Alternatives to Mercury Voice
Compare Mercury Voice alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Mercury Voice in 2026. Compare features, ratings, user reviews, pricing, and more from Mercury Voice competitors and alternatives in order to make an informed decision for your business.
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1
Kimi K3
Moonshot AI
Kimi K3 is Moonshot AI’s most capable model, built for frontier intelligence scenarios such as software engineering, knowledge work, deep reasoning, and multimodal understanding. The model has 2.8 trillion parameters and uses Kimi Delta Attention, a hybrid linear attention mechanism, along with Attention Residuals for long-context performance. Kimi K3 supports a 1 million token context window, making it useful for analyzing large codebases, long documents, complex knowledge bases, and multi-step workflows. It includes native visual understanding for images and videos, with support for structured message formats, base64 image input, uploaded video files, and multimodal reasoning. Developers can use Kimi K3 through an OpenAI-compatible API with support for streaming, structured JSON output, partial mode, custom tools, dynamic tool loading, and automatic context caching.Starting Price: $3 per 1M tokens (input) -
2
Nemotron 3 Ultra
NVIDIA
Nemotron 3 Nano is a compact, open large language model in NVIDIA’s Nemotron 3 family, designed for efficient agentic reasoning, conversational AI, and coding tasks. It uses a hybrid Mixture-of-Experts Mamba-Transformer architecture that activates only a small subset of parameters per token, enabling low-latency inference while maintaining strong accuracy and reasoning performance. It has approximately 31.6 billion total parameters with around 3.2 billion active (3.6 billion including embeddings), allowing it to achieve higher accuracy than previous Nemotron 2 Nano while using less computation per forward pass. Nemotron 3 Nano supports long-context processing of up to one million tokens, enabling it to handle large documents, multi-step workflows, and extended reasoning chains in a single pass. It is designed for high-throughput, real-time execution, excelling in multi-turn conversations, tool calling, and agent-based workflows where tasks require planning, reasoning, and more. -
3
Gemini 3.5 Flash
Google
Gemini 3.5 Flash is Google’s latest frontier AI model designed to combine advanced intelligence, high-speed performance, and agentic workflow execution for developers, enterprises, and everyday users. Built as part of the Gemini 3.5 family, the model excels at coding, long-horizon reasoning, multimodal understanding, and complex multi-step automation tasks while delivering significantly faster output speeds than many competing frontier models. Gemini 3.5 Flash powers AI agents capable of planning, executing, and managing workflows such as application development, codebase maintenance, data analysis, and financial document preparation through the Antigravity harness. The model also supports rich multimodal experiences by generating interactive graphics, dynamic web interfaces, animations, and advanced visual content. Gemini 3.5 Flash is integrated across Google products including the Gemini app, Google Search AI Mode, Google Antigravity, Google AI Studio, Android Studio, and more.Starting Price: $1.50 per 1M tokens (input) -
4
MiniMax M3
MiniMax
MiniMax M3 is an open-weight multimodal AI model designed for coding, agentic workflows, long-context reasoning, and complex automation tasks. The model combines frontier-level coding performance, native multimodal understanding, and a context window of up to 1 million tokens. MiniMax M3 uses MiniMax Sparse Attention to improve long-context efficiency while reducing compute requirements for large-scale inputs. It supports text, image, and video understanding, making it useful for workflows that combine code, documents, visual references, and tool-driven tasks. The model is built for repository-scale reasoning, software engineering, autonomous task execution, tool calling, and multi-step agent workflows. MiniMax M3 helps developers, AI teams, and enterprises build capable agents that can reason across large contexts and work with multimodal information.Starting Price: $0.30 per million input tokens -
5
GLM-5.3-Flash
Z.ai
GLM-5.3-Flash is Z.ai’s natively multimodal model in the GLM-5 series (previously previewed as Ox Alpha), designed to deliver strong coding, agentic, visual, and knowledge-work performance at relatively low inference cost. It uses 320 billion total parameters with 18 billion active parameters, along with a hybrid architecture that combines sparse and linear attention to reduce the cost of long-context processing. The model supports context lengths of up to one million tokens and was trained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash can reason across text, images, documents, interfaces, dashboards, and other visual information while using that feedback to refine its own outputs. Z.ai reports substantial gains over GLM-5.2 on coding and agentic benchmarks, including DeepSWE and AutomationBench, while approaching higher-cost frontier models on several evaluations.Starting Price: $0.15 per 1M tokens (input) -
6
DeepSeek-V4-Pro
DeepSeek
DeepSeek-V4-Pro is a large-scale Mixture-of-Experts (MoE) language model designed for advanced reasoning, coding, and long-context understanding. It features 1.6 trillion total parameters with 49 billion activated parameters, enabling high performance while maintaining efficiency. The model supports an exceptionally large context window of up to one million tokens, allowing it to process extensive documents and workflows. It uses a hybrid attention architecture to optimize long-context performance and reduce computational cost. DeepSeek-V4-Pro is trained on over 32 trillion tokens, improving its knowledge and reasoning capabilities. It also includes advanced optimization techniques for stability and faster convergence during training. The model supports multiple reasoning modes, allowing users to balance speed and accuracy based on their needs. Overall, it provides a powerful open-source solution for complex AI tasks and large-scale applications.Starting Price: $0.435 per 1M tokens (input) -
