Structured cards
Model database
Filter by provider, architecture family, or full-text search across descriptions.
Frontier models
Verified flagships and this month’s launches—Gemini 4 Argon, Nano Banana 2.1, EmbeddingGemma 2, Mistral Large 4, Claude Sonnet 5.5, and GPT-6.1 Sol. Scroll sideways for the full shelf.
By company
Latest verified models from each frontier company, summarized on one page.
All models
Filter and paginate the full catalog. Tabs control lifecycle scope.
Alibaba
Qwen 2.5 72B Instruct
LegacyQwen 2.5 72B Instruct is a large multilingual open-weights model from Alibaba’s Qwen family with strong coding and general chat performance. Common in APAC deployments and on Hugging Face inference endpoints—check license terms for commercial use.
Anthropic
Claude Sonnet 4.6
LegacyAnthropic's Sonnet-tier model documented as the best combination of speed and intelligence in the Claude model overview.
OpenAI
o1
LegacyOpenAI’s o1 series emphasizes extended internal reasoning before answering—useful for competition-style math, complex debugging, and multi-step planning where latency is acceptable. It behaves differently from standard chat models: tune prompts for chain-of-thought style tasks and measure time-to-first-token.
Gemini 2.5 Flash-Lite
LegacyGoogle's fastest and most budget-friendly multimodal model in the Gemini 2.5 family, according to the Gemini API model documentation.
DeepSeek
DeepSeek-V3.2
LegacyDeepSeek's documented successor to the V3.2 experimental line, positioned in official DeepSeek API news as live on app, web, and API.
OpenAI
GPT-5.4 mini
LegacyOpenAI's smaller GPT-5.4 mini model, documented in the official OpenAI API model guide for lower-latency or lower-cost GPT-5 family workloads.
OpenAI
GPT-5.4 nano
LegacyOpenAI's smallest GPT-5.4 nano model, documented in the official OpenAI API model guide for very low-latency or economical GPT-5 family routing.
Gemini 1.5 Flash
LegacyGemini 1.5 Flash targets low-latency, cost-efficient multimodal chat and retrieval workloads on the Gemini API and Vertex AI. It keeps much of the long-context family behavior with faster responses for interactive apps.
Anthropic
Claude 3.5 Haiku
LegacyClaude 3.5 Haiku is Anthropic’s fast, cost-efficient tier for high-volume classification, routing, and simple chat. It targets latency-sensitive paths and agent pre-processing before escalating to Sonnet-class models.
Microsoft
Phi-3 Medium
LegacyPhi-3 Medium is a compact instruct model aimed at strong quality per parameter for on-device and cost-sensitive cloud inference. It competes with other SLMs on coding and reasoning benchmarks—validate on your domain prompts.
Meta
Llama 3.2 1B Instruct
LegacyLlama 3.2 1B Instruct is among the smallest Llama instruct checkpoints for extreme latency and footprint constraints. Use for routing, tagging, and toy assistants—not for complex reasoning without retrieval augmentation.
Mistral AI
Mixtral 8x7B Instruct
LegacyMixtral 8x7B Instruct is a sparse mixture-of-experts open model noted for strong quality per active parameter and efficient inference vs dense models of similar capability. Widely hosted on inference clouds and self-hosted stacks.
OpenAI
GPT-3.5 Turbo
LegacyGPT-3.5 Turbo is a long-standing cost-efficient chat model family on the OpenAI API for simple assistants, classification, and legacy integrations. Many teams still use it for non-critical paths or as a fallback when newer models are rate-limited.
Anthropic
Claude 3 Opus
LegacyClaude 3 Opus was Anthropic’s highest-capability Claude 3-era model for difficult reasoning, nuanced writing, and complex analysis before later Sonnet generations. Teams still reference it for historical benchmarks and legacy deployments—verify current availability in API and Bedrock model lists.
Anthropic
Claude 3 Sonnet
LegacyClaude 3 Sonnet balanced cost and capability in the Claude 3 generation—useful for general assistants and document workflows where Opus was unnecessary. New deployments should compare against Claude 3.5 Sonnet for pricing and quality.
