LLM API

Anthropic's top model tier, above Opus. 1M-token context window and 128K max output tokens. Priced at $10/$50 per M tokens with prompt caching — cache reads are only $0.25/M (2.5% of the input price) and 5-minute cache writes $12.50/M — plus web search. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's Fable-tier model, positioned above Opus. 1M-token context window and 128K max output tokens. Priced at $10/$50 per M tokens with prompt caching (read $1.00/M, 5-minute write $12.50/M) and web search. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Fast, cost-effective model for everyday tasks. Great balance of speed, intelligence, and cost for high-volume applications.

Anthropic's current Opus model (September 2026), cheaper than Opus 5 at $4/$20 per M tokens. 1M-token context window and 128K max output tokens, with prompt caching (read $0.20/M, i.e. 5% of input; 5-minute write $5.00/M) and web search. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's Opus 5 model (superseded by the cheaper Claude Opus 5.5; Claude Fable 5.1 is the top tier), with a 1M-token context window by default and 128K max output tokens. Same pricing as Opus 4.5–4.8 ($5/$25 per M tokens) with prompt caching (read $0.50/M, write $6.25/M) and web search. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's most capable Opus-tier model, with a 1M-token context window (200K on some surfaces), 128K max output tokens, and knowledge cutoff to January 2026. Builds on Opus 4.7 with stronger long-horizon agentic coding, better tool triggering, and adaptive thinking that reasons only when a turn needs it. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's latest flagship model with reliable knowledge cutoff to January 2026 and 128K max output tokens. Builds on Opus 4.6 with improved reasoning, coding, and instruction-following while staying compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's most capable model. Delivers breakthrough performance in reasoning, coding, and complex analysis with enhanced safety and instruction following.

Anthropic's most powerful model for highly complex tasks. Exceptional at research, analysis, and creative projects requiring deep expertise.

Anthropic's newest Sonnet model (September 2026), balancing speed, cost, and intelligence at $2/$10 per M tokens. 1M-token context window and 128K max output tokens, with prompt caching (read $0.10/M, 5-minute write $2.50/M) and web search. Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

Anthropic's Sonnet 5 model, tuned for the best balance of speed, cost, and intelligence. Features a 1M-token context window, 64K max output tokens, and knowledge cutoff to August 2025. Supports adaptive thinking that reasons only when a turn needs it, vision, and tool use. Priced at $2/$10 per M tokens (cache read $0.20/M). Compatible with the Anthropic Messages and OpenAI Chat Completions formats.

The successor to Claude Sonnet 4.5. Anthropic's frontier Sonnet-tier model for complex reasoning, nuanced understanding, and coding tasks with exceptional instruction following, at the same token pricing as Sonnet 4.5.

Anthropic's most intelligent and capable Sonnet model. Best-in-class for complex reasoning, nuanced understanding, and coding tasks with exceptional instruction following.

DeepSeek's strongest V4 model (checkpoint V4-Pro-0813). Advanced reasoning with thinking mode on by default, a 1M-token context window, and discounted cache reads - still far cheaper than comparable frontier models.

DeepSeek's fast, low-cost model - currently DeepSeek V4.1 Flash, with text and image input. Thinking mode is on by default, with a 1M-token context window, up to 384K output tokens, and cache reads at ~98% off. $0.30 input / $1.20 output per million tokens.

Google's flagship reasoning model (GA 2026-10-13). A 1M-token context window with 65,536 max output tokens (thinking included), multimodal input, adjustable thinking levels and agentic tool use, priced at $4 input / $20 output per million tokens with implicitly cached inputs at $0.20/M.

Google's newest Flash-tier model (released 2026-09-02). A 1M-token context window with 65,536 max output tokens, multimodal input, thinking and tool use, priced at $1.50 input / $7.50 output per million tokens with cached inputs at $0.15/M.

Google's Gemini 3.7 Flash (released 2026-08-13), an earlier release in the Gemini 3.x Flash line - newer Flash generations (3.8) are available. A 1M-token context window with 65,536 max output tokens, multimodal input, thinking and tool use, priced at $1.50 input / $7.50 output per million tokens with cached inputs at $0.15/M.

