Amux Special

Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token context window. It is suited for long-running, complex, and asynchronous tasks that previously required frequent human check-ins.

Input:

Output:

Input:

$10$1.5/1M

Output:

$50$7.5/1M

Context length:

1M

Max output:

128K

Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual code generation, and finance and analysis tasks in particular. It also tends to be more concise than Fable 5 in its plans and summaries. We recommend testing it as a direct upgrade wherever you use Fable 5 today, and alongside Opus 5 on reasoning-heavy tasks.

Input:

Output:

Input:

$10$1.5/1M

Output:

$50$7.5/1M

Context length:

1M

Max output:

128K

Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance across reasoning, coding, and computer-use tasks, Haiku 4.5 brings frontier-level capability to real-time and high-volume applications.

Input:

Output:

Input:

$1$0.05/1M

Output:

$5$0.25/1M

Context length:

200K

Max output:

64K

Anthropic: Claude Haiku 5.5 is Anthropic’s most affordable, fastest and most powerful small form factor model yet, built for high-volume, cost-sensitive workloads. It handles summarization, reduction, database query, classification, and subagent encoding tasks with low latency, making it particularly useful in real-time customer support, browser usage, and other speed-critical applications.

Input:

Output:

Input:

$0.1$0.015/1M

Output:

$0.5$0.075/1M

Context length:

1M

Max output:

128K

Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective for large codebases, complex refactors, and multi-step debugging that unfolds over time. The model shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks than prior generations.

Input:

Output:

Input:

$5$0.25/1M

Output:

$25$1.25/1M

Context length:

1M

Max output:

128K

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable agentic execution across extended workflows. It is especially effective for asynchronous agent pipelines where tasks unfold over time - large codebases, multi-stage debugging, and end-to-end project orchestration.

Input:

Output:

Input:

$5$0.25/1M

Output:

$25$1.25/1M

Context length:

1M

Max output:

128K

Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token context window. It is suited for highly autonomous agents, long-horizon agentic work, knowledge work, and memory-driven tasks where coherence over extended sessions matters.

Input:

Output:

Input:

$5$0.25/1M

Output:

$25$1.25/1M

Context length:

1M

Max output:

128K

Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis of charts and documents, complex office deliverables, and coordinating parallel subagents.

Input:

Output:

Input:

$5$0.25/1M

Output:

$25$1.25/1M

Context length:

1M

Max output:

128K

Claude Opus 5.5 is Anthropic's flagship model for demanding reasoning, coding, and long-horizon agentic work, succeeding Claude Opus 5. It is particularly strong at multi-step changes in large codebases, code review and bug finding, financial and scientific analysis, and reading dense charts, diagrams, and screenshots, and it is more careful than its predecessor about only stating figures and citing sources it can back up. The model completes comparable tasks in fewer steps and with fewer tokens than Opus 5, and reports on its work in plainer language, with clear updates on what it did, what it found, and what it needs from the user. Thinking is always adaptive, so effort is the main lever for trading off depth, latency, and cost, and lower effort settings remain effective for latency-sensitive workloads.

Input:

Output:

Input:

$4$0.2/1M

Output:

$20$1/1M

Context length:

1M

Max output:

128K

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Input:

Output:

Input:

$3$0.15/1M

Output:

$15$0.75/1M

Context length:

1M

Max output:

128K

Claude Sonnet 5 is built to be the most agentic Sonnet model yet. It can plan, use tools such as browsers and terminals, and operate autonomously at a level that only recently required larger and more expensive models.

Input:

Output:

Input:

$3$0.15/1M

Output:

$15$0.75/1M

Context length:

1M

Max output:

128K

Anthropic: Claude Sonnet 5.5 is Anthropic’s upgraded Sonnet-class model that delivers greater performance than Claude Sonnet 5 while running more than 30% faster and at a lower cost for many workloads. Designed for agent-based coding, tool usage, and everyday professional tasks, it's ideal for developers and teams building fast and powerful AI agents.

