Cheaper Inference

Kimi K3 is Moonshot AI's open-weight Mixture-of-Experts model with 2.8T total and 104B active parameters and a 1M-token context window. It accepts text, image and video input and produces text, always reasons with low, high or max effort, and is built for long-horizon coding, knowledge work and reasoning.

DeepSeek V4 Pro is a 1.6T-parameter Mixture-of-Experts model with 49B active parameters and a 1M-token context window. Built for advanced reasoning, coding, and long-horizon agentic workflows with a hybrid attention system for efficient long-context processing.

GPT-6.1 Sol delivers near-Astra performance at a lower cost for complex coding, computer use, and professional work. It has a 1.05M token context window (922K input, 128K output), support for text and image inputs, and reasoning effort from low through max.

MiniMax-M3 is MiniMax's natively multimodal Mixture-of-Experts model with about 428B total and 23B active parameters. It takes text, image and video input and produces text, with a context window of up to 1M tokens, and can also operate a desktop computer.

Claude Fable 5 is Anthropic's Mythos-class model released for general use, built for demanding reasoning and long-horizon agentic work. It takes text and image input and produces text output, with a 1M-token context window, up to 128K output tokens and always-on adaptive thinking.

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.

DeepSeek V4.1 Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters (8B active on input, 16B on output) and a 1M-token context window. It natively processes images and text, with strong reasoning, coding, and agentic performance.

Gemma 4 31B is the dense instruction-tuned model in Google DeepMind's open-weight Gemma 4 family, with about 30.7B parameters. It takes text and image input and produces text output, with a 256K-token context window, configurable thinking, native function calling and pretraining on over 140 languages.

Llama 3.3 70B is Meta's multilingual instruction-tuned text model, released in December 2024. It takes text input and produces text and code with a 128K-token context window, officially supports eight languages, and is built for assistant-style chat.

gpt-oss-120b is OpenAI's open-weight Mixture-of-Experts language model with 117B total and 5.1B active parameters, released under the Apache 2.0 license. It takes text input and produces text output with a 128K-token context window, configurable low, medium and high reasoning, full chain-of-thought access and native tool use including function calling and structured outputs.

DeepSeek-V3.2 uses DeepSeek Sparse Attention for efficient long contexts, with strong reasoning and tool use.

DeepSeek V4 Flash 0731 is the July 31, 2026 official release of DeepSeek V4 Flash, a Mixture-of-Experts model with 284B total and 13B active parameters and a 1M-token context window. It keeps the preview's architecture and size but is re-post-trained for much stronger agentic performance, with low, high and max reasoning effort.

Moonshot AI's open-weight multimodal agentic model has 1T total and 32B active parameters, built for long-horizon coding and tool use.

Qwen's dense 27B vision-language model supports reasoning, coding and tool-driven tasks.

Qwen 3.8 27B is an open-weight dense model from Alibaba's Qwen team with 27B parameters and a vision encoder, using hybrid Gated DeltaNet and gated attention layers. It takes text, image and video input with a native 262K-token context window and is built for coding, professional and research work, and long-horizon agentic tasks, with adjustable thinking effort.

GLM-5.1 is Z.ai's open-weight Mixture-of-Experts model for agentic engineering, with 744B total and 40B active parameters. It takes text input and output with a 200K-token context window and up to 128K output tokens, and is tuned for coding agents and long-running tool use.

GLM-5.2 is Z.ai's open-weight Mixture-of-Experts model for long-horizon tasks and software engineering. It takes text input and output with a 1M-token context window and up to 128K output tokens, and offers multiple thinking effort levels to balance performance and latency.

DeepSeek V4 Pro is a 1.6T-parameter Mixture-of-Experts model with 49B active parameters and a 1M-token context window. Built for advanced reasoning, coding, and long-horizon agentic workflows with a hybrid attention system for efficient long-context processing.