Google's Gemini 3.6 Flash (released 2026-07-21), an earlier release in the Gemini 3.x Flash line - newer Flash generations (3.7 and 3.8) are available. A 1M-token context window with 65,536 max output tokens, multimodal input, thinking and tool use, priced at $1.50 input / $7.50 output per million tokens with cached inputs at $0.15/M.

Google's latest Flash-tier workhorse model. Flat pricing across all input modalities (text, image, video, audio, PDF) at $1.50/$9 per million tokens with no long-context premium, a 1M token context window, and 65,536 max output tokens. Cached inputs get a 90% discount.

Google's lightweight Gemini 3.5 model (released 2026-07-21) for high-throughput, budget-conscious workloads. $0.30/$2.50 per million tokens with cached input at $0.03/M. Successor to Gemini 3.1 Flash Lite, which Google retires on 2027-05-07.

Generally available (GA) Gemini 3.1 Flash with native image generation - the stable successor to gemini-3.1-flash-image-preview, at the same price. Can generate images directly in chat responses alongside text. Features separate pricing for text and image output tokens.

Gemini 3.1 Flash with native image generation capabilities. Can generate images directly in chat responses alongside text. Features separate pricing for text and image output tokens.

Stable release of the ultra-lightweight Gemini 3.1 Flash Lite. The most cost-effective Gemini model at $0.25/$1.50 per million tokens, with cached input (90% discount), audio input, and batch (50% discount) support. Ideal for high-throughput, budget-conscious applications.

Lightweight, lower-cost sibling of Gemini 3.1 Flash Image with native image generation. Generates images directly in chat responses alongside text, with separate pricing for text and image output tokens: $0.25 input, $1.50 text output and $30 image output per million tokens.

Ultra-lightweight variant of Gemini 3.1 Flash. The most cost-effective Gemini model with support for cached input and audio input. Ideal for high-throughput, budget-conscious applications.

Google's latest and most capable Gemini model in preview. Features dynamic pricing that adjusts based on context length, with enhanced pricing for inputs over 200K tokens.

Google's most advanced reasoning model with state-of-the-art multimodal understanding, PhD-level reasoning, and leading coding performance.

Generally available (GA) premium image generation model in the Gemini 3 Pro family - the stable successor to gemini-3-pro-image-preview, at the same price. Generates high-quality images directly in chat with the highest fidelity among Gemini image models. Image output tokens are priced at 10x text output tokens.

Google's premium image generation model within the Gemini 3 Pro family. Generates high-quality images directly in chat with the highest fidelity among Gemini image models. Image output tokens are priced at 10x text output tokens.

Google's fast and efficient model with built-in thinking capabilities. Great balance of speed, reasoning, and cost for high-volume applications.

The cheapest tier of the Gemini 2.5 family, optimized for high-volume, latency-sensitive workloads. Delivers 2.5-generation quality at a fraction of the cost, ideal for classification, extraction, and real-time chat at scale.

Google's most capable model with state-of-the-art reasoning and 1M token context. Excels at complex coding, math, and multi-document analysis.

Google's second-generation Gemini embedding model and the successor to gemini-embedding-001. Generates vector representations for semantic search, clustering, classification, and RAG at $0.20 per million text input tokens.

Google's text embedding model for generating vector representations. Optimized for semantic search, clustering, and similarity tasks.

Z.ai's GLM 5.3 served through the OpenRouter aggregator (listed 2026-08-18) - a large-scale text reasoning model built for complex software engineering and long-horizon agent tasks, and the successor to GLM 5.2. 1.31M-token context window, billed at OpenRouter's actual usage cost.

Z.ai's GLM 5.3 Flash served through the OpenRouter aggregator (listed 2026-08-26) - a low-cost native multimodal model (text, image, video input) for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention keeps long-context behavior accurate across a 1.31M-token window. Billed at OpenRouter's actual usage cost.

Z.ai's GLM 5.2 served through the OpenRouter aggregator - a large-scale text reasoning model with a 1M-token context window, suited for long-horizon agent workflows and project-level software engineering. Billed at OpenRouter's actual usage cost.

Z.ai's GLM 4.7 served through the OpenRouter aggregator - a flagship text model with enhanced programming capabilities and more stable multi-step reasoning/execution, showing clear gains on complex agent tasks. 200K context, billed at OpenRouter's actual usage cost.