7
Mercury Edit 2
Inception
Mercury Edit 2 is part of Inception Labs’ Mercury family of AI models, designed to perform high-speed reasoning, coding, and editing tasks using a fundamentally different architecture from traditional large language models. It builds on Mercury 2, a diffusion-based reasoning model that generates and refines entire outputs in parallel rather than producing text token by token, enabling significantly faster performance and more responsive editing workflows. Instead of acting like a sequential “typewriter,” the system behaves more like an editor, starting with a rough draft and iteratively improving it across multiple tokens at once, which allows for real-time interaction and rapid iteration in tasks such as code editing, content generation, and agent-based workflows. This architecture delivers throughput of up to around 1,000 tokens per second, making it several times faster than conventional models while maintaining competitive reasoning quality across benchmarks.Starting Price: $0.25 per 1M input tokens -
8
Mercury 2.5
Inception
Mercury 2.5 is Inception’s most capable production model yet and a significant step up in quality over Mercury 2 while maintaining the same low-latency serving profile. It is the most capable diffusion LLM on the market and, according to Inception, the largest diffusion language model ever trained. Mercury 2.5 delivers a 40% increase in intelligence over Mercury 2, with performance comparable to cost-optimized frontier models such as GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5. It generates at 1,107 tokens per second on widely available NVIDIA GPUs and supports a 260K-token context window. Capabilities include tunable reasoning, parallel tool calls, and schema-aligned JSON. The model is designed for latency-sensitive workloads where many model calls may happen inside a single interaction. In search agents and RAG pipelines, it can support planning, query rewriting, reranking, fact structuring, source summarization, and answer checking while keeping calls fast. -
9
Mercury 2
Inception
Mercury 2 is the first reasoning model fast enough to pick up the phone, a reasoning diffusion language model built for real-time voice agents. Instead of making callers wait through seconds of dead air while an autoregressive model generates thinking tokens one by one, Mercury 2 uses a diffusion large language model architecture to generate tokens in parallel, decoding 1000+ tokens per second on standard NVIDIA GPUs. That speed is fast enough to run a full reasoning pass and start speaking within the latency budget of a natural conversation, reducing the cost of reasoning from seconds of silence to roughly 300 milliseconds. Mercury models work by corrupting clean text into noise, then training a standard Transformer to reverse the process and predict clean text across all positions simultaneously. Because each denoising pass touches many tokens, generation uses the GPU more efficiently than one-token-at-a-time decoding, making custom-silicon-like speed possible on NVIDIA H100s. -
10
Mercury Coder
Inception Labs
Mercury, the latest innovation from Inception Labs, is the first commercial-scale diffusion large language model (dLLM), offering a 10x speed increase and significantly lower costs compared to traditional autoregressive models. Built for high-performance reasoning, coding, and structured text generation, Mercury processes over 1000 tokens per second on NVIDIA H100 GPUs, making it one of the fastest LLMs available. Unlike conventional models that generate text one token at a time, Mercury refines responses using a coarse-to-fine diffusion approach, improving accuracy and reducing hallucinations. With Mercury Coder, a specialized coding model, developers can experience cutting-edge AI-driven code generation with superior speed and efficiency.Starting Price: Free -
11
Aion 1.0 Plan
Microsoft
Aion 1.0 Plan is Microsoft’s local agentic reasoning model for Windows, designed to bring fully agentic workflows onto the device without cloud dependency or per-token cost. It is a 14-billion-parameter reasoning and tool-calling model with a 32K context length, shipping in-box as part of Windows on capable devices. Unlike smaller on-device models focused on everyday text intelligence, Aion 1.0 Plan is built for local agentic reasoning, enabling applications to understand user intent, invoke tools, manage files, and orchestrate sub-agents directly on the device. It belongs to Microsoft’s new generation of on-device small language models purpose-built for local execution, representing the progression from efficient text intelligence at scale to more capable local planning and action. Aion 1.0 Plan is part of Windows’ broader push toward “unmetered intelligence,” where frontier models handle the hardest problems while local models support continuous, lower-cost agent workflows. -
12
DiffusionGemma
Google