DeepSeek V4.1 Flash is a multimodal Mixture-of-Experts model with 552B backbone parameters (8B active on input, 16B on output) and a 1M-token context window. It natively processes images and text, with strong reasoning, coding, and agentic performance.

Gemma 3 27B Instruct is the 27-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

Gemma 4 26B A4B Instruct is the Mixture-of-Experts model in Google DeepMind's open-weight Gemma 4 family, with 25.2B total and 3.8B active parameters per token. It takes text and image input and produces text output, with a 256K-token context window, configurable thinking and native function calling.

Muse Spark Open is an open-weight reasoning model from Meta for coding and agentic workflows. It supports tool calling and native image understanding in a single checkpoint, using a sparse Mixture-of-Experts architecture with 816B total / 42B active parameters and a 1M-token context window.

MiniMax-M2.5 is MiniMax's open-weight large language model for coding, agentic tool use and search, and office work. It takes text input and produces text output, and is built for complex multi-step agentic tasks and deliverable outputs such as Word, PowerPoint and Excel documents.

MiniMax-M2.7 is MiniMax's open-weight model of about 229B parameters, built for agentic and professional work. It handles software engineering (debugging, refactoring, code security), complex agent harnesses with tool search, and editing of Word, Excel and PowerPoint files.

Kimi K2.5 is Moonshot AI's open-weight, natively multimodal Mixture-of-Experts model with 1T total and 32B active parameters and a 256K-token context window. It accepts text and image input, offers thinking and instant modes, and is built for coding, visual coding, agent swarm workflows and office tasks.

Kimi K2.7 Code is Moonshot AI's coding-focused agentic model built on Kimi K2.6, with 1T total parameters and 32B active parameters. It always operates in thinking mode, supports text and image input, and targets long-horizon software engineering, agentic task decomposition, and multi-turn coding workflows with 256K context.

Kimi K3 is an ultra-large-scale, open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at navigating large repositories, using tools, debugging, and iterating against images, logs, tests, and runtime feedback.

Qwen 3 Next 80B is a Mixture-of-Experts model from Alibaba's Qwen team with 80B total and 3B active parameters, using hybrid Gated DeltaNet and gated attention layers with multi-token prediction for efficient long-context work. It takes text input and output with a native 262K-token context window.

Qwen 3.6 35B A3B is a fast mixture-of-experts model with 35B total parameters and ~3B active per token. Strong at agentic coding, STEM reasoning, and tool use, with a native 256K context window.

Qwen 3.8 2.4T is Alibaba's open-weight 2.4-trillion-parameter MoE model (95B active), with major gains in software engineering, research, and long-horizon agentic tasks. It is text-only, requires thinking mode, and supports a 262K-token context window.

Qwen3-VL 235B A22B Instruct is Alibaba's Qwen vision-language Mixture-of-Experts model with about 236B total and 22B active parameters. It takes image, video and text input and produces text, with a native 256K-token context, OCR in 32 languages, GUI agent abilities and long-video understanding.

Grok 4.20 Multi-Agent is a variant of xAI Grok 4.20 designed for collaborative, agent-based workflows. Multiple agents operate in parallel to conduct deep research, coordinate tool use, and synthesize information across complex tasks.

Grok 4.3 is xAI's fast, reliable model with strong tool calling and instruction following, suited to agentic workflows. It takes text and image input and produces text output, with a 1M-token context window, function calling, structured outputs and reasoning effort from none to xhigh.

Grok 4.5 is xAI's intelligent coding model for agentic software engineering and workflow tasks, with function calling, structured outputs, and a 500K-token context window.

Grok 4.6 is xAI's model for coding, agentic tasks and knowledge work, designed for long, multi-step work such as research, codebase tasks and turning an idea into a working application. It takes text and image input and produces text output, with a 500K-token context window, function calling, structured outputs and low to xhigh reasoning effort.

Grok 4.7 is xAI's model for software engineering and professional knowledge work, especially long-running tasks. Compared with Grok 4.6 it uses a larger base model trained longer on harder tasks and checks its own output more carefully, and it takes text and image input with a 500K-token context window and low to xhigh reasoning effort.

xAI's fast coding model trained specifically for agentic coding, currently in early access.