OpenAI's GPT-6.1 Sol — the Sol tier of the GPT-6.1 generation at $2/$10 per million tokens, with cached inputs at a 95% discount ($0.10/M, half of GPT-6 Sol's cached rate). 1.05M token context window (up to 922K input), 128K max output tokens, text and image input. Supports Fast mode and Ultrafast mode. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

OpenAI's GPT-6.1 Terra — a fast, efficient model of the GPT-6.1 generation for visual reasoning, precise computer use and clear, natural writing, at $1/$5 per million tokens with cached inputs at a 95% discount ($0.05/M). 1.05M token context window (up to 922K input), 128K max output tokens, text and image input. Supports Fast mode. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

OpenAI's most capable model (GPT-6 generation, September 2026), built for the hardest end-to-end work: complex reasoning, coding, computer use, research and document creation. 1M token context window, 128K max output tokens, cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

The smallest, lowest-cost tier of OpenAI's GPT-6 generation, at $0.10/$0.50 per million tokens — built for high-volume, latency-sensitive work such as classification, extraction, routing and short-form generation. Cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

The mid tier of OpenAI's GPT-6 generation (in the API since September 14, 2026) — strong reasoning, coding and multimodal performance at $2/$10 per million tokens, a fifth of GPT-6 Astra. 1.05M token context window, 128K max output tokens, cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

The fast, low-cost tier of OpenAI's GPT-5.6 family (GA July 2026). Luna is built for high-throughput workloads at $0.20/$1.20 per million tokens, with a 1M token context window, 128K max output tokens, and cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

The flagship tier of OpenAI's GPT-5.6 family (GA July 2026). Sol delivers the strongest reasoning, coding, and multimodal performance of the generation with a 1M token context window, 128K max output tokens, and cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

The balanced tier of OpenAI's GPT-5.6 family (GA July 2026). Terra offers near-flagship quality at $2/$12 per million tokens (40% of Sol), with a 1M token context window, 128K max output tokens, and cached inputs at a 90% discount. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request.

Maximum-capability tier of the GPT-5.5 family (dated snapshot). Uses more compute to think longer and deliver the most reliable answers on the hardest reasoning, coding and analysis tasks. Served only via the OpenAI Responses API. $30/$180 per million tokens (no cached-input discount); prompts above 272K input tokens bill the whole request at $60/$270.

OpenAI's newest flagship model with a 1.05M token context window and 128K max output tokens. Supports cached inputs at 10× discount and improved reasoning, coding, and multimodal performance over the GPT-5.4 series.

Maximum-capability tier of the GPT-5.4 family (dated snapshot). Uses more compute to think longer and deliver the most reliable answers on the hardest reasoning, coding and analysis tasks. Served only via the OpenAI Responses API. $30/$180 per million tokens (no cached-input discount); prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request ($60/$270 per million tokens).

OpenAI's newest flagship model with 1M context window and 128K output tokens. Delivers top-tier reasoning across all domains with adjustable reasoning effort levels from none to xhigh. Prompts above 272K input tokens are billed at 2x input / 1.5x output for the whole request ($5 / $22.50 per 1M tokens, cached $0.50).

Fast and cost-efficient variant of GPT-5.4 with 400K context window and 128K output tokens. Excellent balance of performance and affordability for everyday tasks.

Ultra-lightweight and fastest GPT-5.4 variant with 400K context and 128K output. Designed for high-throughput, low-latency applications at minimal cost. Supports MCP for tool integration.

Codex model on the GPT-5.3 base, optimized for agentic coding harnesses and served via the OpenAI Responses API. 400K token context window, 128K max output tokens, cached inputs at a 90% discount.

Maximum-capability tier of the GPT-5.2 family (dated snapshot). Thinks longer with more compute to deliver the most reliable answers on the hardest reasoning and coding tasks.

OpenAI's latest and most advanced GPT model. Delivers state-of-the-art performance across reasoning, coding, and creative tasks with enhanced capabilities.

Dated snapshot of GPT-5.1 for reproducible results. Supports cached input tokens for cost savings on repeated context. Ideal for production deployments requiring model version pinning.