DiffusionGemma is an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Released under an Apache 2.0 license, this 26B Mixture of Experts (MoE) model moves beyond the sequential token-by-token processing of typical autoregressive Large Language Models (LLMs). Instead, it generates entire blocks of text simultaneously, delivering up to 4x faster text generation on GPUs. Built on the intelligence-per-parameter of the Gemma 4 family and Gemini Diffusion research, DiffusionGemma integrates a novel diffusion head designed to maximize generation speed. It is designed for researchers and developers exploring speed-critical, interactive local workflows such as in-line editing, rapid iteration, and non-linear text structures. By shifting the decode bottleneck from memory bandwidth to compute, it can generate more than 1,000 tokens per second on a single NVIDIA H100 and more than 700 tokens per second on an NVIDIA GeForce RTX 5090.Starting Price: Free -
13
Sarvam 105B
Sarvam
Sarvam-105B is the flagship large language model in Sarvam’s open source model family, designed to deliver high-performance reasoning, multilingual understanding, and agent-based execution within a single scalable system. Built as a Mixture-of-Experts (MoE) model with approximately 105 billion total parameters, of which only a fraction are activated per token, it achieves strong computational efficiency while maintaining high capability across complex tasks. The model is optimized for advanced reasoning, coding, mathematics, and agentic workflows, making it suitable for tasks that require multi-step problem solving and structured outputs rather than simple conversational responses. Sarvam-105B supports long-context processing of up to around 128K tokens, enabling it to handle large documents, extended conversations, and deep analytical queries without losing coherence.Starting Price: Free -
14
GPT-Realtime-2.1
OpenAI
GPT-Realtime-2.1 is OpenAI’s reasoning model with tool use for low-latency voice agents and complex speech-to-speech workflows. It updates GPT-Realtime-2 with improved alphanumeric recognition, silence and noise handling, and interruption behavior, helping applications understand spoken code, manage imperfect audio, and respond more naturally when users pause or talk over the agent. Developers can configure reasoning effort to balance deeper thinking against latency and output usage, while strong instruction following helps the model stay aligned with a defined role, tone, and workflow. It accepts and produces both audio and text, can take images as input, and supports function calling so an agent can retrieve information or perform actions during a conversation. The model has a 128,000-token context window, supports up to 32,000 output tokens, and includes reasoning-token support for extended interactions.Starting Price: $0.40 per cached input -
15
Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
16
Phi-4-mini-flash-reasoning
Microsoft
Phi-4-mini-flash-reasoning is a 3.8 billion‑parameter open model in Microsoft’s Phi family, purpose‑built for edge, mobile, and other resource‑constrained environments where compute, memory, and latency are tightly limited. It introduces the SambaY decoder‑hybrid‑decoder architecture with Gated Memory Units (GMUs) interleaved alongside Mamba state‑space and sliding‑window attention layers, delivering up to 10× higher throughput and a 2–3× reduction in latency compared to its predecessor without sacrificing advanced math and logic reasoning performance. Supporting a 64 K‑token context length and fine‑tuned on high‑quality synthetic data, it excels at long‑context retrieval, reasoning tasks, and real‑time inference, all deployable on a single GPU. Phi-4-mini-flash-reasoning is available today via Azure AI Foundry, NVIDIA API Catalog, and Hugging Face, enabling developers to build fast, scalable, logic‑intensive applications. -
17
GPT-5 nano
OpenAI
GPT-5 nano is OpenAI’s fastest and most affordable version of the GPT-5 family, designed for high-speed text processing tasks like summarization and classification. It supports text and image inputs, generating high-quality text outputs with a large 400,000-token context window and up to 128,000 output tokens. GPT-5 nano offers very fast response times, making it ideal for applications requiring quick turnaround without sacrificing quality. Pricing is extremely competitive, with input tokens costing $0.05 per million and output tokens $0.40 per million, making it accessible for budget-conscious projects. The model supports advanced API features such as streaming, function calling, structured outputs, and fine-tuning. While it supports image input, it does not handle audio input or web search, focusing on core text tasks efficiently.Starting Price: $0.05 per 1M tokens -
18
Nemotron 3 Super
NVIDIA
Nemotron-3 Super is part of NVIDIA’s Nemotron 3 family of open models designed to enable advanced agentic AI systems that can reason, plan, and execute multi-step workflows across complex environments. The model introduces a hybrid Mamba-Transformer Mixture-of-Experts architecture that combines the efficiency of state-space Mamba layers with the contextual understanding of transformer attention, allowing it to process long sequences and complex reasoning tasks with high accuracy and throughput. This architecture activates only a subset of model parameters for each token, improving computational efficiency while maintaining strong reasoning capabilities and enabling scalable inference for large workloads. Nemotron-3 Super contains roughly 120 billion parameters with around 12 billion active during inference, accelerating multi-step reasoning and collaborative agent interactions across large contexts. -
19
Command A Reasoning
Cohere AI
