued availability. OpenAI Pricing The data snapshot still records historical or observed prices, but developers should not treat them as a live quote without checking access in the intended account and region.

A cheaper model can become more expensive when it requires a custom gateway, unstable endpoint, extra retries, manual quality review, or migration work. A more expensive model can become cheaper at the product level if it reduces failed tool calls or human correction. The available evidence cannot quantify those effects.

The right budget test is small and concrete: verify each model’s endpoint, billing behavior, output limits, and failure handling on the exact application workflow. Until then, Mi:dm K 2.5 Pro Preview is the snapshot cost winner, not a confirmed zero-cost production option.

Mi:dm K 2.5 Pro Preview is the first model to test for difficult reasoning and coding workloads, while GPT-4o (Nov '24) is the fallback candidate when OpenAI platform access is already established.

Choose Mi:dm K 2.5 Pro Preview first if the product’s success depends on reasoning-heavy answers, coding assistance, mathematical work, or multi-step interaction. Its recorded results are higher on MMLU Pro at 0.813, GPQA at 0.722, LiveCodeBench at 0.576, AIME 2025 at 0.786666666666667, and tau2 at 0.494152046783626. The case is strongest when those benchmark-shaped tasks resemble the product’s real requests.

Choose GPT-4o (Nov '24) only after confirming that the exact model identifier can still be called. OpenAI’s current model directory does not list gpt-4o, and the supplied research found no stable alias or version-specific announcement. OpenAI Models The same research also found no verified current price for this model on OpenAI’s pricing page. OpenAI Pricing

GPT-4o (Nov '24) deserves a targeted test when SciCode-like work or TerminalBench Hard-like terminal tasks are central. It records 0.333 on SciCode and 0.0833333333333333 on TerminalBench Hard, compared with 0.297 and 0.0303030303030303 for Mi:dm K 2.5 Pro Preview. Those results do not prove production superiority, but they identify a sensible reason to keep GPT-4o in the trial.

Do not make the final choice from the benchmark leader or the $0 listing alone. The research brief found no reliable community discussions, verified failure cases, or official technical materials for Mi:dm K 2.5 Pro Preview. It also found no version-specific official capability or limitation details for GPT-4o (Nov '24). The decisive missing evidence is deployability: stable access, supported API behavior, billing, limits, and repeatable quality on your own test set.

A practical decision rule is simple: test Mi:dm K 2.5 Pro Preview for quality first, retain GPT-4o (Nov '24) as a comparison candidate, and reject either model if access or operational behavior cannot be verified.

Mi:dm K 2.5 Pro Preview deserves the first quality test, but developers should verify access and billing before treating its benchmark lead as a deployment decision.

The available evidence supports a cautious comparison. Quantitative results come from the supplied Artificial Analysis snapshot. Product-status conclusions come from the supplied research brief and the linked OpenAI documentation. No direct, verifiable source was found for Mi:dm K 2.5 Pro Preview, so several practical questions remain unanswered.