MiMo-V2.6-Flash is Xiaomi's efficiency-balanced omnimodal model: a sparse Mixture-of-Experts with 309B total and 15B activated parameters, natively understanding text, image, video, and audio with a 1M-token context window. Trained with mixed reinforcement learning across coding, general agents, visual, and cybersecurity tasks, it is built for long-horizon coding, tool use, and computer use.

GLM-4.6 is Z.ai's open-weight Mixture-of-Experts model with 355B total and 32B active parameters. It takes text input and output with a 200K-token context window and up to 128K output tokens, and is built for agentic coding, front-end development, reasoning, writing and role-play.

GLM-4.7 is Z.ai's open-weight Mixture-of-Experts model with 355B total and 32B active parameters. It takes text input and output with a 200K-token context window and up to 128K output tokens, and is built for agentic coding, UI generation, tool use, reasoning and writing.

GLM-5 is Z.ai's open-weight Mixture-of-Experts model with 744B total and 40B active parameters, built for complex systems engineering and long-horizon agentic tasks. It takes text input and output with a 200K-token context window and up to 128K output tokens.

The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528.

DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2.

DeepSeek V3.1 is DeepSeek's hybrid Mixture-of-Experts model with 671B total and 37B active parameters, supporting both thinking and non-thinking modes in a single model. It has a 128K-token context window and improves tool calling and agent tasks over DeepSeek V3.

DeepSeek V4 Flash (Preview) is the April 2026 preview release of DeepSeek's efficiency-focused Mixture-of-Experts model, with 284B total and 13B active parameters and a 1M-token context window. It offers a non-thinking mode and two thinking levels (high and max) for fast reasoning and coding workloads.

DeepSeek-V4-Flash-Vision-Exp is DeepSeek's experimental multimodal model in the V4-Flash family, adding visual understanding to the V4-Flash architecture. It serves a 1M-token (1,048,576) context window and supports image input with visual grounding, tool calling, structured/JSON output, and configurable reasoning effort (low/high/max, or disabled).

Gemma 3 12B Instruct is the 12-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

Gemma 3 4B Instruct is the 4-billion-parameter instruction-tuned model in Google DeepMind's Gemma 3 family of open-weight multimodal models. It takes text and image input and produces text output, with a 128K-token context window and support for over 140 languages, for tasks such as chat, summarization, question answering and image understanding.

Ling 3.0 Flash is inclusionAI's hybrid reasoning model, a hybrid-linear Mixture-of-Experts with 124B total and 5.1B active parameters built for efficient agentic workflows such as coding and deep research. It takes text input and output with a 256K-token context window, and its thinking mode can be turned off per request.

Ling 3.0 Flash VL is inclusionAI's multimodal version of Ling 3.0 Flash, a Mixture-of-Experts model with 124B total and 5.5B active parameters. It takes text, image and video input and produces text with a context of up to 256K tokens, and is built for document and chart understanding, GUI agents and long-video question answering.

Ling 3.1 Flash is inclusionAI's native hybrid reasoning model, a hybrid-linear Mixture-of-Experts with 560B total and 25B active parameters that builds on Ling 3.0 Flash. It takes text input and output with a 1M-token context window, is designed for complex real-world tasks, and lets thinking be turned on or off.

Muse Glimmer 30B is an open-weight multimodal model from Meta Superintelligence Lab, distilled from Muse Spark. It is a dense 30B transformer that takes text and image input and produces text with a context window of at least 131K tokens, built for local and coding agents, tool calling and multimodal reasoning on a single consumer GPU or Mac.

gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

Qwen3.5-35B-A3B is an efficient Mixture-of-Experts model from Alibaba's Qwen3.5 series with 35B total parameters and only 3B activated per token. It features a 262K token context window (extensible to 1M with YaRN), thinking/reasoning mode, tool calling, and support for 201 languages. Delivers strong performance on reasoning, coding, and vision-language tasks at a fraction of the compute cost.

