GPT-5.6 Sol and Grok 4.5 capabilities

GPT-5.6 Sol's most striking demonstrations are three-dimensional. A community tool built on Sol reconstructs a photographed object as a pure code-based Three.js 3D model, and Sol generated a voxel-based rendering of Manhattan in a single run - cited as direct evidence of improved spatial reasoning over GPT-5.5 ^[2]. On the community side, users testing Sol's ability to create 3D models in Blender compared it favorably against dedicated tools like Meshy. This is a capability leap distinct from raw code quality: the model appears to hold a coherent spatial mental model long enough to emit geometry-as-code in one shot, which is why OpenAI markets Sol as its best coding model for agentic and long-horizon work ^[1].

The competitive axis has shifted from capability alone to cost-per-outcome. Grok 4.5 lists at $2 per million input and $6 per million output, roughly a third of Sol's $5/$30, and is reported to cut coding-agent cost around 80 percent at near-frontier speed - though with higher hallucination rates flagged ^[7]. OpenAI's counter is token economy: it claims Sol is 54 percent more token-efficient on AI coding tasks and reaches a new state of the art on a coding benchmark using less than half the output tokens of the prior leader ^[1]. Grok, meanwhile, is reported to use roughly 4.2x fewer output tokens than the strongest prior frontier model on SWE Bench Pro ^[3], meaning the effective price gap narrows once you price the tokens each model actually spends.

Grok 4.5's differentiator is that xAI/SpaceXAI co-trained it with Cursor on trillions of tokens of real developer interaction data, tuning it to "think like a developer" across multi-file codebases and hold coherence over extended sessions ^[4][5]. That real-world trace data is a genuine training moat rivals cannot easily replicate. But the same closeness is a liability: community discussion surfaced that an earlier Cursor codebase snapshot leaked into Grok 4.5's training, inflating its CursorBench score, with the contaminated data since removed. The lesson is that benchmarks built on the same data used for training deserve heavy discounting, and Grok's most impressive coding numbers should be read with that caveat.

On aggregate benchmarks the two models are closer than the marketing suggests. Sol leads the overall aggregate 86 to 82 and the agentic average 92.0 to 83.3, including Terminal-Bench 2.0 at 91.9 percent versus 83.3 percent ^[6]. Yet on the pure coding average Grok 4.5 narrowly edges ahead, 64.7 to 64.6 ^[6]. Sol carries a larger context window of roughly 1M tokens against Grok's 500K, while Grok wins decisively on price at $2/$6 versus $5/$30 ^[6]. The takeaway: Sol owns agentic and long-context work, Grok owns cost-sensitive coding at parity accuracy, and the right choice depends entirely on which of those axes your workload lives on.