LLM models catalog

Currently available modelsCopy Anchor Link

Check the live status page for current availability. The tables below describe the documented model inventory; deployments and settings may change.

-> Complete list of models (API)

For programmatic access, use a maintained model alias or an exact model identifier returned by the API. Model identifiers are case-sensitive.

Generative modelsCopy Anchor Link

In the table below:

• Context is the documented context-window size, expressed in tokens.
• Max output is the documented output-token limit.
• Quant. describes the numerical representation used for model weights.
• — means that no value is listed here.

These limits describe capacity, not guaranteed response length, accuracy, or processing speed. Effective limits can also depend on service configuration.

• Kimi K3.1-pro — New Moonshot AI’s open-weight 2.8T-parameter Mixture-of-Experts (MoE) flagship model. Native multimodal with vision support, a 1M-token context window, and strong performance in long-horizon coding, reasoning, knowledge work, and agentic workflows.
• GLM 5.3 — 756B-parameter language model optimized for advanced reasoning, tool use, and multi-step agentic tasks.
• GLM 5.3 Flash — 320B-parameter (18B active) Mixture-of-Experts multimodal model from ZAI for reasoning, coding, and agentic tasks. The NVIDIA NVFP4 build reduces weight memory by about 3.3 times compared with BF16 and keeps benchmark accuracy close to the BF16 baseline.
• GPT-OSS-120B — OpenAI’s 120B-parameter open-weight language model offering strong general-purpose performance across reasoning, coding, and tool use.
• DeepSeek V4.1 Flash-1017 — efficient reasoning model with a small activated parameter count relative to leading proprietary models. Excels at mathematics, logical reasoning, and code generation.
• Qwen3.8 Flash Next — 180B (MoE) multimodal model with improved coding performance. fp8 quantization, 256k context, and a maximum output of 48k tokens.
• Qwen3.8 27B — 27B (dense) multimodal model with significantly improved coding performance. Supports images and videos, with configurable thinking for balancing speed and reasoning.
• Mistral Medium 3.5 — 128B multimodal model for text, coding, tool use, and agentic workflows.
• Gemma 4 — Google’s 31B multimodal model; useful for OCR/vision and general-purpose tasks.
• Whisper Large v3 — OpenAI’s 1.55B automatic speech recognition (ASR) and speech-translation model; supports 99+ languages with robust transcription even in noisy conditions. Available via API only. See its model card for background information. It is not a text-chat model.
• Canary 1B v2: Multitask Speech Transcription and Translation Model Canary-v2 is a powerful 1-billion parameter model built for high-quality speech transcription and translation across 25 European languages. It excels at both automatic speech recognition (ASR) and speech translation (AST).

Model aliasesCopy Anchor Link

An alias is a maintained name that points to a deployed model. Its target can change when the service is updated.

Aliases reduce the need to change model names in scripts, but they do not guarantee identical behaviour, outputs, or uninterrupted access.

The documented deepseek and deepseek-thinking targets use the same underlying model weights with different reasoning defaults.

Use an exact model identifier when you need to record which version was used. An exact identifier does not reserve that version or guarantee its future availability.

Embedding modelsCopy Anchor Link

Embedding models represent text as numerical vectors for semantic search and document retrieval. Rerankers perform a different task: they reorder candidate results according to their relevance to a query.

These services are accessed programmatically. See the API guide for access and supported requests.

Check the deployed configuration before designing a document-processing pipeline. Input limits, supported languages, and preprocessing requirements should be evaluated for your dataset.

Programming languages proficiencyCopy Anchor Link

The following results are reported from evaluations using the Aider Polyglot Benchmark.

Use them as supporting information when choosing a model. Performance on benchmark exercises does not establish correctness for your software or research workflow.

Obsolete modelsCopy Anchor Link