Alternatives to Phi-4-mini-reasoning
Compare Phi-4-mini-reasoning alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Phi-4-mini-reasoning in 2026. Compare features, ratings, user reviews, pricing, and more from Phi-4-mini-reasoning competitors and alternatives in order to make an informed decision for your business.
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1
Phi-4-reasoning
Microsoft
Phi-4-reasoning is a 14-billion parameter transformer-based language model optimized for complex reasoning tasks, including math, coding, algorithmic problem solving, and planning. Trained via supervised fine-tuning of Phi-4 on carefully curated "teachable" prompts and reasoning demonstrations generated using o3-mini, it generates detailed reasoning chains that effectively leverage inference-time compute. Phi-4-reasoning incorporates outcome-based reinforcement learning to produce longer reasoning traces. It outperforms significantly larger open-weight models such as DeepSeek-R1-Distill-Llama-70B and approaches the performance levels of the full DeepSeek-R1 model across a wide range of reasoning tasks. Phi-4-reasoning is designed for environments with constrained computing or latency. Fine-tuned with synthetic data generated by DeepSeek-R1, it provides high-quality, step-by-step problem solving. -
2
Phi-4-reasoning-plus
Microsoft
Phi-4-reasoning-plus is a 14-billion parameter open-weight reasoning model that builds upon Phi-4-reasoning capabilities. It is further trained with reinforcement learning to utilize more inference-time compute, using 1.5x more tokens than Phi-4-reasoning, to deliver higher accuracy. Despite its significantly smaller size, Phi-4-reasoning-plus achieves better performance than OpenAI o1-mini and DeepSeek-R1 at most benchmarks, including mathematical reasoning and Ph.D. level science questions. It surpasses the full DeepSeek-R1 model (with 671 billion parameters) on the AIME 2025 test, the 2025 qualifier for the USA Math Olympiad. Phi-4-reasoning-plus is available on Azure AI Foundry and HuggingFace. -
3
DeepSeek R1
DeepSeek
DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.Starting Price: Free -
4
EXAONE Deep
LG
EXAONE Deep is a series of reasoning-enhanced language models developed by LG AI Research, featuring parameter sizes of 2.4 billion, 7.8 billion, and 32 billion. These models demonstrate superior capabilities in various reasoning tasks, including math and coding benchmarks. Notably, EXAONE Deep 2.4B outperforms other models of comparable size, EXAONE Deep 7.8B surpasses both open-weight models of similar scale and the proprietary reasoning model OpenAI o1-mini, and EXAONE Deep 32B shows competitive performance against leading open-weight models. The repository provides comprehensive documentation covering performance evaluations, quickstart guides for using EXAONE Deep models with Transformers, explanations of quantized EXAONE Deep weights in AWQ and GGUF formats, and instructions for running EXAONE Deep models locally using frameworks like llama.cpp and Ollama.Starting Price: Free -
5
DeepCoder
Agentica Project
DeepCoder is a fully open source code-reasoning and generation model released by Agentica Project in collaboration with Together AI. It is fine-tuned from DeepSeek-R1-Distilled-Qwen-14B using distributed reinforcement learning, achieving a 60.6% accuracy on LiveCodeBench (representing an 8% improvement over the base), a performance level that matches that of proprietary models such as o3-mini (2025-01-031 Low) and o1 while using only 14 billion parameters. It was trained over 2.5 weeks on 32 H100 GPUs with a curated dataset of roughly 24,000 coding problems drawn from verified sources (including TACO-Verified, PrimeIntellect SYNTHETIC-1, and LiveCodeBench submissions), each problem requiring a verifiable solution and at least five unit tests to ensure reliability for RL training. To handle long-range context, DeepCoder employs techniques such as iterative context lengthening and overlong filtering.Starting Price: Free -
6
Open R1
Open R1
Open R1 is a community-driven, open-source initiative aimed at replicating the advanced AI capabilities of DeepSeek-R1 through transparent methodologies. You can try Open R1 AI model or DeepSeek R1 free online chat on Open R1. The project offers a comprehensive implementation of DeepSeek-R1's reasoning-optimized training pipeline, including tools for GRPO training, SFT fine-tuning, and synthetic data generation, all under the MIT license. While the original training data remains proprietary, Open R1 provides the complete toolchain for users to develop and fine-tune their own models.Starting Price: Free -
7
DeepScaleR
Agentica Project
