Grok 4.6 vs. GPT-5.6 Sol: A Comparative Analysis

What the benchmarks show

When evaluating the intelligence of Grok 4.6 and GPT-5.6 Sol, both models arrive at an identical intelligence index of 59. However, a deeper look into specific domain benchmarks reveals nuanced differences in their capabilities. GPT-5.6 Sol demonstrates a slight edge in technical performance, recording a coding index of 78.3 compared to Grok 4.6’s 74.4. This trend continues across several specialized benchmarks, where GPT-5.6 Sol achieves higher scores in HLE (0.473 vs. 0.421), SciCode (0.56 vs. 0.546), and LCR (0.763 vs. 0.727).

While Grok 4.6 performs admirably, particularly with a GPQA score of 0.935—marginally higher than GPT-5.6 Sol’s 0.931—the latter offers a broader suite of validated metrics, including TerminalBench Hard and TAU2. For users requiring specialized reasoning in complex coding or scientific environments, GPT-5.6 Sol provides a more robust performance profile, whereas Grok 4.6 remains highly competitive in general-purpose intelligence tasks.

Speed and cost

The economic disparity between these two models is substantial. Grok 4.6 is priced at a blended rate of $3.00 per million tokens, with input costs at $2.00 and output at $6.00. In contrast, GPT-5.6 Sol carries a significantly higher price tag, with a blended rate of $11.25 per million tokens, driven by a $30.00 output cost. For organizations processing massive datasets, the cost-to-performance ratio of Grok 4.6 is objectively more favorable.

However, this cost difference is reflected in the operational speed of the models. GPT-5.6 Sol delivers a faster output speed of 70.021 tokens per second, compared to Grok 4.6’s 63.864 tokens per second. Furthermore, GPT-5.6 Sol exhibits a lower time-to-first-token latency of 25.458 seconds, versus 30.506 seconds for Grok 4.6. Users must decide if the premium paid for GPT-5.6 Sol is offset by the time saved during high-frequency interactions.

Which model fits which workflow

Selecting the appropriate model requires an assessment of the specific constraints of the project. Grok 4.6 is ideally suited for high-volume, long-running processes where token consumption is high and budget predictability is essential. Its performance is more than sufficient for a wide range of general tasks, and the lower cost structure allows for greater scalability without a proportional increase in expenditure.

GPT-5.6 Sol is better positioned for workflows where speed and coding accuracy are the primary bottlenecks. The model’s superior performance in coding and logic-heavy benchmarks makes it a more reliable partner for software engineering tasks or complex automated systems. While the higher output cost is a factor, the reduction in latency and the increased reliability in technical benchmarks provide a tangible advantage for developers who cannot afford the performance overhead of slower, less precise models.