Grok-1 vs Mi:dm K 2.5 Pro Preview: The Ultimate Performance & Pricing Comparison

Mi:dm K 2.5 Pro Preview is the safer evidence-based choice, while Grok-1 remains too poorly documented for a confident production recommendation.

The supplied data gives Mi:dm K 2.5 Pro Preview measurable results across reasoning, mathematics, coding-related evaluation, instruction following, and tool-oriented tasks. Grok-1 has only one reported score, 5.8 on the Artificial Analysis Intelligence Index. That does not establish a broad capability advantage or disadvantage.

The larger issue is not simply model quality. Neither model has a confirmed current context window, output limit, API parameter set, multimodal profile, or current official price in the supplied research. The official xAI model directory currently points general coding and other tasks toward Grok 4.6, and it does not list Grok-1 as a current model. See the xAI model documentation.

Data provided by https://artificialanalysis.ai/

The practical conclusion is simple: Mi:dm K 2.5 Pro Preview has more decision evidence, but its production access is also unverified. Treat this comparison as a screening result, then confirm availability and run a task-specific trial before committing.

Mi:dm K 2.5 Pro Preview offers the stronger documented case, but neither model has enough verified operational information for an unconditional production decision.

Mi:dm K 2.5 Pro Preview records 78.7 on the Artificial Analysis Math Index, 0.813 on MMLU Pro, 0.722 on GPQA, and 0.576 on LiveCodeBench. Those results suggest useful evidence for mathematical reasoning, broad knowledge work, difficult question answering, and software tasks. They do not prove that the model will perform well on a specific codebase, agent workflow, or tool integration.

Grok-1 has a reported Artificial Analysis Intelligence Index score of 5.8, but the supplied data contains no coding index, mathematics index, MMLU Pro result, GPQA result, LiveCodeBench result, or tool-use result for Grok-1. The missing values prevent a fair capability comparison. Grok-1 may be better or worse on individual tasks, but the supplied evidence cannot establish that.

The documentation gap changes the buying decision. xAI’s current model page does not provide a verified Grok-1 access path, stable alias, current price, limits, or failure guidance. Mi:dm K 2.5 Pro Preview has no verified official documentation in the supplied research either. Therefore, Mi:dm K 2.5 Pro Preview wins on available evidence, not on confirmed production readiness.

For a developer, the selection order is evidence first, access second, then task quality. If either model fails the access check, the benchmark comparison becomes academic.

Mi:dm K 2.5 Pro Preview has the more useful performance record, but the evidence still cannot show which model is faster or better for your application.

The available results cover several different kinds of work. Mi:dm K 2.5 Pro Preview scores 0.813 on MMLU Pro and 0.722 on GPQA, which supports a case for general reasoning and difficult question answering. Its 78.7 Artificial Analysis Math Index score and 0.786666666666667 on AIME 25 indicate meaningful mathematical evaluation coverage. These results are relevant when your product needs structured reasoning, technical explanation, or quantitative problem solving.

The coding evidence is narrower. Mi:dm K 2.5 Pro Preview scores 0.576 on LiveCodeBench and 0.297 on SciCode. Those numbers provide more information than the empty coding fields for Grok-1, but they do not answer whether it will edit an unfamiliar repository safely, preserve existing behavior, or complete a long engineering task. The TerminalBench Hard result is 0.0303030303030303, which is a warning against assuming strong autonomous terminal performance from the other scores.

The tool-oriented results also look mixed. Mi:dm K 2.5 Pro Preview records 0.494152046783626 on tau2, 0.45578231292517 on IFBench, and 0.13 on LCR. That combination suggests that instruction following and tool interaction should be tested directly, especially when errors create operational or financial consequences.

Grok-1’s 5.8 Artificial Analysis Intelligence Index result is not enough to infer relative quality. No comparable Grok-1 scores are supplied for the other tasks. The supplied research also found no reliable community tests for Grok-1’s coding quality, speed, long-task stability, or tool calling. Both models show 0 median output tokens per second and 0 latency seconds in the supplied data. Those entries do not establish a real-world speed winner.

Grok-1 and Mi:dm K 2.5 Pro Preview are tied at $0 per 1M blended tokens, but the supplied evidence does not prove that either model is currently free or callable.

The zero price should be read as missing commercial evidence, not as a dependable operating cost. The research found no current official Grok-1 price and no verified access path. It also found no verifiable official pricing material for Mi:dm K 2.5 Pro Preview. A developer cannot build a reliable budget from either entry without confirming the actual endpoint, account requirements, quotas, and billing terms.

The cost conclusion can reverse quickly in a real project. A nominally free model becomes expensive if access is unstable, requests require manual handling, outputs need repeated retries, or the model cannot complete the task without additional orchestration. A paid model can be cheaper overall if it reduces retries, review time, failed tool calls, or migration work. The supplied material does not contain those operational measurements.

The same limitation applies to input and output pricing. Both models are listed at $0 per 1M input tokens and $0 per 1M output tokens. That is useful for identifying a data gap, not for choosing a vendor. Developers should confirm commercial terms directly before estimating monthly spend.

For procurement, record the price as unverified rather than free. Ask for a current endpoint, stable model name, usage limits, and a billable rate. If the provider cannot answer those questions, cost is not the only risk. Availability and continuity become the larger concern.

Mi:dm K 2.5 Pro Preview is the better first candidate for evaluation-heavy work, while Grok-1 should remain an exploratory option until its current access is confirmed.

Choose Mi:dm K 2.5 Pro Preview for a controlled trial when your workflow depends on reasoning, mathematical analysis, technical questions, or software assistance. Its available results give you concrete starting points: 0.813 on MMLU Pro, 0.576 on LiveCodeBench, and 0.494152046783626 on tau2. These scores do not guarantee success, but they let your team compare real tasks against a measurable baseline.

Choose Grok-1 only when you have a specific existing access path or historical workload that makes it relevant. The supplied research could not confirm a current official Grok-1 endpoint, stable API alias, current price, context window, or replacement relationship. The xAI model documentation currently emphasizes Grok 4.6 and does not provide the missing Grok-1 details. That creates continuity risk for a new integration.

Do not select either model solely because the listed price is $0. Do not select Mi:dm K 2.5 Pro Preview solely because it has more benchmark results. The research contains no direct head-to-head test, no verified latency comparison, no confirmed context limit, and no reliable community report for either model’s current coding experience.

A sensible developer decision is to test the exact prompts and tools your product uses. Measure successful task completion, retry frequency, review effort, and behavior after tool errors. Keep the model unapproved for production until access, limits, pricing, and output quality are all confirmed.

Mi:dm K 2.5 Pro Preview deserves the first technical trial, but the supplied evidence leaves important production questions unanswered.

The biggest unanswered question is availability. The research does not verify whether Mi:dm K 2.5 Pro Preview is directly callable, and the xAI documentation does not confirm a current Grok-1 integration path. The second unanswered question is operational behavior. Neither model has verified context, output, parameter, multimodal, latency, or failure-mode documentation in the supplied material.

Developers should therefore separate two decisions. The first is which model has stronger public evidence. That answer is Mi:dm K 2.5 Pro Preview. The second is which model can safely support the intended product. The supplied research does not answer that question for either model.

Run a small task-based trial only after confirming access and commercial terms. Use representative code, reasoning, and tool workflows. Keep the evaluation narrow enough that the team can inspect every failure.