Qwen3 30B A3B is a Mixture-of-Experts model in the Qwen3 family with 30.5B total and 3.3B active parameters from Alibaba's Qwen team, released in April 2025. It switches between a thinking mode for complex reasoning and a fast non-thinking mode, supports 119 languages, and has a native 32K-token context window (131K with YaRN).

Qwen3.5-27B is Alibaba's largest dense Qwen3.5 model, delivering near-frontier quality across reasoning, coding, and instruction following. It features a 262K token context window (extensible to 1M), thinking/reasoning mode, tool calling, multi-token prediction, and support for 201 languages. Best suited for production deployments and complex enterprise tasks requiring top-tier performance.

Inkling Small is a smaller model in Thinking Machines Lab's Inkling family, a Mixture-of-Experts with 276B total and 12B active parameters. Like Inkling, it accepts text, image and audio input and generates text, with a context of up to 1M tokens and variable thinking effort.

MiMo-V2.6-Pro is the flagship of Xiaomi's MiMo-V2.6 series: a sparse Mixture-of-Experts model with 1.02T total and 42B activated parameters, natively omnimodal across text, image, video and audio, with a 1M-token context window. Trained with large-scale mixed reinforcement learning and groupwise agentic grading, it is built for demanding agentic work including long-horizon coding, multi-tool agents and cybersecurity tasks.

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.

MiniMax-M2 is MiniMax's open-weight Mixture-of-Experts language model built for coding and agentic workflows, with 230 billion total parameters and 10 billion active per token. It takes text input and produces text output, with a context window of 204,800 tokens. It is designed for multi-file edits, code-run-fix loops and test-validated repairs, and for long tool-calling sequences across shell, browser, retrieval and code-execution tools.

MiniMax-M2.1 is MiniMax's open-weight Mixture-of-Experts language model, an update to MiniMax-M2 focused on real-world coding and agentic tasks. It takes text input and produces text output. MiniMax highlights multi-language programming across Rust, Java, Go, C++, Kotlin, Objective-C, TypeScript and JavaScript, native Android and iOS development, web and app development, tool use and long-horizon planning, and multi-step office workflows.

Kimi K2 Thinking is Moonshot AI's open-weight thinking model, a Mixture-of-Experts model with about 1 trillion total parameters and 32 billion active per token. It takes text input and produces text output, with a 256K-token context window. It is built to interleave step-by-step reasoning with function calls, and is designed to stay goal-directed across 200 to 300 sequential tool calls for research, coding and writing tasks.

Qwen3 Coder 30B A3B Instruct is an open-weight Mixture-of-Experts coding model from the Qwen team. It has 30.5B total parameters, with 3.3B active per token (8 of 128 experts), and a native 262,144-token context window. It is a text-only, non-thinking model built for agentic coding, agentic browser use and repository-scale code understanding, with a dedicated function-calling format, and is released under Apache 2.0.

Qwen3 Coder Next is an open-weight coding model from Alibaba's Qwen team, built for coding agents and local development on the Qwen3-Next hybrid-attention Mixture-of-Experts architecture with 80B total and 3B active parameters. It has a native 262K-token context window and is aimed at agentic coding, tool calling and recovery from execution failures.

GLM 4.7 Flash is a text model.

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 such as long code refactors, front-end development, and finance and analysis tasks. It features a 1M token context window, 128K max output tokens, always-on adaptive thinking, and strong multimodal capabilities, and tends to be more concise in its plans and summaries.

Claude Haiku 5.5 is built for high-volume, latency-sensitive work such as classification, routing, extraction, and subagent tasks. It features a 1M token context window, 128K max output tokens, adaptive thinking, and strong document and vision understanding.

Claude Opus 4.5 is Anthropic's frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and reasoning benchmarks, and improved robustness to prompt injection.

