Eleven providers, not eleven variants
Metamorfon currently integrates eleven distinct providers — AI21, Anthropic, Cohere, DeepSeek, Google, Mistral, Moonshot, OpenAI, Perplexity, Qwen, and SpaceXAI.
This choice is not driven by a logic of feature accumulation or commercial coverage. It follows from a methodological requirement: if confrontation between models is meant to reveal disagreements, presuppositions, and blind spots, then as far as possible the participants should not all come from the same technical and organizational environment.
Provider plurality is therefore not an optional addition to the system. It is part of the conditions under which Metamorfon seeks to organize confrontation.
This idea extends two principles developed elsewhere: Metamorfon, an Instrument of Thought explains why contradiction can serve as a form of testing without constituting proof; Make Two AIs Debate describes how that confrontation is organized in practice. A third question remains: what differences are actually being brought into contact?
Provider diversity is a means, not a guarantee
Two models offered by the same provider can differ profoundly in size, architecture, specialization, or generation. But they may also share some of their data, technical choices, optimization procedures, or alignment policies.
It is generally impossible to know precisely everything they have in common. However, a study covering more than 350 LLMs shows that model errors are far from independent, and that sharing an architecture or a provider is among the factors associated with stronger error correlation (Kim et al., ICML 2025).
Diversifying providers is therefore a direct way to avoid restricting a confrontation to several variants of the same model family.
But the same study immediately sets a limit on that idea: changing providers is not enough to make errors independent. The largest and highest-performing models can produce strongly correlated errors even when they come from different providers and architectures (Kim et al., ICML 2025).
Provider diversity therefore does not guarantee epistemic diversity. It broadens the conditions under which such diversity may emerge.
This distinction matters. Metamorfon does not assume that a model from Anthropic, Google, Mistral, or DeepSeek each represents a homogeneous and distinct “school of thought.” It creates a space in which possible differences can become observable — but also, conversely, in which convergence between otherwise independent models can itself become an object of analysis.
What sessions make observable
In multi-provider sessions, differences appear at several levels.
They may concern how a problem is divided up, how much caution is considered necessary before reaching a conclusion, what status is given to an empirical argument, which examples are selected, what deserves an objection, or what kind of evidence is demanded from an opponent.
Some models more readily take the initiative; others work more within the frame already established. Some quickly formulate a general rule; others accumulate distinctions or conditions. Two models can reach the same conclusion through very different conceptions of what justifies it.
These observations are not enough, by themselves, to attribute a stable “epistemic style” to a provider. A session remains a situated observation: the model, its version, the prompt, the language, the question, and the dynamics of the debate all matter.
This is precisely why Dialogue Architectures seek to keep the conditions under which models encounter one another as constant as possible. Same context, same instructions, same question: the differences that appear then become easier to examine as properties of model behavior in that situation, rather than as consequences of different treatment.
Provider diversity takes on its methodological meaning here: it expands the space in which differences can appear without deciding in advance what those differences will be or how deep they will run.
Cultural differences exist — but so do convergences
The question becomes more delicate when values, cultural sensitivities, or normative presuppositions are involved.
Comparative studies do find differences between LLMs in how they respond to culturally charged questions. Masoud and colleagues observe variation across models and show, among other things, that the language used for fine-tuning can alter responses to questions involving cultural dimensions (Masoud et al., COLING 2025).
But these differences should not be overstated. A broader study published in 2026, covering ten LLMs, eleven languages, and several forms of cultural framing, finds that prompt language and the requested cultural perspective do affect responses. Yet the models studied also display remarkably strong common tendencies regardless of their origin: their answers remain anchored in a relatively narrow range of cultural values (Bulté & Rigouts Terryn, Computational Linguistics, 2026).
This may be the most important result for Metamorfon.
Provider plurality allows differences to be sought; it does not allow us to assume that they exist.
Convergence between models from different providers can therefore be as informative as disagreement. It may signal a robust conclusion. It may also reveal shared vocabulary, common presuppositions, or a deeper homogeneity within the ecosystem.