Command A Reasoning is Cohere’s most advanced enterprise-ready language model, engineered for high-stakes reasoning tasks and seamless integration into AI agent workflows. The model delivers exceptional reasoning performance, efficiency, and controllability, scaling across multi-GPU setups with support for up to 256,000-token context windows, ideal for handling long documents and multi-step agentic tasks. Organizations can fine-tune output precision and latency through a token budget, allowing a single model to flexibly serve both high-accuracy and high-throughput use cases. It powers Cohere’s North platform with leading benchmark performance and excels in multilingual contexts across 23 languages. Designed with enterprise safety in mind, it balances helpfulness with robust safeguards against harmful outputs. A lightweight deployment option allows running the model securely on a single H100 or A100 GPU, simplifying private, scalable use. -
20
Nemotron 3.5 Lightning
NVIDIA
NVIDIA Nemotron 3.5 Lightning is an open 30B-parameter mixture-of-experts model with 3B active parameters, designed for high-volume, low-latency execution in long-running and always-on AI agents. Built for the execution layer of agentic systems, it handles frequent tasks such as tool calls, output validation, routine commands, and subagent delegation while larger reasoning models focus on planning and orchestration. Its MoE architecture activates only a fraction of parameters for each token, combining the capacity of a larger model with lower compute requirements. The model is trained for popular agent harnesses and supports speculative decoding through multi-token prediction, DFlash, and DSpark to improve inference speed across different serving scenarios. It is available with BF16 and NVFP4 checkpoints and can run from local systems such as DGX Spark and GeForce RTX hardware to data center environments. -
21
Celeris-1
Celeris-1
Celeris-1 is a low-latency, general-purpose language model platform and a diffusion model designed to deliver frontier-level intelligence at dramatically higher speed. Instead of generating one token at a time like traditional autoregressive models, Celeris uses a diffusion-based inference architecture that enables parallel generation and response times measured in milliseconds. On its published MMLU-Pro benchmark, Celeris-1 reaches 75.9 accuracy with a 158 ms median response time and 1,664 output tokens per second, placing it within a few points of frontier models while running more than 10x faster. The model is exposed through an OpenAI-compatible API, so developers can point existing SDKs and clients at Celeris with minimal code changes. Streaming is enabled for interactive applications, with responses as low as 24 ms and no buffering or batch delay.Starting Price: $0.20 per 1M tokens -
22
Step 3.5 Flash
StepFun
Step 3.5 Flash is an advanced open source foundation language model engineered for frontier reasoning and agentic capabilities with exceptional efficiency, built on a sparse Mixture of Experts (MoE) architecture that selectively activates only about 11 billion of its ~196 billion parameters per token to deliver high-density intelligence and real-time responsiveness. Its 3-way Multi-Token Prediction (MTP-3) enables generation throughput in the hundreds of tokens per second for complex multi-step reasoning chains and task execution, and it supports efficient long contexts with a hybrid sliding window attention approach that reduces computational overhead across large datasets or codebases. It demonstrates robust performance on benchmarks for reasoning, coding, and agentic tasks, rivaling or exceeding many larger proprietary models, and includes a scalable reinforcement learning framework for consistent self-improvement.Starting Price: Free -
23
Seed2.0 Lite
ByteDance
Seed2.0 Lite is part of ByteDance’s Seed2.0 family of general-purpose multimodal AI agent models designed to handle complex, real-world tasks with a balanced focus on performance and efficiency. It offers enhanced multimodal understanding and instruction-following capabilities compared with earlier Seed models, enabling it to process and reason about text, visual elements, and structured information reliably for production-grade applications. As a mid-sized model in the series, Lite is optimized to deliver good quality outputs with responsive performance at lower cost and faster inference than the Pro variant while surpassing the previous generation’s capabilities, making it suitable for workflows that require stable reasoning, long-context understanding, and multimodal task execution without needing the highest possible raw performance. -
24
Inkling-Small
Thinking Machines Lab
Inkling-Small is an efficient model that offers performance comparable to Inkling at a quarter of its size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters, trained on NVIDIA GB300 NVL72 systems. It supports native reasoning across text, images, and audio, variable thinking effort, and context windows of up to one million tokens. Users adjust reasoning effort from minimal to extra high to balance performance and compute. Improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. It performs well in coding and tool-use harnesses, exceeds 80% on SWE-bench Verified, and combines strong reasoning with efficient output. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens.Starting Price: $0.30 per million input tokens -
25
Olmo 3
Ai2
Olmo 3 is a fully open model family spanning 7 billion and 32 billion parameter variants that delivers not only high-performing base, reasoning, instruction, and reinforcement-learning models, but also exposure of the entire model flow, including raw training data, intermediate checkpoints, training code, long-context support (65,536 token window), and provenance tooling. Starting with the Dolma 3 dataset (≈9 trillion tokens) and its disciplined mix of web text, scientific PDFs, code, and long-form documents, the pre-training, mid-training, and long-context phases shape the base models, which are then post-trained via supervised fine-tuning, direct preference optimisation, and RL with verifiable rewards to yield the Think and Instruct variants. The 32 B Think model is described as the strongest fully open reasoning model to date, competitively close to closed-weight peers in math, code, and complex reasoning.Starting Price: Free -