Input:

Output:

Input:

$2$0.1/1M

Output:

$10$0.5/1M

Context length:

1M

Max output:

128K

Seedance 2.0 is a video generation model from ByteDance. It supports text-to-video, image-to-video with first and last frame control, and multimodal reference-to-video. It is particularly strong at preserving character consistency, visual style, and camera movement from reference material. The number of tokens is given by (height of output video * width of output video * duration * 24) / 1024

Input:

Output:

Input:

—

Output:

$2.4-7.7$1.68-6.622/1M

Context length:

—

Max output:

—

Seedance 2.0 Fast is a video generation model from ByteDance. It supports text-to-video, image-to-video with first and last frame control, and multimodal reference-to-video. It prioritizes generation speed and lower cost over maximum output quality. The number of tokens is given by (height of output video * width of output video * duration * 24) / 1024

Input:

Output:

Input:

—

Output:

$3.3-5.6$2.31-5.04/1M

Context length:

—

Max output:

—

Seedance 2.0 Mini is a video generation model from ByteDance. It supports text-to-video, image-to-video with first and last frame control, and multimodal reference-to-video with image, video, and audio inputs. It supports 480p and 720p output for 4-15 second videos. The number of tokens is given by (height of output video * width of output video * duration * 24) / 1024

Input:

Output:

Input:

—

Output:

$2.1-3.5$1.47-3.15/1M

Context length:

—

Max output:

—

Seedance 2.5 is a video generation model from ByteDance. It is suited for long-form storytelling, multimodal reference-based generation, video editing, and video extension. It supports first-frame and first-and-last-frame control, up to 50 image, video, and audio reference assets, optional generated audio, and multilingual audiovisual generation.

Input:

Output:

Input:

—

Output:

$6.4-11.7$4.48-10.062/1M

Context length:

—

Max output:

—

Gemini 2.5 Flash Image (also known as "Nano Banana") is now generally available. It is a cutting-edge image generation model with context understanding that supports image generation, editing, and multi-turn conversations.

Input:

Output:

Input:

$0.3$0.255/1M

Output:

$2.5$2.125/1M

Context length:

33K

Max output:

8K

Google: Nano Banana 2 (Gemini 3.1 Flash Image) is designed for speed and efficiency, enabling fast, interactive responses and high throughput.

Input:

Output:

Input:

$0.5$0.425/1M

Output:

$3$2.55/1M

Context length:

66K

Max output:

33K

Google: Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image) is Google's fastest and most cost-effective Gemini image model, built for high-frequency development pipelines and rapid visual exploration. It can complete text-to-image generation in about 5 seconds, which is about 2.7 times faster than Gemini 3.1 Flash Image, while maintaining the character consistency, precise editing capabilities and real-world knowledge of the Nano Banana series. A single embedded API handles text-to-image, image editing, and multi-image composition. As a multi-modal model, it can also generate text while returning images. Output resolution is 1K, supports 14 aspect ratios, and comes with an invisible SynthID watermark for easy identification as AI-generated. The best balance of quality and speed in the Nano Banana 2 series, it generates thousands of images at a fraction of the cost of heavyweight production models, making it ideal for prototyping, real-time applications and large-scale visual workflows.

Input:

Output:

Input:

$0.25$0.2125/1M

Output:

$1.5$1.275/1M

Context length:

66K

Max output:

33K

Gemini 3.1 Pro is the next generation in the Gemini series of models, a suite of highly-capable, natively multimodal, reasoning models. Gemini 3 Pro is now Google’s most advanced model for complex tasks, and can comprehend vast datasets, challenging problems from different information sources, including text, audio, images, video, and entire code repositories

Input:

Output:

Input:

$2$0.2/1M

Output:

$12$1.2/1M

Context length:

1.05M

Max output:

66K

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and less hedging, while reducing token use and the number of model calls needed to complete a task.

Input:

Output:

Input:

$1.5$0.15/1M

Output:

$7.5$0.75/1M

Context length:

1.05M

Max output:

66K

Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step problem solving.