Meta
Llama 3.2 3B Instruct
LegacyLlama 3.2 3B Instruct is a compact instruct model in Meta’s 3.2 generation aimed at mobile and edge scenarios with multilingual support on supported checkpoints. Verify hardware targets and license terms for your distribution channel.
xAI
Grok-3
LegacyGrok-3 represents xAI’s newer generation aimed at stronger reasoning and tool use versus Grok-2. Capabilities and rollout are version-specific—validate against xAI documentation for your account tier.
Gemini 1.0 Pro
LegacyGemini 1.0 Pro represents Google’s first broadly marketed Gemini-era general model for text and basic multimodal tasks on Vertex and consumer surfaces. New projects should prefer 1.5+ generations unless constrained by legacy integrations—verify availability.
Cohere
Command R
LegacyCommand R is Cohere’s earlier RAG-oriented model line preceding Command R+, focused on grounded generation with connectors and multilingual enterprise search. Useful when comparing tiered Cohere stacks or maintaining legacy integrations.
OpenAI
GPT-4 Turbo
LegacyGPT-4 Turbo is a widely deployed GPT-4-class chat model with a large context window on the OpenAI API, aimed at long-document workflows, retrieval bundles, and production assistants that do not require GPT-4o’s multimodal stack. It remains a common baseline for cost/quality tradeoffs.
Anthropic
Claude 3 Haiku
LegacyClaude 3 Haiku is the fast Claude 3-era model for simple tasks and high throughput. Prefer 3.5 Haiku when available for better quality at similar latency targets—confirm SKUs on your cloud path.
OpenAI
GPT-4o mini
LegacyGPT-4o mini is a cost-optimized GPT-4o-family model for high-volume chat, moderation, and routing layers where frontier quality is unnecessary. It supports multimodal inputs on supported API surfaces and is often used as a fast first pass before escalating to larger models.
Gemini 2.0 Flash
LegacyGemini 2.0 Flash is Google’s efficiency-oriented multimodal model generation aimed at fast agentic and interactive experiences. Capabilities and naming evolve—validate against the current Gemini API reference for tool use and context limits.
01.AI
Yi-34B
LegacyCatalog entry for this named release; see the provider’s official documentation for modalities, pricing, and context limits.
Recommended
Top current models by information quality score — good defaults when you are not sure where to start.
Anthropic
Claude Opus 5.5
CurrentLatestClaude Opus 5.5 is Anthropic's September 22, 2026 Opus-class model for long-running agentic coding and knowledge work, and the first model in the Claude 5.5 family. The Claude API ID is claude-opus-5-5, with a 1M-token context window, 128K synchronous max output, adaptive thinking that cannot be disabled, and default effort medium. Anthropic prices it at $4 input and $20 output per million tokens and says typical workloads cost about 40% less to run than Opus 5.
OpenAI
GPT-Image-2.5 Sunburst
CurrentLatestGPT-Image-2.5 Sunburst is OpenAI's September 8, 2026 most capable image generation and editing model. The API ID is gpt-image-2.5-sunburst, with dated snapshot gpt-image-2.5-sunburst-2026-09-08. It takes text and image input and outputs images. OpenAI positions it for workflows where editing precision matters most, with quality settings low, medium, high, xhigh, max, and auto. Call it on the Images API or as the model of the Responses API image generation tool.
Alibaba Qwen
Qwen3.8-27B
CurrentLatestQwen3.8-27B is Qwen's deployment-oriented dense multimodal model for coding, professional work, research, and long-horizon agents. The official repository documents 27B parameters, native image and video understanding, flexible reasoning effort, a 262,144-token native context extensible to 1M, and compatibility with Transformers, vLLM, SGLang, and TokenSpeed.
Meta
Muse Glimmer 30B
CurrentLatestMuse Glimmer 30B is Meta Superintelligence Labs' open-weight multimodal model for local agents, coding, tool use, long-horizon reasoning, and image understanding. Meta's model card documents a dense 29.6B-parameter architecture with a dedicated perception encoder, 131,072+ context, text-and-image input, text output, controllable reasoning effort, and Apache 2.0 weights.
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