26
DeepSeek-V4-Flash
DeepSeek
DeepSeek-V4-Flash is a high-efficiency Mixture-of-Experts (MoE) language model designed for fast, scalable reasoning and text generation. It features 284 billion total parameters with 13 billion activated parameters, delivering strong performance while optimizing computational cost. The model supports an extensive context window of up to one million tokens, enabling it to process large documents and complex workflows with ease. Its hybrid attention architecture enhances long-context efficiency by reducing memory and compute requirements. Trained on over 32 trillion tokens, DeepSeek-V4-Flash demonstrates solid capabilities across knowledge, reasoning, and coding tasks. It is designed for scenarios where speed and efficiency are critical, offering a balance between performance and resource usage. The model also supports multiple reasoning modes, allowing users to adjust between faster outputs and deeper analysis.Starting Price: $0.14 per 1M tokens (input) -
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Gemini 2.5 Flash-Lite
Google
Gemini 2.5 is Google DeepMind’s latest generation AI model family, designed to deliver advanced reasoning and native multimodality with a long context window. It improves performance and accuracy by reasoning through its thoughts before responding. The model offers different versions tailored for complex coding tasks, fast everyday performance, and cost-efficient high-volume workloads. Gemini 2.5 supports multiple data types including text, images, video, audio, and PDFs, enabling versatile AI applications. It features adaptive thinking budgets and fine-grained control for developers to balance cost and output quality. Available via Google AI Studio and Gemini API, Gemini 2.5 powers next-generation AI experiences. -
28
Big Pickle
OpenCode
Big Pickle is an AI model available through OpenCode Zen, a curated model provider focused on coding-agent workflows. The model is designed for text-based input, reasoning tasks, function calling, and developer workflows that require long-context understanding. Big Pickle supports a large context window, making it useful for working across bigger codebases, project files, technical prompts, and multi-step coding tasks. It can be accessed through OpenCode Zen using an OpenAI-compatible API format, allowing developers to integrate it into agentic coding tools and automation workflows. The model is positioned as a free or low-cost option within OpenCode’s coding-agent ecosystem. Big Pickle helps developers experiment with AI-assisted coding, reasoning, tool use, and long-context automation without relying only on premium frontier models.Starting Price: Free -
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Nemotron 3 Nano Omni
NVIDIA
NVIDIA Nemotron 3 Nano Omni is an open, omni-modal foundation model designed to unify perception and reasoning across text, images, audio, video, and documents within a single efficient architecture. It eliminates the need for separate models for each modality, reducing inference latency, orchestration complexity, and cost while maintaining consistent cross-modal context. It is purpose-built for agentic AI systems, acting as a perception and context sub-agent that gives larger AI agents the ability to “see, hear, and read” in real time across screens, recordings, and structured or unstructured data. It supports advanced multimodal reasoning tasks such as document understanding, speech recognition, long audio-video analysis, and computer-use workflows, enabling agents to interpret dynamic interfaces and complex environments. Built with a hybrid architecture optimized for long context and throughput, it can process large inputs like multi-page documents.Starting Price: Free -
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NVIDIA Llama Nemotron
NVIDIA
NVIDIA Llama Nemotron is a family of advanced language models optimized for reasoning and a diverse set of agentic AI tasks. These models excel in graduate-level scientific reasoning, advanced mathematics, coding, instruction following, and tool calls. Designed for deployment across various platforms, from data centers to PCs, they offer the flexibility to toggle reasoning capabilities on or off, reducing inference costs when deep reasoning isn't required. The Llama Nemotron family includes models tailored for different deployment needs. Built upon Llama models and enhanced by NVIDIA through post-training, these models demonstrate improved accuracy, up to 20% over base models, and optimized inference speeds, achieving up to five times the performance of other leading open reasoning models. This efficiency enables handling more complex reasoning tasks, enhances decision-making capabilities, and reduces operational costs for enterprises. -
31
Qwen3-Max
Alibaba
Qwen3-Max is Alibaba’s latest trillion-parameter large language model, designed to push performance in agentic tasks, coding, reasoning, and long-context processing. It is built atop the Qwen3 family and benefits from the architectural, training, and inference advances introduced there; mixing thinker and non-thinker modes, a “thinking budget” mechanism, and support for dynamic mode switching based on complexity. The model reportedly processes extremely long inputs (hundreds of thousands of tokens), supports tool invocation, and exhibits strong performance on benchmarks in coding, multi-step reasoning, and agent benchmarks (e.g., Tau2-Bench). While its initial variant emphasizes instruction following (non-thinking mode), Alibaba plans to bring reasoning capabilities online to enable autonomous agent behavior. Qwen3-Max inherits multilingual support and extensive pretraining on trillions of tokens, and it is delivered via API interfaces compatible with OpenAI-style functions.Starting Price: Free -