DeepScaleR is a 1.5-billion-parameter language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning and a novel iterative context-lengthening strategy that gradually increases its context window from 8K to 24K tokens during training. It was trained on ~40,000 carefully curated mathematical problems drawn from competition-level datasets like AIME (1984–2023), AMC (pre-2023), Omni-MATH, and STILL. DeepScaleR achieves 43.1% accuracy on AIME 2024, a roughly 14.3 percentage point boost over the base model, and surpasses the performance of the proprietary O1-Preview model despite its much smaller size. It also posts strong results on a suite of math benchmarks (e.g., MATH-500, AMC 2023, Minerva Math, OlympiadBench), demonstrating that small, efficient models tuned with RL can match or exceed larger baselines on reasoning tasks.Starting Price: Free -
8
OpenAI o3-mini
OpenAI
OpenAI o3-mini is a lightweight version of the advanced o3 AI model, offering powerful reasoning capabilities in a more efficient and accessible package. Designed to break down complex instructions into smaller, manageable steps, o3-mini excels in coding tasks, competitive programming, and problem-solving in mathematics and science. This compact model provides the same high-level precision and logic as its larger counterpart but with reduced computational requirements, making it ideal for use in resource-constrained environments. With built-in deliberative alignment, o3-mini ensures safe, ethical, and context-aware decision-making, making it a versatile tool for developers, researchers, and businesses seeking a balance between performance and efficiency. -
9
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. -
10
OpenAI o3-mini-high
OpenAI
The o3-mini-high model from OpenAI advances AI reasoning by refining deep problem-solving in coding, mathematics, and complex tasks. It features adaptive thinking time with adjustable reasoning modes (low, medium, high) to optimize performance based on task complexity. Outperforming the o1 series by 200 Elo points on Codeforces, it delivers high efficiency at a lower cost while maintaining speed and accuracy. As part of the o3 family, it pushes AI problem-solving boundaries while remaining accessible, offering a free tier and expanded limits for Plus subscribers. -
11
DeepSeekMath
DeepSeek
DeepSeekMath is a specialized 7B parameter language model developed by DeepSeek-AI, designed to push the boundaries of mathematical reasoning in open-source language models. It starts from the DeepSeek-Coder-v1.5 7B model and undergoes further pre-training with 120B math-related tokens sourced from Common Crawl, alongside natural language and code data. DeepSeekMath has demonstrated remarkable performance, achieving a 51.7% score on the competition-level MATH benchmark without external tools or voting techniques, closely competing with the likes of Gemini-Ultra and GPT-4. The model's capabilities are enhanced by a meticulous data selection pipeline and the introduction of Group Relative Policy Optimization (GRPO), which optimizes both mathematical reasoning and memory usage. DeepSeekMath is available in base, instruct, and RL versions, supporting both research and commercial use, and is aimed at those looking to explore or apply advanced mathematical problem-solving in AI contexts.Starting Price: Free -
12
OpenAI o1-mini
OpenAI
OpenAI o1-mini is a new, cost-effective AI model designed for enhanced reasoning, particularly excelling in STEM fields like mathematics and coding. It's part of the o1 series, which focuses on solving complex problems by spending more time "thinking" through solutions. Despite being smaller and 80% cheaper than its sibling, the o1-preview, o1-mini performs competitively in coding tasks and mathematical reasoning, making it an accessible option for developers and enterprises looking for efficient AI solutions. -
13
OpenAI o1
OpenAI
OpenAI o1 represents a new series of AI models designed by OpenAI, focusing on enhanced reasoning capabilities. These models, including o1-preview and o1-mini, are trained using a novel reinforcement learning approach to spend more time "thinking" through problems before providing answers. This approach allows o1 to excel in complex problem-solving tasks in areas like coding, mathematics, and science, outperforming previous models like GPT-4o in certain benchmarks. The o1 series aims to tackle challenges that require deeper thought processes, marking a significant step towards AI systems that can reason more like humans, although it's still in the preview stage with ongoing improvements and evaluations. -
14
DeepSeek R2
DeepSeek
