Grok 4.7 vs. MiMo-V2.6-Pro: A Comparative Analysis

What the Benchmarks Show

Both Grok 4.7 and MiMo-V2.6-Pro, released on September 21, 2026, arrive with an identical intelligence index of 46.3. While this suggests a shared tier of general capability, their performance on specific benchmarks reveals distinct differences in technical aptitude. Across the HLE, SciCode, and LCR benchmarks, MiMo-V2.6-Pro consistently outperforms Grok 4.7. Specifically, MiMo-V2.6-Pro achieved an HLE score of 0.494 compared to Grok 4.7’s 0.423, and a SciCode score of 0.609 against Grok 4.7’s 0.578. The LCR benchmark further highlights this gap, with MiMo-V2.6-Pro scoring 0.863 compared to 0.77 for Grok 4.7. These results suggest that while the models may be equally "intelligent" in a general sense, MiMo-V2.6-Pro is better optimized for the specific tasks measured by these benchmarks.

Speed and Cost

The economic and performance profiles of these models present a clear trade-off for developers. MiMo-V2.6-Pro is significantly more affordable, with a blended pricing model of $0.54 per million tokens, compared to the $3.00 per million tokens charged for Grok 4.7. This represents a substantial cost advantage for high-volume users. However, Grok 4.7 compensates for its higher price with a faster time to first token, clocking in at 0.732 seconds compared to 1.344 seconds for MiMo-V2.6-Pro. If your application requires near-instantaneous feedback, the extra cost of Grok 4.7 may be justified. Conversely, for batch processing where total throughput is more critical than the latency of the first token, MiMo-V2.6-Pro’s output speed of 95.064 tokens per second—more than double the 38.606 tokens per second of Grok 4.7—makes it the more efficient choice.

Which Model Fits Which Workflow

Determining the right model requires an assessment of your project's specific constraints. Grok 4.7 is designed for scenarios where the speed of the initial interaction is paramount. Its lower time to first token makes it suitable for real-time conversational interfaces or interactive tools where user experience is tied to immediate responsiveness. The higher cost is a premium paid for this specific performance characteristic.

MiMo-V2.6-Pro is better suited for high-volume, compute-intensive tasks. Its superior benchmark scores across HLE, SciCode, and LCR indicate a higher level of proficiency in complex reasoning and technical tasks. When paired with its significantly lower cost and higher sustained output speed, it becomes the logical choice for data processing pipelines, automated research, or large-scale content generation where budget and throughput are the primary drivers of success.

Decision Takeaway

Ultimately, neither model is objectively superior in every category. The decision rests on whether your priority is minimizing the latency of the first response or maximizing the value and speed of the total output. By aligning these technical metrics with your specific use case, you can select the model that best fits your operational requirements.