32
Mistral NeMo
Mistral AI
Mistral NeMo, our new best small model. A state-of-the-art 12B model with 128k context length, and released under the Apache 2.0 license. Mistral NeMo is a 12B model built in collaboration with NVIDIA. Mistral NeMo offers a large context window of up to 128k tokens. Its reasoning, world knowledge, and coding accuracy are state-of-the-art in its size category. As it relies on standard architecture, Mistral NeMo is easy to use and a drop-in replacement in any system using Mistral 7B. We have released pre-trained base and instruction-tuned checkpoints under the Apache 2.0 license to promote adoption for researchers and enterprises. Mistral NeMo was trained with quantization awareness, enabling FP8 inference without any performance loss. The model is designed for global, multilingual applications. It is trained on function calling and has a large context window. Compared to Mistral 7B, it is much better at following precise instructions, reasoning, and handling multi-turn conversations.Starting Price: Free -
33
Nemotron 3 Nano
NVIDIA
Nemotron 3 Nano is the smallest model in the NVIDIA Nemotron 3 family, built for agentic AI applications with strong reasoning, conversational ability, and cost-efficient inference. It is a hybrid Mamba-Transformer Mixture-of-Experts model with 3.2 billion active parameters, 3.6 billion including embeddings, and 31.6 billion total parameters. NVIDIA describes it as more accurate than the previous Nemotron 2 Nano while activating less than half of the parameters per forward pass, improving efficiency without sacrificing performance. The model is positioned as more accurate than GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 on popular benchmarks across different categories. On an 8K input and 16K output setting using a single H200, it delivers inference throughput 3.3 times higher than Qwen3-30B-A3B and 2.2 times higher than GPT-OSS-20B. Nemotron 3 Nano supports context lengths up to 1 million tokens and is reported to outperform GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. -
34
MiMo-V2-Flash
Xiaomi Technology
MiMo-V2-Flash is an open weight large language model developed by Xiaomi based on a Mixture-of-Experts (MoE) architecture that blends high performance with inference efficiency. It has 309 billion total parameters but activates only 15 billion active parameters per inference, letting it balance reasoning quality and computational efficiency while supporting extremely long context handling, for tasks like long-document understanding, code generation, and multi-step agent workflows. It incorporates a hybrid attention mechanism that interleaves sliding-window and global attention layers to reduce memory usage and maintain long-range comprehension, and it uses a Multi-Token Prediction (MTP) design that accelerates inference by processing batches of tokens in parallel. MiMo-V2-Flash delivers very fast generation speeds (up to ~150 tokens/second) and is optimized for agentic applications requiring sustained reasoning and multi-turn interactions.Starting Price: Free -
35
GLM-4.7-Flash
Z.ai
GLM-4.7 Flash is a lightweight variant of GLM-4.7, Z.ai’s flagship large language model designed for advanced coding, reasoning, and multi-step task execution with strong agentic performance and a very large context window. It is an MoE-based model optimized for efficient inference that balances performance and resource use, enabling deployment on local machines with moderate memory requirements while maintaining deep reasoning, coding, and agentic task abilities. GLM-4.7 itself advances over earlier generations with enhanced programming capabilities, stable multi-step reasoning, context preservation across turns, and improved tool-calling workflows, and supports very long context lengths (up to ~200 K tokens) for complex tasks that span large inputs or outputs. The Flash variant retains many of these strengths in a smaller footprint, offering competitive benchmark performance in coding and reasoning tasks for models in its size class.Starting Price: Free -
36
GPT-Realtime-1.5
OpenAI
GPT-Realtime-1.5 is a flagship voice AI model from OpenAI designed for real-time audio interactions and conversational applications. It supports both audio input and output, making it ideal for voice agents and customer support systems. The model delivers fast performance with high responsiveness, enabling natural, real-time conversations. It can process multiple input types, including text, audio, and images, while generating both text and audio responses. With a 32,000-token context window, it can handle extended conversations and maintain context effectively. The model is optimized for high-performance use cases where speed and accuracy are critical. It also supports function calling, allowing integration with external tools and workflows. Overall, it provides a powerful solution for building interactive, real-time voice applications.Starting Price: $4.00 per 1M tokens (input) -
37
Trinity-Large-Thinking
Arcee AI
Trinity Large Thinking is a frontier open source reasoning model developed by Arcee AI, designed specifically for complex, multi-step problem solving and autonomous agent workflows that require long-horizon planning and tool use. Built on a sparse Mixture-of-Experts architecture with roughly 400 billion total parameters but only about 13 billion active per token, the model achieves high efficiency while maintaining strong reasoning performance across tasks such as mathematical problem solving, code generation, and multi-step analysis. It introduces extended chain-of-thought reasoning capabilities, allowing the model to generate intermediate “thinking traces” before producing final answers, which improves accuracy and reliability in complex scenarios. Trinity Large Thinking supports a very large context window of up to 262K tokens, enabling it to process long documents, maintain state across extended interactions, and operate effectively in continuous agent loops.Starting Price: Free -
