In the space of six weeks, three flagship model families shipped in rapid succession: multiple iterations across Google's Gemini 3 series, Claude Opus 4.7 from Anthropic, and GPT-5.5 from OpenAI. Add DeepSeek V4, Mistral Small 4, and a dozen others and you have what analysts are calling one of the densest model release windows in AI history.

The multi-model era is here

In 2026, there is no clear winner for model choice, and organisations are increasingly keeping multiple models in flight. The competitive gap between frontier models has narrowed significantly in terms of general capability, while their specialisations have become more pronounced. GPT-5.4 leads on reasoning benchmarks. Gemini 3.1 Pro leads on multimodal tasks and cost efficiency. Claude Opus 4.7 leads on complex, long-horizon agentic work.

Single-model strategies are giving way to something more sophisticated. Microsoft has announced plans to move away from promoting specific model names altogether, shifting focus to what a user is trying to accomplish and routing work to whichever models best handle each specific task.

What this means for private capital firms

Frontier models are genuinely powerful at reasoning, synthesis, and analysis, and every new release makes them more so. For private capital firms, the question is what sits on top of that capability to make it work consistently across the deal lifecycle.

Generic models don't arrive knowing how your firm structures an IC memo, how to weight sell-side materials against management accounts, or how a comparable deal from 18 months ago is relevant to the target in front of you today. That's not a limitation of the models, it's simply what a domain-specific layer adds. Forward-thinking organisations are already implementing an orchestration layer that sits above the models, capable of switching between them and contextualising every decision with business logic. For private capital firms, that business logic is PE-specific: the workflows, the document hierarchy, and the institutional memory that makes one firm's output materially different from another's.

Our model-agnostic approach

Capsa is built on a multi-model architecture across Anthropic, OpenAI, and Google that draws on the strengths of each provider. Different models lead on different tasks: reasoning, complex agentic work, document analysis, speed. A multi-model approach means users always benefit from the best available capability for each workflow, without trade-offs. It also means no vendor dependency - whenever a better model ships, Capsa incorporates it so our users are always running on the best ones available.

Our recently launched Skills feature extends this further. Skills allow our users to codify their firm’s specific way of working - memo structure, IC prep format, document review methodology - and deploy it at workspace level, so every team member automatically works to the same standard on every task, every deal. No individual setup, no inconsistency across the team.

The model improves, the domain layer compounds. Every release cycle, the gap between firms with infrastructure and firms without it widens.

Don't miss future insights. Subscribe to the Deal Intelligence Brief here.