Claude Opus 4.6 is Anthropic's Opus model for demanding coding, long-running agentic work and knowledge work such as research, financial analysis and building documents. It takes text and image input and produces text output, with a 1M-token context window, up to 128K output tokens and adaptive thinking with effort controls.

Claude Opus 4.7 is Anthropic's Opus model for advanced software engineering, especially complex, long-running coding tasks. It builds on Opus 4.6 with more precise instruction following, verification of its own outputs and higher-resolution vision, and offers a 1M-token context window, up to 128K output tokens and adaptive thinking.

Claude Opus 4.8 is Anthropic's Opus model for dependable, long-running agentic work and coding. It builds on Opus 4.7 with sharper judgment and more candid flagging of doubts about its own output, and takes text and image input with a 1M-token context window, up to 128K output tokens and adaptive thinking.

Claude Opus 4.8 Fast is Claude Opus 4.8 run in Anthropic's fast mode, the same model weights with a faster inference configuration that raises output speed by up to 2.5x. Capabilities are unchanged: text and image input, text output, a 1M-token context window and adaptive thinking for agentic work and complex coding.

Claude Opus 5 is Anthropic's Opus model for daily professional work, with gains over Opus 4.8 in software engineering, knowledge work and scientific research. It checks its own work and iterates until a task succeeds, and takes text and image input with a 1M-token context window, up to 128K output tokens and adaptive thinking.

Claude Opus 5 Fast is Claude Opus 5 run in Anthropic's fast mode, the same model weights with a faster inference configuration that raises output speed by up to 2.5x. Capabilities are unchanged: text and image input, text output, a 1M-token context window and adaptive thinking for coding, knowledge work and research.

Claude Opus 5.5 is Anthropic's Opus model for long-running agentic coding and knowledge work. Compared with Opus 5 it uses fewer tokens per task and writes more clearly, and it takes text and image input with a 1M-token context window, up to 128K output tokens and always-on adaptive thinking.

Claude Sonnet 4.6 is Anthropic's best combination of speed and intelligence, offering strong performance on coding, reasoning, and general tasks with excellent speed and cost efficiency. It features a 1M token context window and 64K max output tokens.

Claude Sonnet 5 is Anthropic's Sonnet model built for agentic work: planning, using tools such as browsers and terminals, coding, reasoning and knowledge work. It takes text and image input and produces text, with a 1M-token context window, up to 128K output tokens and adaptive thinking, and improves on Sonnet 4.6.

Claude Sonnet 5.5 is Anthropic's Sonnet model, balancing speed and intelligence for well-scoped everyday work such as building features, fixing bugs and creating documents, slides and spreadsheets. It improves on Sonnet 5 in coding, token efficiency and clear writing, with a 1M-token context window, up to 128K output tokens and adaptive thinking.

Seed 2.1 Turbo is one of ByteDance Seed's two Seed 2.1 models (with Pro), an agent model for general agents, end-to-end coding and multimodal understanding. It understands text, images and video with a 256K-token context window and supports deep thinking, GUI tasks, tool calling and structured output.

Gemini 3.1 Pro Preview is the preview release of Google's Gemini 3.1 Pro, a reasoning model for software engineering and agentic workflows that need precise tool use. It refines the Gemini 3 Pro series with better thinking, improved token efficiency and more grounded, factually consistent answers. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window and up to 65K output tokens.

Gemini 3.5 Flash is a Flash model in Google's Gemini 3 series, optimized for real-world tasks at higher speed and lower cost. It supports thinking. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window and up to 65K output tokens.

Gemini 3.5 Flash-Lite is the fastest, most cost-efficient Gemini 3.5 model with 1M context, ideal for everyday questions, summarization, and lightweight coding tasks.

Gemini 3.6 Flash is a Flash model in Google's Gemini 3 series designed for agentic work, with strengths in code generation, agentic execution and spatial reasoning. It suits fast agentic loops with complex coding cycles. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window and up to 65K output tokens.