38
Gemini 3 Flash
Google
Gemini 3 Flash is Google’s latest AI model built to deliver frontier intelligence with exceptional speed and efficiency. It combines Pro-level reasoning with Flash-level latency, making advanced AI more accessible and affordable. The model excels in complex reasoning, multimodal understanding, and agentic workflows while using fewer tokens for everyday tasks. Gemini 3 Flash is designed to scale across consumer apps, developer tools, and enterprise platforms. It supports rapid coding, data analysis, video understanding, and interactive application development. By balancing performance, cost, and speed, Gemini 3 Flash redefines what fast AI can achieve. -
39
Nemotron 3
NVIDIA
NVIDIA Nemotron 3 is a family of open large language models developed by NVIDIA to power advanced reasoning, conversational AI, and autonomous AI agents. The Nemotron 3 series includes three models designed for different scales of AI workloads while maintaining high efficiency and accuracy. These models focus on “agentic AI” capabilities, meaning they can perform multi-step reasoning, coordinate with tools, and operate as components within multi-agent systems used in automation, research, and enterprise applications. The architecture uses a hybrid mixture-of-experts (MoE) design combined with transformer-based techniques, allowing the model to activate only a subset of parameters for each task, which improves performance while reducing computational cost. Nemotron 3 models are built to deliver strong reasoning, conversational, and planning abilities while maintaining high throughput for large-scale deployment. -
40
Claude Fable 5.5
Anthropic
Claude Fable 5.5 is not currently an announced or released Anthropic model as of September 30, 2026. Anthropic's current model documentation lists Claude Fable 5.1 as the latest Fable model alongside Claude Opus 5.5 and Claude Sonnet 5.5. Fable 5.1 remains Anthropic's premium model for demanding reasoning and long-horizon agentic workloads, with a 1-million-token context window and maximum output of 128,000 tokens. It uses adaptive thinking that is always enabled and defaults to a high reasoning effort level. Fable 5.1 is priced at $10 per million input tokens and $50 per million output tokens, with cache reads priced at $0.25 per million tokens. Anthropic has released Opus 5.5 and Sonnet 5.5, but there is currently no official model documentation, pricing, release date, benchmark data, or API identifier for Claude Fable 5.5. -
41
GPT-4o mini
OpenAI
A small model with superior textual intelligence and multimodal reasoning. GPT-4o mini enables a broad range of tasks with its low cost and latency, such as applications that chain or parallelize multiple model calls (e.g., calling multiple APIs), pass a large volume of context to the model (e.g., full code base or conversation history), or interact with customers through fast, real-time text responses (e.g., customer support chatbots). Today, GPT-4o mini supports text and vision in the API, with support for text, image, video and audio inputs and outputs coming in the future. The model has a context window of 128K tokens, supports up to 16K output tokens per request, and has knowledge up to October 2023. Thanks to the improved tokenizer shared with GPT-4o, handling non-English text is now even more cost effective. -
42
Kimi K2
Moonshot AI
Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.Starting Price: Free -
43
Devstral 2
Mistral AI
Devstral 2 is a next-generation, open source agentic AI model tailored for software engineering: it doesn’t just suggest code snippets, it understands and acts across entire codebases, enabling multi-file edits, bug fixes, refactoring, dependency resolution, and context-aware code generation. The Devstral 2 family includes a large 123-billion-parameter model as well as a smaller 24-billion-parameter variant (“Devstral Small 2”), giving teams flexibility; the larger model excels in heavy-duty coding tasks requiring deep context, while the smaller one can run on more modest hardware. With a vast context window of up to 256 K tokens, Devstral 2 can reason across extensive repositories, track project history, and maintain a consistent understanding of lengthy files, an advantage for complex, real-world projects. The CLI tracks project metadata, Git statuses, and directory structure to give the model context, making “vibe-coding” more powerful.Starting Price: Free -
44
Mercurial
Mercurial
Mercurial is a free, distributed source control management tool. It efficiently handles projects of any size and offers an easy and intuitive interface. Mercurial efficiently handles projects of any size and kind. Every clone contains the whole project history, so most actions are local, fast and convenient. Mercurial supports a multitude of workflows and you can easily enhance its functionality with extensions. Mercurial strives to deliver on each of its promises. Most tasks simply work on the first try and without requiring arcane knowledge. The functionality of Mercurial can be increased with extensions, either by activating the official ones which are shipped with Mercurial or downloading some from the wiki or by writing your own. Extensions are written in Python and can change the workings of the basic commands, add new commands and access all the core functions of Mercurial. -
