Nebius

What is Nebius?

Nebius is a European AI infrastructure and inference provider headquartered in Amsterdam. Through Nebius Token Factory, it hosts and serves open-weight models from developers such as Moonshot AI, Alibaba Qwen, NVIDIA, Meta, OpenAI, and Z.ai. Nebius should therefore be evaluated as a hosting and inference platform for open models rather than as a research lab publishing its own frontier model family.

Nebius provides serverless and dedicated inference, fine-tuning, custom-model deployment, batch processing, and retrieval infrastructure. Its Token Factory API follows the OpenAI format, allowing teams to integrate supported models without adopting a provider-specific request structure.

Nebius is headquartered in Amsterdam, while its public European cloud regions include Finland and France. Eden AI’s EU endpoint restricts requests to providers and models cleared for European processing and does not silently reroute unsupported requests outside the region. 

Nebius main AI capabilities

Nebius supports chat and text generation for assistants, content workflows, summarization, extraction, and question answering. Its catalog includes general-purpose language models as well as models optimized for reasoning, code generation, and instruction following.

Long-context options support workflows such as analyzing large document collections, processing extensive conversation histories, reviewing codebases, and building retrieval-augmented generation applications. Depending on the selected model, context windows range from approximately 8,000 tokens to 1 million tokens.

Nebius also provides multimodal models that can interpret both text and images, alongside embedding models for semantic search, classification, clustering, and retrieval. Its API supports function calling and structured JSON output on compatible models, making it suitable for agents that must interact with external tools or return machine-readable responses.

For specialized workloads, teams can fine-tune supported base models, deploy LoRA adapters, or run custom weights on dedicated endpoints. Batch inference is also available for asynchronously processing large datasets without tying the workload to a real-time request.

When should you choose Nebius?

Nebius is a strong choice when an organization must keep AI processing within the European Union. On Eden AI, Nebius models are available through the EU endpoint, making the provider relevant for GDPR-related controls, internal data-residency policies, regulated workloads, and public-sector procurement requirements.

It is also suitable for teams that prefer open-weight models. Models from families such as Kimi, Qwen, Nemotron, Llama, GPT-OSS, and GLM offer greater portability than proprietary model APIs and can reduce dependence on a single frontier-model developer.

Nebius is particularly relevant for high-volume applications where per-token cost materially affects the economics of the product. Typical use cases include AI assistants, autonomous agents, code generation, long-context document reasoning, retrieval-augmented generation, and large batch-processing workloads.

Input prices across the catalog start at approximately $0.06 per million tokens and can reach around $3.00 per million tokens for larger model tiers. Lower price does not automatically mean better value: teams should benchmark accuracy, latency, tool use, context handling, and total output cost on representative production data before committing to a model.

Nebius is especially compelling for teams that want access to frontier-class open models, including models such as Kimi K3 and Qwen3.5-397B, without relying exclusively on the pricing or deployment conditions of proprietary frontier labs.

Nebius pros and cons

Nebius states that Token Factory offers a zero-retention mode in which requests and outputs are not stored or reused for training. It also states that processing facilities hold SOC 2 Type II, ISO 27001, and HIPAA certifications. These controls should still be reviewed against the exact region, service configuration, and contractual requirements of the intended workload. 

Nebius models, features and capabilities on Eden AI

Nebius is available on Eden AI as a provider for generative AI, multimodal understanding, and text embeddings. Its catalog is particularly strong in open-weight language models designed for reasoning, coding, agentic workflows, long-context processing, and cost-efficient inference.

Nebius Token Factory supports text, code, reasoning, vision, embeddings, function calling, and structured JSON outputs through an OpenAI-compatible API. The provider regularly adds newly released open models, so developers can test different model families without building a separate integration for each one.

Relevant selected features for Nebius

• Chat: Build conversational assistants, customer-support agents, internal copilots, and multi-turn applications.
• Text generation: Generate, transform, classify, extract, or rewrite text from natural-language instructions.
• Code generation: Produce, explain, review, debug, and modify code with coding-oriented and reasoning models.
• Multimodal chat: Submit text and images to compatible vision-language models and receive a text response.
• Embeddings: Convert text into numerical vectors for semantic search, retrieval, clustering, and recommendation.
• Summarization: Condense documents, conversations, reports, and other long-form content.
• Question answering: Generate answers from instructions, supplied context, or retrieved documents.