OpenAI's latest flagship model. Delivers exceptional performance across reasoning, coding, and creative tasks with a massive 1M token context window and 32K output tokens. Supports vision, function calling, and JSON mode.

Fast and efficient variant of GPT-5. Delivers strong performance across reasoning, coding, and creative tasks with a 1M token context window and 32K output tokens, at a fraction of the cost of GPT-5.

Ultra-fast and lightweight variant of GPT-5. Designed for high-throughput, low-latency applications with a 1M token context window and 32K output tokens at minimal cost.

OpenAI's maximum-capability GPT-5 tier. Uses more compute to think longer and deliver the most reliable answers on the hardest reasoning, coding, and analysis tasks.

OpenAI's most capable model for coding and instruction following. Features a 1M token context window, 32K output tokens, and major improvements in coding, complex prompts, and long-context tasks. 20% cheaper than GPT-4o on output.

A significant leap in small model performance. Matches or exceeds GPT-4o in intelligence while reducing latency by nearly half and cost by 83%. Ideal balance of speed, quality, and affordability.

OpenAI's flagship multimodal model. Industry-leading performance in reasoning, coding, and creative tasks with native vision capabilities and structured output support.

Cost-effective, fast model with strong performance. Best for high-volume tasks where speed and cost matter more than absolute capability.

OpenAI's current audio model with native audio input/output via chat completions - the successor to GPT Audio and GPT Audio Mini, which OpenAI retires on 2027-01-20. Text tokens are $2.50 in / $10 out and audio tokens $32 in / $64 out per million tokens.

Full-size multimodal model with native audio input/output via chat completions. Handles speech in and speech out with strong general intelligence behind it.

Lightweight multimodal model with native audio input/output capabilities. Optimized for voice-based interactions and audio processing tasks.

OpenAI's open-weight gpt-oss-120b, a 117B-parameter Mixture-of-Experts model (5.1B active per forward pass) built for high-reasoning, agentic and general-purpose production use - served through OpenRouter only; it is not available on the direct OpenAI API. 131K-token context, billed at OpenRouter's actual usage cost.

xAI's newest flagship Grok model (API release 2026-09-02). Strongest reasoning and general capability in the Grok family, with a 500K-token context window, text and image input, and adjustable reasoning effort - at the same price as Grok 4.6.

xAI's previous-generation flagship Grok model with a 500K-token context window. Grok 4.7 has since superseded it at the same price.

xAI's coding-focused Grok model with a 500K-token context window - same token rates as Grok 4.6 with cheaper cache reads.

Cost-efficient Grok model with a 1M-token context window - under half the price of the 4.5/4.6 tier.

xAI's Grok 4.3 served through the OpenRouter aggregator - 1M-token context, vision input, billed at OpenRouter's actual usage cost. Prefer the direct grok-4.3 route unless you need the OpenRouter path.

Grok 4.20 Multi-Agent beta (0309 snapshot) - deep-research style multi-agent orchestration with a 1M-token context window. Served through the Responses API only.

Grok 4.20 non-reasoning variant (0309 snapshot) - fast responses with a 1M-token context window at the same rates as the reasoning variant.

Grok 4.20 reasoning variant (0309 snapshot) - chain-of-thought reasoning with a 1M-token context window. Reasoning tokens are billed additively on top of completion tokens.

xAI's agentic-coding Grok model with a 256K-token context window - the lowest rates in the Grok family.

Moonshot AI's Kimi K3 served through the OpenRouter aggregator - a 2.8T-parameter open-weight multimodal reasoning model with a 1M-token context and image/video input, built for complex coding, knowledge work, and long-horizon agentic workflows. Billed at OpenRouter's actual usage cost.

Moonshot AI's Kimi K2.7 Code served through the OpenRouter aggregator - a coding-focused member of the Kimi K2 family built to complete end-to-end programming tasks reliably over long contexts, with a native multimodal mixture-of-experts architecture, 256K context, and image input. Billed at OpenRouter's actual usage cost.

Meta's Llama 3.3 70B instruction-tuned multilingual model served through OpenRouter - text in/text out with 131K-token context, a strong open-weight general-purpose chat model. This id replaces the retired direct llama-3.3-70b-versatile route. Billed at OpenRouter's actual usage cost.