45
GPT-5.4
OpenAI
GPT-5.4 is an advanced artificial intelligence model developed by OpenAI to support complex professional and technical work. The model combines improvements in reasoning, coding, and agent-based workflows into a single system designed for real-world productivity tasks. GPT-5.4 can generate, analyze, and edit documents, spreadsheets, presentations, and other work outputs with greater accuracy and efficiency. It also features improved tool integration, enabling the model to interact with software environments and external tools to complete multi-step workflows. With enhanced context capabilities supporting up to one million tokens, GPT-5.4 can process and reason over very large amounts of information. The model also improves factual accuracy and reduces errors compared to earlier versions. By combining strong reasoning, coding ability, and tool use, GPT-5.4 helps users complete complex tasks faster and with fewer iterations. -
46
Meta Model API
Meta
Meta Model API is a new developer API for building with Muse Spark 1.1, Meta’s multimodal reasoning model built for agentic tasks, coding, tool use, computer use, and multimodal understanding. Now in public preview, it gives developers a way to access Muse Spark 1.1 through an OpenAI-compatible package, making it easier to point existing clients at the API, keep the same code structure, and set the model to muse-spark-1.1. Muse Spark 1.1 is designed for personal agentic tasks that require planning and orchestration across external apps and services, with the ability to generalize to new native tools, MCP servers, and custom skills. As a main agent, it can gather context, make a plan, and delegate execution across parallel subagents; as a subagent, it follows its role, understands available tools, and knows when to escalate back. The model can actively manage a 1 million-token context window, remember actions, retrieve information from much earlier work, and compact context.Starting Price: $1.25 per 1M tokens -
47
GLM-5-Turbo
Z.ai
GLM-5-Turbo is a high-speed variant of Z.ai’s GLM-5 model, designed to deliver efficient and stable performance in agent-driven environments while maintaining strong reasoning and coding capabilities. It is optimized for high-throughput workloads, particularly long-chain agent tasks where multiple steps, tools, and decisions must be executed in sequence with reliability and low latency. It supports advanced agentic workflows, enabling systems to perform multi-step planning, tool calling, and task execution with improved responsiveness compared to larger flagship models. GLM-5-Turbo inherits core capabilities from the GLM-5 family, including strong reasoning, coding performance, and support for long-context processing, while focusing on optimization of core requirements such as speed, efficiency, and stability in production environments. It is designed to integrate with agent frameworks like OpenClaw, where it can coordinate actions, process inputs, and execute tasks.Starting Price: Free -
48
GPT-5 mini
OpenAI
GPT-5 mini is a streamlined, faster, and more affordable variant of OpenAI’s GPT-5, optimized for well-defined tasks and precise prompts. It supports text and image inputs and delivers high-quality text outputs with a 400,000-token context window and up to 128,000 output tokens. This model excels at rapid response times, making it suitable for applications requiring fast, accurate language understanding without the full overhead of GPT-5. Pricing is cost-effective, with input tokens at $0.25 per million and output tokens at $2 per million, providing savings over the flagship model. GPT-5 mini supports advanced features like streaming, function calling, structured outputs, and fine-tuning, but does not support audio input or image generation. It integrates well with various API endpoints including chat completions, responses, and embeddings, making it versatile for many AI-powered tasks.Starting Price: $0.25 per 1M tokens -
49
Mercury Recruit
Mercury360
Mercury Recruit is a modern, AI-powered Applicant Tracking System (ATS) designed to help organisations hire smarter, faster, and more efficiently. Whether you're a growing SMB, a large enterprise, or a recruitment agency managing multiple clients, Mercury Recruit centralises your entire hiring workflow into one platform. Key capabilities include AI-powered candidate ranking that automatically scores applicants against your exact job criteria, dramatically reducing manual screening time by up to 80%. A dynamic candidate portal and personalised talent pool make it easy to manage applicants from multiple sources in one place. Customisable workflows and real-time team collaboration ensure consistent, structured evaluations across every hire. Beyond speed, Mercury Recruit helps you improve quality of hire, reduce total recruitment costs, lower reliance on job boards and agencies, and build stronger teams with best-fit talent.Starting Price: $150 per month -
50
MiniMax M1
MiniMax
MiniMax‑M1 is a large‑scale hybrid‑attention reasoning model released by MiniMax AI under the Apache 2.0 license. It supports an unprecedented 1 million‑token context window and up to 80,000-token outputs, enabling extended reasoning across long documents. Trained using large‑scale reinforcement learning with a novel CISPO algorithm, MiniMax‑M1 completed full training on 512 H800 GPUs in about three weeks. It achieves state‑of‑the‑art performance on benchmarks in mathematics, coding, software engineering, tool usage, and long‑context understanding, matching or outperforming leading models. Two model variants are available (40K and 80K thinking budgets), with weights and deployment scripts provided via GitHub and Hugging Face.