The session on AI monoculture and thought convergence asks precisely this question: what remains of plurality when different models end up mobilizing the same categories, the same criteria of legitimacy, or the same ways of making a proposition acceptable?
The point is not to treat each provider as an independent viewpoint by definition. It is to have enough points of comparison to observe where independence exists and where it disappears.
Diversity also helps make biases visible
A bias is harder to recognize when it forms part of the common background of every available answer.
If several systems spontaneously frame a problem in the same way, some presuppositions may begin to look like self-evident facts — not because they have been established, but because no alternative framing reveals that they are themselves choices.
This is a general property of comparison: an outside perspective does not guarantee that the first perspective is wrong, but it can make some of its particularities perceptible.
Metamorfon uses model plurality in this sense. The goal is not to make several AIs vote in order to determine which one is right. It is to place their responses in sufficiently structured relation that both differences and convergences can be examined.
A difference can reveal a presupposition. A convergence can reveal an even deeper one.
The value of plurality therefore lies not only in the disagreement it produces, but in the very possibility of comparing the frames from which disagreement or agreement becomes possible.
From pluralism in the tool to pluralism in the ecosystem
This question extends beyond Metamorfon.
The production of foundation models is subject to powerful forces of concentration: high fixed training costs, economies of scale, access to compute, data, talent, and distribution channels. Vipra and Korinek argue that these characteristics create a strong tendency toward market concentration and that large-scale dependence on a single model or a small number of models can also create systemic vulnerabilities (Vipra & Korinek, Brookings, 2023).
The problem is therefore not only economic.
The U.S. National Telecommunications and Information Administration uses the term “algorithmic monoculture” for an ecosystem that would depend on one or a few foundation models across a wide range of uses. Among the risks it associates with such a situation are methodological uniformity, systemic failures, concentration of cultural power, and the marginalization of certain perspectives. Conversely, it notes that the availability of alternatives can help limit such monoculture (NTIA, Competition, Innovation, and Research).
An ecosystem dominated by two or three actors would therefore raise a question that cannot be reduced to prices, innovation, or commercial competition.
It would also raise a question of knowledge.
How can we recognize what a model treats as self-evident if every available system treats the same thing as self-evident? How can we identify a systemic blind spot if no sufficiently different system exists to produce the contrast that would make it visible? How can we study the diversity of AI behavior if the objects available for comparison come from an ever narrower set of design pipelines?
The answer is not that more providers would mechanically produce more truth.
It is more modest: plurality preserves possibilities for comparison that disappear with concentration.
A plurality that must remain open to challenge
It is in this sense that Metamorfon integrates a broad range of providers.
Not because eleven is an optimal number, nor because eleven providers represent eleven irreducible worldviews, nor because smaller providers are necessarily more original or larger ones more homogeneous.
The list will evolve: new actors will emerge, some will disappear, and models will change families or behavior.
What must remain stable is the principle: do not confuse model plurality with plurality within a single provider, but do not confuse provider plurality with genuine model independence either.
The first distinction justifies keeping the system open. The second prevents that openness from becoming a guarantee.
The breadth of the selection is therefore not intended to certify epistemic diversity. It allows that diversity to be sought, tested, and — when it is absent — allows that absence itself to become observable.
This is also why provider plurality is not merely a technical property of Metamorfon. It expresses a broader view of AI: its critical value also depends on the existence of other systems against which its own choices, presuppositions, and blind spots can become visible.
References
- Kim, E. M., Garg, A., Peng, K. & Garg, N., “Correlated Errors in Large Language Models,” ICML 2025.
- Masoud, R. I., Liu, Z., Ferianc, M., Treleaven, P. & Rodrigues, M., “Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede’s Cultural Dimensions,” COLING 2025.
- Bulté, B. & Rigouts Terryn, A., “LLMs and Cultural Values: The Impact of Prompt Language and Explicit Cultural Framing,” Computational Linguistics, 2026.
- Vipra, J. & Korinek, A., “Market concentration implications of foundation models: The Invisible Hand of ChatGPT,” Brookings Institution, 2023.
- National Telecommunications and Information Administration, “Competition, Innovation, and Research,” Dual-Use Foundation Models with Widely Available Model Weights Report.