Why :Harvey: is Multi-Model by Design

Last year, Harvey went multi-model, expanding its platform to incorporate leading foundation models from Anthropic, Google DeepMind, and OpenAI. That decision was a deliberate architectural commitment — one that has only become more consequential as the AI landscape has evolved.

Being multi-model creates both opportunity and resilience. The opportunity is straightforward: Different models have different strengths and weaknesses, and a platform that harnesses all of them delivers better outcomes than one constrained to a single provider. Resilience has become equally important. Enterprise AI adoption introduces a category of risk that most organizations are only beginning to reckon with — model provider risk. When a provider experiences a disruption, whether operational, regulatory, or geopolitical, organizations that depend exclusively on that provider face an immediate question: What now?

Harvey's customers never have to ask that question.

We built a multi-model platform because doing so is the only responsible way to serve organizations whose work demands the best available intelligence, uninterrupted continuity, and full control over their technology stack. That commitment rests on three pillars: quality, reliability, and choice.

Quality: The Best Model for Every Task

No single model is the best at everything — that is the nature of frontier AI development. Different model architectures, training datasets, and optimization targets produce models with meaningfully different strengths and weaknesses.

A platform that restricts itself to only one family of models inherently leaves performance on the table.

In our experience, Claude Opus 4.6, for example, excels at agentic reasoning and deep multi-step legal analysis, while GPT-5.2 pairs strong analytical abilities with capability awareness — surfacing its own limitations and producing reliably sourced, structurally sound long-form output. And sometimes the right model is not the largest one. For high-volume product surfaces like Vault, the challenge is balancing quality with latency. We have found that models like Sonnet 4.6 and Gemini 3 Flash deliver strong performance with a significantly lower latency footprint, making them well-suited for use cases where speed and throughput matter as much as analytical depth.

At Harvey, our research team is constantly working to match the right model to the right task — a process informed by continuous evaluation through BigLaw Bench and expert preference testing from our Applied Legal Research team. Every model we integrate strengthens the platform, and our customers benefit from the combined frontier of AI capability rather than the trajectory of any single provider.

Reliability: Continuity Without Compromise

For the organizations Harvey serves, downtime is not just an inconvenience, it is a risk to client commitments, deal timelines, and regulatory obligations. In an environment where both the availability of compute and the regulatory landscape shift rapidly — across regions, across industries, and sometimes with little warning — it becomes crucial to minimize single points of failure.

Harvey’s multi-model architecture provides structural redundancy. If one provider experiences capacity constraints, service degradation, or an outage, Harvey can route work to an alternative model without disrupting the user's workflow.

Reliability also means meeting customers where they are — literally — since regional availability varies across providers. Today, for example, Opus 4.6 is available to our customers in Australia, where certain other frontier models have not yet been deployed. For all of our customers across the US, EU, AU, and other locales, Harvey has built infrastructure to support localized data processing for the models that are available in those regions, ensuring that data sovereignty requirements are met without sacrificing access to the best available models.

Security and compliance constraints add another dimension. Harvey requires all model providers to meet the same non-negotiable standards: zero data retention, no human review of customer data, and no training on customer inputs. Our multi-provider relationships give us leverage to enforce these commitments consistently, and the flexibility to respond if a provider's posture changes.

Choice: Putting Control Where it Belongs

Harvey's customers operate in environments where governance, compliance, and organizational policy shape every technology decision. Different firms have different requirements, different risk tolerances, and different views on which providers they are willing to work with. With respect to model performance, we have seen both firms and individuals develop their own model preferences over time.

We believe those decisions belong to our customers, not to us.

Harvey allows workspace administrators to determine which models are available to their organization. If a firm requires that a specific provider be disabled — whether for regulatory, policy, or preference reasons — we can facilitate that change. At the user level, if an individual practitioner works most effectively with a particular model, they can select it directly in our model selector.

A Philosophy, Not a Feature

Harvey is multi-model because we believe it is the right way to build and deploy AI for the professionals and institutions whose work demands the highest standard of quality, reliability, and control. That conviction has guided our architecture from the beginning, shaping how we run evaluation, how we design our products and infrastructure, and how we build trust with the institutions that rely on us. And it will continue to guide these decisions as the frontier of model intelligence advances.

The AI landscape will continue to shift. New paradigms and forms of risk will emerge; and model providers will inevitably face disruptions over time, whether commercial, regulatory, or technical. The organizations building on Harvey should not need to worry about whether their platform can adapt. That is our job.