Meta's Llama 3.1 8B instruction-tuned model served through OpenRouter - a fast, efficient small open-weight model with 131K-token context for high-volume, low-cost workloads. This id replaces the retired direct llama-3.1-8b-instant route. Billed at OpenRouter's actual usage cost.

MiniMax's M3 multimodal foundation model served through OpenRouter - text, image and video input, 1M-token context with up to 512K output tokens, suited for long-horizon agentic work and coding. Billed at OpenRouter's actual usage cost.

MiniMax's M2.7 served through OpenRouter - an agentic large language model built for autonomous, real-world productivity with 204K-token context and multi-agent tool use. This id replaces the retired direct MiniMax-M2.7 route. Billed at OpenRouter's actual usage cost.

Mistral Large 3 (2512) served through the OpenRouter aggregator - Mistral's most capable model to date, a sparse mixture-of-experts with 41B active parameters (675B total) released under Apache 2.0, with 256K context and image/file input. Billed at OpenRouter's actual usage cost.

Meta's Muse Spark 1.3 over the direct Meta API - a multimodal reasoning model for agentic work that accepts text, images, video, audio and PDF documents and returns text, with a 1M-token context window and cached-input pricing. Answers are generated after reasoning tokens, so keep max_tokens at 2,048 or more.

The discounted Contributor tier of Meta's Muse Spark 1.3 - same model, about 1/12 of the standard price, on the condition that prompts and responses may be used by Meta to train its models. Do not send personal, confidential or customer data. Shares an upstream limit of 100 requests / 3M tokens per minute.

Meta's Muse Spark 1.1 served through the OpenRouter aggregator - a multimodal reasoning model built for agentic tasks that accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context. Billed at OpenRouter's actual usage cost.

OpenAI's frontier reasoning model. Uses extended chain-of-thought to solve complex problems in science, coding, and math with high accuracy at a competitive price.

Highest-effort version of o3. Thinks longer with more compute to deliver the most reliable answers on the hardest science, math, and coding problems.

Alibaba's Qwen3.8 Max (0902) served through the OpenRouter aggregator - the updated snapshot of Qwen3.8 Max and the id OpenRouter now lists for it (since 2026-09-03), replacing qwen/qwen3.8-max. The flagship of the Qwen3.8 series, a 2.4-trillion-parameter mixture-of-experts multimodal reasoning model for complex reasoning, visual understanding, and agentic work with a 1M-token context, billed at OpenRouter's actual usage cost.

Qwen3.8 27B served through the OpenRouter aggregator - an open-weight dense vision-language model for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking mode and a 256K-token context. Billed at OpenRouter's actual usage cost.

Qwen3.8 2.4T-A95B served through the OpenRouter aggregator - the open-weight sparse mixture-of-experts variant of Qwen3.8 Max, with 95B active parameters out of 2.4T total. Text-only reasoning model with a 1M-token context and up to 262K output tokens, billed at OpenRouter's actual usage cost.

Alibaba's Qwen3.8 Flash served through the OpenRouter aggregator (listed 2026-08-26) - a low-cost multimodal reasoning model and the successor to Qwen3.7 Flash, suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis and long-video analysis. 1M-token context, billed at OpenRouter's actual usage cost.

Alibaba's Qwen3.8 Max served through the OpenRouter aggregator - the flagship of the Qwen3.8 series and GA successor to Qwen3.8 Max Preview. A multimodal reasoning model for complex reasoning, visual understanding, and agentic work with a 1M-token context, billed at OpenRouter's actual usage cost.

Alibaba's Qwen3.7 Flash served through the OpenRouter aggregator - a low-cost vision-language reasoning model for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition and spatial understanding. 1M-token context, billed at OpenRouter's actual usage cost.

Upstage's Solar Pro 4 served through the OpenRouter aggregator - a cost-efficient LLM with a 524K-token context window, built for long-horizon tasks and agentic workflows with strengths in office productivity and document-intensive work. Billed at OpenRouter's actual usage cost.

OpenAI's most capable text embedding model. Generates high-quality vector representations for semantic search, clustering, and similarity tasks where retrieval accuracy matters most.

OpenAI's cost-efficient text embedding model. Generates vector representations for semantic search, clustering, and similarity tasks at very low cost.