Granite 4.2 30B vs. GPT-5.5 (xhigh): A Comparative Analysis

What the benchmarks show

When evaluating the intelligence and coding capabilities of these two models, a significant disparity emerges. OpenAI’s GPT-5.5 (xhigh), released in April 2026, demonstrates a superior intelligence index of 56.3 compared to the 23.7 index of IBM’s Granite 4.2 30B. This gap is mirrored in their coding performance, where GPT-5.5 scores 74.9 against Granite’s 29.9. The benchmark data further underscores this divide; GPT-5.5 achieves a GPQA score of 0.935 and an HLE score of 0.458, significantly outpacing Granite 4.2’s scores of 0.644 and 0.112, respectively.

While GPT-5.5 shows higher proficiency across complex reasoning and technical tasks, it is important to note that both models lack publicly available math index data. Furthermore, GPT-5.5 exhibits broader utility in specialized domains, evidenced by its performance in TerminalBench Hard (0.606) and TAU2 (0.938). Granite 4.2 30B, while trailing in these high-level metrics, remains a capable model for its size class, providing a baseline of performance that may be sufficient for less demanding, routine tasks where the extreme overhead of a larger model is not required.

Speed and cost

The economic and operational profiles of these models are fundamentally different. Granite 4.2 30B is positioned as a high-efficiency tool, offering a blended pricing model of $0.28 per million tokens. With an output speed of 77.292 tokens per second and a time-to-first-token of 0.239 seconds, it is optimized for high-throughput, latency-sensitive applications. This predictability makes it an attractive option for developers building real-time systems where cost control is a primary constraint.

In contrast, GPT-5.5 (xhigh) operates at a premium price point, with a blended cost of $11.25 per million tokens—roughly 40 times the cost of the Granite model. While specific speed metrics for GPT-5.5 are currently unavailable, the significant cost increase reflects its status as a high-intelligence, resource-intensive model. Organizations considering GPT-5.5 must weigh the necessity of its advanced reasoning capabilities against the substantial increase in operational expenditure.

Which model fits which workflow

Selecting the appropriate model requires an assessment of the specific workflow requirements. Granite 4.2 30B is best suited for high-volume, repetitive tasks where the cost-per-token is a critical factor. Its speed and lower price point make it ideal for internal tools, automated data processing, and applications where latency must be minimized. It is a pragmatic choice for teams that need consistent performance without the financial burden of top-tier intelligence models.

GPT-5.5 (xhigh) is designed for workflows that demand the highest possible accuracy and reasoning depth. It is the appropriate choice for complex software engineering, advanced research, and tasks that require nuanced understanding or high-level problem solving. While the cost is significantly higher, the performance gains in coding and logic-based benchmarks suggest that it will reduce the need for human intervention or iterative correction in complex projects. Ultimately, the decision rests on whether the task requires the raw intelligence of a frontier model or the efficient, high-speed execution of a specialized, mid-sized model.