Input:

Output:

Input:

$1.5$0.15/1M

Output:

$7.5$0.75/1M

Context length:

1.05M

Max output:

66K

Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.

Input:

Output:

Input:

$1.5$0.15/1M

Output:

$7.5$0.75/1M

Context length:

1.05M

Max output:

66K

Google: Nano Banana Pro (Gemini 3 Pro Image) is an AI image generation model based on Google Gemini 3 Pro, and is a major upgrade of its previous products. It aims to go beyond simple pattern matching, turn to a more rationally driven system, and make improvements in terms of physical understanding, text rendering, and image consistency. Its main features include faster processing speed, native 2K resolution, and the ability to edit existing images with higher precision, aiming to provide more reliable, professional-level generated results.

Input:

Output:

Input:

$2$1.7/1M

Output:

$12$10.2/1M

Context length:

66K

Max output:

8K

Google: Gemini 4 Argon is a cutting-edge model released by Google DeepMind on September 30, 2026. It is the first model in the Gemini 4 series to feature a new naming scheme. It is strong in programming, financial research, legal drafting, and autonomously fixing cybersecurity vulnerabilities. The model supports text, image, video, and file inputs, and has a context window of 1 million tokens. In benchmark tests, its DeepSWE v1.1 score was 77.9%, ahead of Claude Opus 5.5 and GPT-6 Astra, and ranked first in the Vals Index with 68.90%.

Input:

Output:

Input:

$4$0.4/1M

Output:

$20$2/1M

Context length:

1.05M

Max output:

66K

MoonshotAI: Kimi K3 is Kimi’s most powerful model to date, with 2.8 trillion parameters. It is built based on the hybrid linear attention mechanism Kimi Delta Attention and attention residual technology. It has native visual understanding capabilities and a context window of 1 million tokens. It is suitable for cutting-edge intelligent scenarios such as software engineering, knowledge work, and deep reasoning.

Input:

Output:

Input:

$3$0.9/1M

Output:

$15$4.5/1M

Context length:

1.05M

Max output:

1.05M

GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs, enabling high-context reasoning, coding, and multimodal analysis within the same workflow.

Input:

Output:

Input:

$2.5$2/1M

Output:

$15$12/1M

Context length:

1.05M

Max output:

128K

GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding, and tool use, while reducing latency and cost for large-scale deployments.

Input:

Output:

Input:

$0.75$0.6/1M

Output:

$4.5$3.6/1M

Context length:

400K

Max output:

128K

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs, enabling large-scale reasoning, coding, and multimodal workflows within a single system.

Input:

Output:

Input:

$5$4/1M

Output:

$30$24/1M

Context length:

1.05M

Max output:

128K

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for its price tier.

Input:

Output:

Input:

$0.2$0.02/1M

Output:

$1.2$0.12/1M

Context length:

1.05M

Max output:

128K

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks and long-horizon problem solving.

Input:

Output:

Input:

$4$0.2/1M

Output:

$20$1/1M

Context length:

1.05M

Max output:

128K

GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is suited for everyday coding, reasoning, and agentic tasks where capability and cost need to be balanced, offering strong performance at roughly half the cost of Sol.

Input:

Output:

Input:

$2$1.6/1M

Output:

$12$9.6/1M

Context length:

1.05M

Max output:

128K

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

Input:

Output:

Input:

$2$0.1/1M

Output:

$10$0.5/1M

Context length:

1.05M

Max output:

128K

GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon agentic tasks that involve computer and browser use.

Input:

Output:

Input:

$10$0.5/1M

Output:

$50$2.5/1M

Context length:

1.05M

Max output:

128K

OpenAI: GPT-6 Luna is a fast and cost-effective model in the OpenAI GPT-6 series, positioned lower than GPT-6 Sol. It is suitable for high-concurrency and latency-sensitive workloads such as chat, classification, and lightweight agent tasks; at higher inference workloads, it can take on complex software engineering and computer usage tasks that previously required Sol-level models. It shares the improvements in factual reliability of the GPT-6 family, as well as a clearer, more concise communication style.