DeepSeek R2 is the anticipated successor to DeepSeek R1, a groundbreaking AI reasoning model launched in January 2025 by the Chinese AI startup DeepSeek. Building on R1’s success, which disrupted the AI industry with its cost-effective performance rivaling top-tier models like OpenAI’s o1, R2 promises a quantum leap in capabilities. It is expected to deliver exceptional speed and human-like reasoning, excelling in complex tasks such as advanced coding and high-level mathematical problem-solving. Leveraging DeepSeek’s innovative Mixture-of-Experts architecture and efficient training methods, R2 aims to outperform its predecessor while maintaining a low computational footprint, potentially expanding its reasoning abilities to languages beyond English.Starting Price: Free -
15
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. -
16
DeepSeek-V3.2-Speciale
DeepSeek
DeepSeek-V3.2-Speciale is a high-compute variant of the DeepSeek-V3.2 model, created specifically for deep reasoning and advanced problem-solving tasks. It builds on DeepSeek Sparse Attention (DSA), a custom long-context attention mechanism that reduces computational overhead while preserving high performance. Through a large-scale reinforcement learning framework and extensive post-training compute, the Speciale variant surpasses GPT-5 on reasoning benchmarks and matches the capabilities of Gemini-3.0-Pro. The model achieved gold-medal performance in the International Mathematical Olympiad (IMO) 2025 and International Olympiad in Informatics (IOI) 2025. DeepSeek-V3.2-Speciale does not support tool-calling, making it purely optimized for uninterrupted reasoning and analytical accuracy. Released under the MIT license, it provides researchers and developers an open, state-of-the-art model focused entirely on high-precision reasoning.Starting Price: Free -
17
Hunyuan T1
Tencent
Hunyuan T1 is Tencent's deep-thinking AI model, now fully open to all users through the Tencent Yuanbao platform. This model excels in understanding multiple dimensions and potential logical relationships, making it suitable for handling complex tasks. Users can experience various AI models on the platform, including DeepSeek-R1 and Tencent Hunyuan Turbo. The official version of the Tencent Hunyuan T1 model will also be launched soon, providing external API access and other services. Built upon Tencent's Hunyuan large language model, Yuanbao excels in Chinese language understanding, logical reasoning, and task execution. It offers AI-based search, summaries, and writing capabilities, enabling users to analyze documents and engage in prompt-based interactions. -
18
OpenAI o4-mini
OpenAI
The o4-mini model is a compact and efficient version of the o3 model, released following the launch of GPT-4.1. It offers enhanced reasoning capabilities, with improved performance in tasks that require complex reasoning and problem-solving. The o4-mini is designed to meet the growing demand for advanced AI solutions, serving as a more efficient alternative while maintaining the capabilities of its predecessor. This model is part of OpenAI's strategy to refine and advance their AI technologies ahead of the anticipated GPT-5 launch. -
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OpenAI o4-mini-high
OpenAI
OpenAI o4-mini-high is an enhanced version of the o4-mini, optimized for higher reasoning capacity and performance. It maintains the same compact size but significantly boosts its ability to handle more complex tasks with improved efficiency. Whether you're dealing with large datasets, advanced mathematical computations, or intricate coding problems, o4-mini-high provides faster, more accurate responses, making it perfect for high-demand applications. -
20
DeepSeek-V3.2
DeepSeek
DeepSeek-V3.2 is a next-generation open large language model designed for efficient reasoning, complex problem solving, and advanced agentic behavior. It introduces DeepSeek Sparse Attention (DSA), a long-context attention mechanism that dramatically reduces computation while preserving performance. The model is trained with a scalable reinforcement learning framework, allowing it to achieve results competitive with GPT-5 and even surpass it in its Speciale variant. DeepSeek-V3.2 also includes a large-scale agent task synthesis pipeline that generates structured reasoning and tool-use demonstrations for post-training. The model features an updated chat template with new tool-calling logic and the optional developer role for agent workflows. With gold-medal performance in the IMO and IOI 2025 competitions, DeepSeek-V3.2 demonstrates elite reasoning capabilities for both research and applied AI scenarios.Starting Price: Free -
21
DeepSeek-Coder-V2
DeepSeek