Gemini 3.7 Flash is a Flash model in Google's Gemini 3 series of natively multimodal reasoning models, built as a workhorse for coding and agentic work. It improves on Gemini 3.6 Flash in coding and web development, supports low, medium and high thinking levels, and has a 1M-token context window with up to 65K output tokens.

Gemini 3.8 Flash is a Flash model in Google's Gemini 3 series, engineered for long-horizon software engineering, autonomous agents and complex enterprise workflows at Flash-level speed and cost. It takes text, image, video, audio and PDF input and produces text output, with a 1M-token context window, up to 65K output tokens and low, medium and high thinking levels.

Gemini 4 Argon is Google's frontier model for complex workflows across software engineering, enterprise knowledge work, and cybersecurity defense, with a unified 1M token context window and tunable thinking.

Mercury 2 is Inception's diffusion-based reasoning language model, which generates by refining many tokens in parallel rather than one at a time. It takes text input and output with a 128K-token context window and supports tunable reasoning depth, native tool calling and schema-aligned JSON output.

MiniMax M3.1 Flash is a frontier model for coding, agentic workflows, and complex reasoning, with a 1M-token context window, text, image, and video input, and tunable thinking depth from low to max (max by default).

OpenAI's multimodal model with vision capabilities, strong reasoning, and broad knowledge. Popular for its balanced performance across tasks. Version: 2024-11-20.

OpenAI's cost-efficient small model that delivers GPT-4 level intelligence at a fraction of the cost. Ideal for high-volume applications requiring strong reasoning. Version: 2024-07-18.

GPT-5.2 is OpenAI's GPT-5 series model for complex professional work. It takes text and image input and produces text output, with a 400K-token context window, up to 128K output tokens and reasoning effort from none to xhigh.

GPT-5.3 Codex is OpenAI's coding model built on GPT-5.3 and optimized for agentic coding tasks in Codex and similar environments. It takes text and image input and produces text output, with a 400K-token context window, up to 128K output tokens and low to xhigh reasoning effort.

GPT-5.4 is OpenAI's GPT-5 series model for coding and complex professional work. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to xhigh.

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.

GPT-5.5 is OpenAI's GPT-5 series model for coding and complex professional work. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to xhigh.

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, built for complex professional work such as reasoning, coding and agentic workflows. It takes text and image input and produces text output, with a context window of about 1.05M tokens, up to 128K output tokens and reasoning effort from none to max.

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.

GPT-6 Astra is the top tier of OpenAI's GPT-6 family, built for demanding work such as complex reasoning, coding, computer use, research and document creation. It takes text and image input and produces text, with a 1.05M-token context window (up to 922K input and 128K output tokens) and configurable reasoning effort.

GPT-6 Luna is OpenAI's most efficient GPT-6 model for focused, high-volume tasks. It is suited for chat, classification, and lightweight agentic workflows, with a 1.05M token context window (922K input, 128K output), text and image inputs, and capable reasoning for its price tier.

GPT-6 Sol is built for complex coding and agentic workflows. It is OpenAI's GPT-6 coding and agent model, with a 1.05M token context window (922K input, 128K output), support for text and image inputs, and reasoning effort from none through max.

Qwen 3.8 Flash is an efficient multimodal model from Alibaba's Qwen team, built on the open-weight Qwen3.8 Flash Next Mixture-of-Experts model with 125B total and 6B active parameters. It accepts text, image and video input with a 1M-token context window, supports function calling and structured output, and has a thinking mode that can be turned off.

Qwen 3.8 Max is Alibaba's flagship 2.4-trillion-parameter MoE model, with major gains over Qwen 3.7 Max in software engineering and office-productivity workflows and strong long-horizon, multi-agent performance. It accepts both text and vision-language input (images and video), operates in thinking mode only, and supports a 1M-token context window.

GLM-5 Turbo is a fast inference model from Z.ai tuned for strong performance in agent-driven environments and production coding workflows.

GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks with image and text inputs.