Released simultaneously on September 21, 2026, SpaceXAI’s Grok 4.7 and Xiaomi’s MiMo-V2.6-Pro represent two distinct approaches to AI performance. While Grok 4.7 maintains a slight edge in general intelligence, MiMo-V2.6-Pro offers superior benchmark results and significantly lower operational costs, forcing users to weigh raw intelligence against efficiency and budget constraints.
What the benchmarks show
When evaluating the technical capabilities of Grok 4.7 and MiMo-V2.6-Pro, the data reveals a nuanced landscape. Grok 4.7 holds a marginal lead in the Intelligence Index, scoring 46.4 compared to MiMo-V2.6-Pro’s 46.3. This suggests that for tasks requiring high-level reasoning or general knowledge, Grok 4.7 may provide a slight advantage. However, this lead does not translate into broader performance metrics.
In standardized testing, MiMo-V2.6-Pro consistently outperforms Grok 4.7. Across the HLE, SciCode, and LCR benchmarks, the MiMo-V2.6-Pro model demonstrates higher proficiency. Specifically, its LCR score of 0.863 significantly eclipses the 0.767 score achieved by Grok 4.7. While the Intelligence Index provides a high-level overview, the benchmark data suggests that MiMo-V2.6-Pro is more capable when applied to specialized scientific and logical reasoning tasks, despite the near-identical intelligence ratings.
Speed and cost
The most striking divergence between these two models lies in their economic and operational profiles. MiMo-V2.6-Pro is positioned as a high-efficiency model, with a blended pricing structure of $0.54 per million tokens. In contrast, Grok 4.7 is significantly more expensive, costing $3.00 per million tokens—nearly six times the price of the Xiaomi offering. For organizations processing large datasets or maintaining high-frequency API calls, this price disparity will likely be the deciding factor.
Operational speed presents a different set of tradeoffs. MiMo-V2.6-Pro is faster in terms of raw output, delivering 95.064 tokens per second. However, it suffers from a higher time-to-first-token latency of 1.344 seconds. Grok 4.7 is slower in total output speed at 44.37 tokens per second, but it is more responsive at the start of a request, with a time-to-first-token of only 0.67 seconds. Users who prioritize immediate feedback in conversational interfaces may find Grok 4.7’s responsiveness preferable, while those prioritizing throughput will favor the speed of MiMo-V2.6-Pro.
Which model fits which workflow
Selecting the appropriate model requires an assessment of your specific operational priorities. Grok 4.7 is best suited for applications where the absolute highest intelligence index is required and where the initial response time is critical to the user experience. Its lower latency at the start of generation makes it a strong candidate for interactive, real-time chat applications where the user expects an immediate response.
Conversely, MiMo-V2.6-Pro is designed for high-volume, performance-heavy workflows. Its superior benchmark scores across scientific and logical domains make it better suited for complex data analysis, automated coding tasks, or research-heavy applications. Because it is substantially more cost-effective, it is the clear choice for scaling operations where token consumption is a primary driver of infrastructure costs. While it is slightly slower to begin generating text, its high sustained throughput makes it ideal for batch processing and large-scale document analysis.
Verdict
The choice between these models depends on your specific tolerance for latency and cost. If your workflow demands the highest possible intelligence index and you are less concerned with budget, Grok 4.7 is the logical choice. However, for most production environments, MiMo-V2.6-Pro is the superior option. It delivers higher benchmark performance across all measured categories at a fraction of the cost, making it the more pragmatic tool for high-volume, cost-sensitive AI applications.
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