DeepSeek-Coder-V2 is an open source code language model designed to excel in programming and mathematical reasoning tasks. It features a Mixture-of-Experts (MoE) architecture with 236 billion total parameters and 21 billion activated parameters per token, enabling efficient processing and high performance. The model was trained on an extensive dataset of 6 trillion tokens, enhancing its capabilities in code generation and mathematical problem-solving. DeepSeek-Coder-V2 supports over 300 programming languages and has demonstrated superior performance on benchmarks such surpassing other models. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, optimized for instruction-based tasks; DeepSeek-Coder-V2-Base, suitable for general text generation; and lightweight versions like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, designed for environments with limited computational resources. -
22
ERNIE X1 Turbo
Baidu
ERNIE X1 Turbo, developed by Baidu, is an advanced deep reasoning AI model introduced at the Baidu Create 2025 conference. Designed to handle complex multi-step tasks such as problem-solving, literary creation, and code generation, this model outperforms competitors like DeepSeek R1 in terms of reasoning abilities. With a focus on multimodal capabilities, ERNIE X1 Turbo supports text, audio, and image processing, making it an incredibly versatile AI solution. Despite its cutting-edge technology, it is priced at just a fraction of the cost of other top-tier models, offering a high-value solution for businesses and developers.Starting Price: $0.14 per 1M tokens -
23
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. -
24
Reka Flash 3
Reka
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization. -
25
Sky-T1
NovaSky
Sky-T1-32B-Preview is an open source reasoning model developed by the NovaSky team at UC Berkeley's Sky Computing Lab. It matches the performance of proprietary models like o1-preview on reasoning and coding benchmarks, yet was trained for under $450, showcasing the feasibility of cost-effective, high-level reasoning capabilities. The model was fine-tuned from Qwen2.5-32B-Instruct using a curated dataset of 17,000 examples across diverse domains, including math and coding. The training was completed in 19 hours on eight H100 GPUs with DeepSpeed Zero-3 offloading. All aspects of the project, including data, code, and model weights, are fully open-source, empowering the academic and open-source communities to replicate and enhance the model's performance.Starting Price: Free -
26
MiniMax-M2.1
MiniMax
MiniMax-M2.1 is an open-source, agentic large language model designed for advanced coding, tool use, and long-horizon planning. It was released to the community to make high-performance AI agents more transparent, controllable, and accessible. The model is optimized for robustness in software engineering, instruction following, and complex multi-step workflows. MiniMax-M2.1 supports multilingual development and performs strongly across real-world coding scenarios. It is suitable for building autonomous applications that require reasoning, planning, and execution. The model weights are fully open, enabling local deployment and customization. MiniMax-M2.1 represents a major step toward democratizing top-tier agent capabilities.Starting Price: Free -
27
Gemini 3 Deep Think
Google
The most advanced model from Google DeepMind, Gemini 3, sets a new bar for model intelligence by delivering state-of-the-art reasoning and multimodal understanding across text, image, and video. It surpasses its predecessor on key AI benchmarks and excels at deeper problems such as scientific reasoning, complex coding, spatial logic, and visual-/video-based understanding. The new “Deep Think” mode pushes the boundaries even further, offering enhanced reasoning for very challenging tasks, outperforming Gemini 3 Pro on benchmarks like Humanity’s Last Exam and ARC-AGI. Gemini 3 is now available across Google’s ecosystem, enabling users to learn, build, and plan at new levels of sophistication. With context windows up to one million tokens, more granular media-processing options, and specialized configurations for tool use, the model brings better precision, depth, and flexibility for real-world workflows. -
28
Seed2.0 Mini
ByteDance
Seed2.0 Mini is the smallest member of ByteDance’s Seed2.0 series of general-purpose multimodal agent models, designed for high-throughput inference and dense deployment while retaining the core strengths of its larger siblings in multimodal understanding and instruction following. Part of a family that also includes Pro and Lite, the Mini variant is optimized for high-concurrency and batch generation workloads, making it suitable for applications where efficient processing of many requests at scale matters as much as capability. Like other Seed2.0 models, it benefits from systematic enhancements in visual reasoning, motion perception, structured extraction from complex inputs like text and images, and reliable execution of multi-step instructions, but it trades some raw reasoning and output quality for faster, more cost-effective inference and better deployment efficiency. -