Available Nebius models on Eden AI

All the Nebius models listed below are available through Eden AI in the EU region. Prices are expressed in US dollars per one million tokens and exclude Eden AI’s platform fee. Model availability, pricing, and context limits may change, so production applications should confirm the current values in Eden AI’s live model catalog.

Frontier and long-context reasoning

Kimi K3 provides the largest context window and the highest price in the Nebius catalog shown here. Models such as Qwen3-235B-A22B-Instruct provide a lower-cost option for applications that still require a large context window.

Coding and agentic models

These models are suited to code generation, software-development assistants, tool-using agents, task planning, and multi-step workflows. Context length differs substantially within this category: Kimi K2.6 and MiniMax M2.5 are better candidates for workflows that must retain extensive instructions, code, or task history.

Efficient and high-volume models

This group provides the strongest options for applications where inference volume and unit economics matter. Entry pricing starts at $0.06 per million input tokens for the lowest-cost Nemotron models, while larger models such as Hermes 4 405B cost more but may provide stronger performance on complex instructions and agentic tasks.

Multimodal vision models

Vision-language models accept both images and text. They can describe visual content, answer questions about images, extract visible information, analyze screenshots, and interpret image-based documents. They should not be treated as substitutes for specialized OCR or computer-vision systems when deterministic extraction or precise object coordinates are required.

Embedding models

Qwen3 Embedding converts text into numerical vectors rather than generating a natural-language completion. It can be used for semantic search, retrieval-augmented generation, document matching, clustering, classification, and recommendation systems.

Catalog note: Nebius publicly identifies models such as Kimi K3, GLM-5.1, MiniMax M2.5, Nemotron 3 Ultra, Qwen3-235B-A22B, GPT-OSS, and Qwen3 Embedding as part of Token Factory. The exact Eden AI model identifier should nevertheless be copied from the live catalog before implementation, particularly for the Nemotron and Hermes entries whose names may include additional parameter or version suffixes.

Nebius API output: what data can be extracted or generated?

For language and multimodal models, the main output is generally a generated message containing text, reasoning results, structured data, or a requested tool call. For embedding models, the output is a vector representing the semantic meaning of the supplied text.

Important note on Nebius accuracy and reliability

Nebius provides the infrastructure used to serve the models, but output quality depends primarily on the selected model, its version, the prompt, the inference configuration, and the application’s data.

Open-weight models vary significantly in reasoning quality, factual reliability, code generation, multilingual performance, tool use, latency, and instruction following. A model that performs well on public benchmarks may still underperform on a company’s documents, terminology, output format, or production workflow.

Teams should therefore benchmark several models using representative prompts and data before choosing a default. Evaluation should measure task accuracy, hallucination rate, structured-output validity, tool-call success, latency, input and output cost, and performance at the expected context length.

Model identifiers should also be pinned in production whenever versioned slugs are available. Nebius continuously expands its catalog as new open models are released, and model availability or aliases may change over time. Nebius states that its catalog is updated monthly and currently includes more than 60 text, reasoning, vision, and embedding models.

What can you build with Nebius?

Nebius is particularly well suited to applications that combine European data residency, open-weight models, long context windows, and cost-efficient inference. Through Eden AI, teams can use Nebius models to build assistants, document-analysis systems, developer tools, and large-scale processing pipelines while keeping eligible workloads in the EU region.

Use case 1 - EU-resident AI assistants and copilots

Nebius can power customer-facing assistants, internal knowledge copilots, employee-support tools, and domain-specific agents that process data in the European Union.

This makes it relevant to organizations that must consider GDPR requirements, internal data-location policies, contractual residency commitments, or public-sector procurement rules. Instead of sending prompts to an unrestricted global endpoint, teams can access eligible Nebius models through Eden AI’s EU endpoint.

Typical applications include:

• Internal assistants connected to company documentation
• Customer-support copilots that summarize cases and suggest responses
• Financial or legal research assistants
• Public-sector knowledge bases
• Tool-using agents that interact with internal APIs and business systems

Nebius supports open-weight models from several developers, allowing teams to compare model families without becoming dependent on a single proprietary AI lab. Compatible models can also return structured JSON or function calls, which is useful when an assistant must trigger workflows rather than only generate text.

EU availability does not make an application GDPR-compliant by itself. Organizations must still define a lawful basis for processing, limit the data sent to the model, configure retention appropriately, secure access, and assess any additional systems used in the workflow.

Use case 2 - Long-context document and codebase analysis

Nebius offers models with context windows of up to 1 million tokens, making it suitable for applications that need to analyze more information within a single request.

