Available Models

The OpenRouter Model Ecosystem

When you integrate with the OpenRouter API, you are not committing to a single model or a single provider. You gain access to an ecosystem that spans frontier research labs, open-source model communities, and specialized model builders — all behind one consistent API surface. The catalog below organizes every available model by its essential characteristics: which organization built it, how much context it can process in a single request, and what types of input and output it supports.

Choosing among two hundred models can feel overwhelming. Most teams find their core set of 3-5 models after a few weeks of experimentation. The key is understanding the dimensions that matter for your workload: if you are building a long-document summarization pipeline, context window size is paramount. If you are building a real-time chat interface, latency and cost-per-token matter more than raw benchmark scores. If your application processes images or diagrams, multimodal capability becomes a hard requirement. The catalog makes these dimensions visible at a glance.

The model landscape changes rapidly — new releases appear monthly, and providers sometimes deprecate older versions. Resources from NIST's AI standards program offer guidance on evaluating model suitability for production applications, emphasizing the importance of testing models against domain-specific benchmarks rather than relying solely on public leaderboard rankings.

How Provider Diversity Benefits Your Architecture

Relying on a single AI provider introduces concentration risk. If that provider experiences an outage during a critical deployment window, every model-dependent feature in your application stops working simultaneously. The OpenRouter model catalog enables a multi-provider strategy: you can designate a primary model for daily operations while configuring fallback models from entirely different providers for resilience. This architecture decision costs nothing to implement through the API and provides meaningful reliability improvements without additional infrastructure.

The breadth of the catalog also supports cost optimization strategies that single-provider arrangements cannot match. You might use a frontier model like GPT-4o for complex reasoning tasks that generate direct revenue, while routing simpler classification or summarization workloads to more economical models like Llama 3.3 or Gemini Flash. The API surface stays identical across all models — only the model name parameter changes.

Text Generation and Chat Models

The core of the OpenRouter catalog consists of general-purpose text generation models optimized for chat completions. These models handle conversational AI, content creation, question answering, summarization, and virtually any text-in/text-out task. The table below captures the essential specifications you need when selecting models for a new project: the provider behind each model, the maximum context window that determines how much conversation history or document content you can include in a single request, and whether the model supports modalities beyond text.

Model Catalog Reference

Each row below represents a model that is production-accessible through the OpenRouter API. Context window values reflect the maximum token capacity advertised by the provider at the time of documentation. Actual available capacity may be lower during high-demand periods for certain models. Modality indicators show whether a model accepts image inputs, generates structured outputs, or supports function calling — capabilities that materially affect which use cases a model can serve.

The catalog shown above represents a snapshot of the most frequently accessed models on the platform. Additional models — including fine-tuned variants, older version snapshots, and community-hosted open-weight models — are available through the API. Filtering parameters in your API requests let you query for models matching specific criteria such as minimum context window, maximum cost per token, or provider preference.

Organizations subject to compliance frameworks may want to review the Better Business Bureau's business standards for evaluating technology vendor relationships, particularly around data handling and service reliability disclosures.

Finding Models for Your Specific Workload

The sheer number of available models can feel like an embarrassment of riches. To cut through the noise, start by defining the non-negotiable requirements of your application. If your system processes legal documents that routinely exceed 100 pages, the context window filter eliminates most of the catalog immediately — only models with 128K+ token windows remain viable. If your application includes a user-facing interface where response delay matters, models with sub-second time-to-first-token become the relevant comparison set.

Cost considerations should be factored in early, not after prototype completion. A model that performs beautifully during development at low request volumes may become financially unsustainable when scaled to handle thousands of daily requests. OpenRouter displays per-token pricing directly in the API response, making it straightforward to run cost projections before committing to a model for production workloads. Teams that skip this step often discover budget overruns weeks into deployment when the monthly invoice arrives.

The interplay between model capability and task difficulty is another dimension worth explicit attention. Simple classification tasks — categorizing support tickets, extracting structured data from forms, detecting sentiment in user feedback — rarely benefit from the most expensive frontier models. A capable instruction-tuned model at a tenth of the per-token cost often delivers indistinguishable accuracy on bounded, well-defined tasks. Reserve frontier models for genuinely ambiguous reasoning problems where the quality gap between model tiers is measurable.

Free Models and Prototyping Access

OpenRouter provides several models at zero cost with generous rate limits, making the platform immediately useful for prototyping and evaluation without financial commitment. Free-tier models include Llama 3.3 70B, Gemini Flash variants, and DeepSeek V3 — each a capable model in its own right. The free access model serves a dual purpose: it lets developers explore the API integration workflow before making purchasing decisions, and it provides a safety net for workloads where quality requirements permit using no-cost models indefinitely. Many production applications route the majority of their request volume through free or low-cost models, reserving premium model access for the subset of requests where quality sensitivity justifies the higher per-token rate.

Frequently Asked Questions About OpenRouter Models