Input:

Output:

Input:

$0.1$0.01/1M

Output:

$0.5$0.05/1M

Context length:

1.05M

Max output:

128K

OpenAI: GPT-6 Sol is a cost-effective high-end model in the OpenAI GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna. It is suitable for demanding professional work, agency coding, business workflow automation and computer use, and is particularly good at long-span software engineering tasks in real code bases. It delivers near Astra-level reliability at a fraction of the cost and continues Astra’s clear, concise communication style in technical and programming conversations.

Input:

Output:

Input:

$2$0.1/1M

Output:

$10$0.5/1M

Context length:

1.05M

Max output:

128K

OpenAI's latest image generation model. Supports high-fidelity image generation and editing via the dedicated Images API.

Input:

Output:

Input:

$5$4/1M

Output:

$30$24/1M

Context length:

10K

Max output:

—

GPT Image 2.5 Flare is an image generation and editing model from OpenAI, positioned as the speed-oriented tier of the GPT Image 2.5 series. It is suited to high-volume everyday generation, creator content, and rapid prototyping via the dedicated Images API.

Input:

Output:

Input:

$5$4/1M

Output:

$30$24/1M

Context length:

10K

Max output:

—

GPT Image 2.5 Sunburst is an image generation and editing model from OpenAI, positioned as the precision-oriented tier of the GPT Image 2.5 series. It is suited to detailed creative work where editing accuracy matters more than generation speed, via the dedicated Images API.

Input:

Output:

Input:

$5$4/1M

Output:

$30$24/1M

Context length:

10K

Max output:

—

Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications requiring high factual accuracy. Reasoning can be configured between none/low/medium/high (default low) effort levels.

Input:

Output:

Input:

$1.25$0.0625/1M

Output:

$2.5$0.125/1M

Context length:

1M

Max output:

1M

Grok 4.5 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM.

Input:

Output:

Input:

$2$0.1/1M

Output:

$6$0.3/1M

Context length:

500K

Max output:

500K

Grok 4.6 is SpaceXAI's smartest model with frontier performance on coding, knowledge work, and STEM.

Input:

Output:

Input:

$2$0.1/1M

Output:

$6$0.3/1M

Context length:

500K

Max output:

500K

Grok 4.7 is SpaceXAI's flagship model for coding, agentic tasks, and knowledge work, succeeding Grok 4.6. It is particularly strong at long-running software engineering tasks, verifying its own work, and managing long context, and it improves on its predecessor at professional knowledge work such as drafting documents and presentations.

Input:

Output:

Input:

$2$0.1/1M

Output:

$6$0.3/1M

Context length:

500K

Max output:

500K

Grok Build 0.1 is SpaceXAI’s fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized for interactive coding agents, tool use, and multi-step development tasks. The model powers SpaceXAI’s Grok Build CLI and features a 256K context window with no text output limit, making it well suited for long-horizon coding and automation workflows. Currently in early access.

Input:

Output:

Input:

$1$0.05/1M

Output:

$2$0.1/1M

Context length:

256K

Max output:

256K

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering, and complex multi-step automation.

Input:

Output:

Input:

$1.4$0.42/1M

Output:

$4.4$1.32/1M

Context length:

1M

Max output:

128K

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency.

Input:

Output:

Input:

$1.4$0.42/1M

Output:

$4.4$1.32/1M

Context length:

1M

Max output:

128K

GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.

Input:

Output:

Input:

$0.15$0.045/1M

Output:

$0.5$0.15/1M

Context length:

1M

Max output:

128K

Z.AI: GLM 5.3 FlashX is a faster, smoother version of GLM-5.3-Flash with inference speeds up to 200 tokens/s. Z.AI: GLM 5.3 FlashX is Z.ai’s native multi-modal model, suitable for efficient programming and long-range agent tasks. Its hybrid sparse and linear attention architecture reduces computational overhead while maintaining accurate long-context performance.

Input:

Output:

Input:

$0.375$0.1125/1M

Output:

$1.25$0.375/1M

Context length:

1M

Max output:

128K