Long-context models can process extensive reports, technical documentation, conversation histories, legal materials, or large sections of a software repository without dividing every task into small, isolated prompts.

Potential applications include:

• Comparing clauses across long contracts
• Summarizing financial, technical, or regulatory reports
• Answering questions across a large document set
• Reviewing a repository or multiple source-code files
• Mapping dependencies and identifying repeated code patterns
• Maintaining extended context in long-running agents
• Analyzing detailed support histories or project documentation

For example, a development team could submit architecture documentation, selected repository files, an issue description, and coding conventions in the same context. The model could then explain the relevant components, identify likely causes, and propose a change that follows the existing codebase’s structure.

A large advertised context window does not guarantee that every token receives equal attention. Retrieval quality, instruction placement, document ordering, latency, and total token cost can still affect the result. Teams should benchmark long-context models using documents that resemble their real production inputs.

Use case 3 - High-volume batch processing and classification

Nebius is also a strong fit for workloads where the same operation must be applied to thousands or millions of records.

Selected models start at approximately $0.06 per million input tokens, making them relevant for use cases where per-token cost has a direct effect on product margins or operational budgets.

Typical batch workloads include:

• Classifying support tickets by topic, urgency, or department
• Extracting structured fields from text
• Categorizing products, articles, or user-generated content
• Summarizing large collections of records
• Enriching CRM entries
• Detecting themes or sentiment in customer feedback
• Preparing documents for search and retrieval
• Generating embeddings for large knowledge bases

Smaller and more efficient models may be sufficient for repetitive tasks with narrow instructions. A company does not necessarily need the largest reasoning model to assign a category, detect intent, or extract a predefined set of fields.

The lowest input price should not be considered in isolation. Output-token pricing, response length, retry rates, latency, and classification accuracy all contribute to the actual cost per successfully processed record. The most economical model is the one that achieves the required quality at the lowest total production cost.

Nebius use cases by industry

Nebius is most compelling when these applications also benefit from open-weight model choice, long-context processing, or high-volume inference. Alternative providers may remain more appropriate when a workload requires proprietary frontier models, extremely low real-time latency, or processing in regions outside the European Union.

Why use Nebius through Eden AI?

Using Nebius through Eden AI adds a unified management layer around its open-weight models. Teams can access Nebius alongside more than 50 other AI providers, compare models through one integration, configure fallback strategies, and centralize usage and cost monitoring. Eden AI currently provides access to more than 500 AI models through a unified gateway.

Key benefits of using Nebius on Eden AI

• Access Nebius and other providers through one API: Integrate once and use Nebius alongside proprietary and open-weight model providers.
• Compare models without rebuilding your application: Test Nebius models against alternatives based on output quality, latency, context capacity, and price.
• Reduce provider lock-in: Keep the option to change the model or provider used for a workflow without maintaining a separate integration for each vendor.
• Improve reliability with fallback and routing: Send traffic to backup models when the primary Nebius model is unavailable, rate-limited, or returns an error.
• Centralize monitoring and billing: Track model usage, latency, errors, and costs across projects and providers from the same environment.

One API for Nebius and 50+ AI providers

Eden AI gives developers access to Nebius and more than 50 other AI providers through a single API. Instead of maintaining separate authentication methods, SDKs, request formats, billing accounts, and monitoring systems, teams can connect once and select models using a standardized identifier.

For LLM applications, Eden AI provides an OpenAI-compatible chat-completions endpoint. A development team can therefore use the same general request structure for a Nebius model and for models offered by other supported providers. Switching models usually requires changing the model identifier rather than rebuilding the application’s inference layer.

This unified approach is especially useful when an application combines several requirements. A team might use Nebius for cost-efficient EU-hosted open models, another provider for a proprietary frontier model, and a specialized provider for OCR, speech, translation, or image analysis.

Compare Nebius with other AI models

Nebius models can be evaluated against alternatives through the same Eden AI environment. Teams can compare models based on criteria such as:

• Response quality and task accuracy
• Reasoning and instruction-following performance
• Context-window capacity
• Code-generation quality
• Multimodal understanding
• Latency and throughput
• Input and output pricing
• Regional availability
• Structured-output and function-calling reliability

This matters because the strongest model depends on the workload. A large Nebius reasoning model may be appropriate for complex document analysis, while a smaller model may achieve a better cost-to-performance ratio for classification or extraction. A proprietary frontier model may still perform better on another task.

Eden AI’s pricing offer includes tools for comparing model accuracy, latency, and price, allowing teams to benchmark providers before choosing a production default.

Using a common gateway also makes continuous evaluation easier. A team can periodically test new Nebius releases or alternative providers without replacing the surrounding authentication, monitoring, and billing infrastructure.

Add fallback and routing for production reliability

Eden AI supports fallback between models and providers. If the primary Nebius model fails because of a provider outage, rate limit, or request error, Eden AI can retry the request using the next model specified in the fallback list. This avoids requiring the application team to develop and maintain all retry and provider-switching logic itself.

For example, an application could use a Nebius model as its primary choice because of its EU availability and low inference cost, then define another eligible model as a backup. The fallback model should support the same essential capabilities, such as vision input, function calling, structured output, or a sufficiently large context window.

Routing can also be used to assign different models to different workloads. A production architecture might send:

• Complex reasoning requests to a larger Nebius model
• Classification tasks to a lower-cost model
• Image-based requests to a multimodal model
• High-priority traffic to a model with more predictable capacity
• Failed requests to a compatible fallback model

Fallback improves continuity, but it must be configured carefully. Backup models may differ in price, context limits, regional processing, output style, and supported features. Teams should verify that every fallback remains compatible with the application’s technical and data-residency requirements.

Monitor usage, billing and costs in one place

Eden AI centralizes usage and billing across Nebius and other supported providers. Teams can monitor spending, latency, errors, and model usage without reconciling separate provider dashboards and invoices. Eden AI describes this approach as one integration, one invoice, and one dashboard.

Each API response can include the exact cost of the request in US dollars, making it possible to measure expenditure at request level. Teams can use this information to calculate cost by feature, customer, workflow, model, or environment.

Multiple API keys can also be created for separate projects or environments. This allows organizations to distinguish, for example, production usage from testing, or one product team’s consumption from another’s.

Centralized cost monitoring is particularly valuable when comparing Nebius models with alternatives. A cheaper token price does not always produce the lowest operational cost if the model generates longer responses, requires more retries, or achieves lower task accuracy. Tracking real production usage helps teams choose models based on total cost per successful task rather than advertised token pricing alone.

Best Nebius alternatives and comparisons on Eden AI

Nebius is a strong choice for European inference, open-weight models, long-context processing, and cost-sensitive workloads. Together AI, Groq, Mistral AI, and Replicate may be better suited when catalog breadth, ultra-low latency, proprietary European models, or generative media are the priority.

Nebius vs Together AI

Nebius and Together AI both provide managed inference for open-weight models without requiring teams to operate their own GPU infrastructure.

Nebius is the stronger option when EU data residency is mandatory because its Eden AI catalog is available through the EU region. Together AI offers European dedicated deployments for some enterprise customers, but its standard serverless ecosystem is more US-centered.

Together AI provides a broader catalog across text, vision, image, video, audio, embeddings, and transcription. It also offers more deployment options, including dedicated containers and provisioned throughput.

Choose Nebius for consistent EU availability, long-context LLMs, and cost-efficient text inference. Choose Together AI when modality breadth and deployment flexibility matter more.

Nebius vs Groq

Nebius prioritizes EU availability, model choice, long context windows, and cost-efficient inference. Groq focuses on raw inference speed through its LPU-based infrastructure.

Groq is often the better choice for voice agents, live assistants, coding tools, and other applications where response time directly affects the user experience. Nebius is better suited to workloads that require European processing, a broader open-weight catalog, contexts up to 1 million tokens, or lower token costs at scale.

Teams should compare both providers using representative requests and measure latency, accuracy, context handling, reliability, and total cost per successful task.

Nebius vs Mistral AI

Nebius and Mistral AI both have a strong European presence, but they occupy different parts of the AI ecosystem.

Nebius is an inference platform that hosts models from developers such as Moonshot AI, Qwen, NVIDIA, Meta, OpenAI, and Z.ai. Mistral AI is a European model lab that develops and serves its own general-purpose and specialist models.

Choose Nebius when you want access to several open-weight model families, including Kimi, Qwen, Nemotron, Llama, GPT-OSS, and GLM. Choose Mistral when you specifically want models developed by a European lab or specialist capabilities such as Codestral, Voxtral, OCR, and moderation.

Nebius offers greater model diversity, while Mistral provides a more vertically integrated European model ecosystem.

Frequently asked questions about Nebius on Eden AI