29
MathGPT.ai
MathGPT.ai
MathGPT.ai is an AI-driven, cheat-proof math tutoring and course management platform that seamlessly combines a smart course creator with an AI tutor and assignment manager. Educators can integrate pre-loaded OER content, upload custom textbooks, lecture notes, or other resources, then deliver interactive learning experiences as students work directly “within” their materials. The platform guides students through problems using step-by-step reasoning, personalized coaching, and real-time feedback to build deep understanding and confidence. Its proprietary, deterministic AI model is specifically optimized for high accuracy in mathematical reasoning, avoiding typical LLM approximations. Trusted by classrooms, it also features live demos that show how it solves problems and adapts to learning styles, while reinforcing STEM education through curriculum-aligned, infinitely patient instruction.Starting Price: $25 per month -
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GPT-5.4 mini
OpenAI
GPT-5.4 mini is a fast and efficient AI model designed for high-performance tasks such as coding, reasoning, and multimodal understanding. It delivers strong capabilities similar to larger models while maintaining lower latency and cost. The model is optimized for responsive applications where speed is critical, including coding assistants and real-time workflows. GPT-5.4 mini supports advanced features such as tool use, function calling, and image interpretation. It performs well on complex tasks while running significantly faster than previous mini models. The model is also suitable for subagent systems, where it handles smaller tasks within larger AI workflows. By combining speed, efficiency, and strong performance, GPT-5.4 mini enables scalable AI applications across various use cases. -
31
Solar Pro 2
Upstage AI
Solar Pro 2 is Upstage’s latest frontier‑scale large language model, designed to power complex tasks and agent‑like workflows across domains such as finance, healthcare, and legal. Packaged in a compact 31 billion‑parameter architecture, it delivers top‑tier multilingual performance, especially in Korean, where it outperforms much larger models on benchmarks like Ko‑MMLU, Hae‑Rae, and Ko‑IFEval, while also excelling in English and Japanese. Beyond superior language understanding and generation, Solar Pro 2 offers next‑level intelligence through an advanced Reasoning Mode that significantly boosts multi‑step task accuracy on challenges ranging from general reasoning (MMLU, MMLU‑Pro, HumanEval) to complex mathematics (Math500, AIME) and software engineering (SWE‑Bench Agentless), achieving problem‑solving efficiency comparable to or exceeding that of models twice its size. Enhanced tool‑use capabilities enable the model to interact seamlessly with external APIs and data sources.Starting Price: $0.1 per 1M tokens -
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MiniMax M2.5
MiniMax
MiniMax M2.5 is a frontier AI model engineered for real-world productivity across coding, agentic workflows, search, and office tasks. Extensively trained with reinforcement learning in hundreds of thousands of real-world environments, it achieves state-of-the-art performance in benchmarks such as SWE-Bench Verified and BrowseComp. The model demonstrates strong architectural thinking, decomposing complex problems before generating code across more than ten programming languages. M2.5 operates at high throughput speeds of up to 100 tokens per second, enabling faster completion of multi-step tasks. It is optimized for efficient reasoning, reducing token usage and execution time compared to previous versions. With dramatically lower pricing than competing frontier models, it delivers powerful performance at minimal cost. Integrated into MiniMax Agent, M2.5 supports professional-grade office workflows, financial modeling, and autonomous task execution.Starting Price: Free -
33
Qwen2.5-VL-32B
Alibaba
Qwen2.5-VL-32B is a state-of-the-art AI model designed for multimodal tasks, offering advanced capabilities in both text and image reasoning. It builds upon the earlier Qwen2.5-VL series, improving response quality with more human-like, formatted answers. The model excels in mathematical reasoning, fine-grained image understanding, and complex, multi-step reasoning tasks, such as those found in MathVista and MMMU benchmarks. Its superior performance has been demonstrated in comparison to other models, outperforming the larger Qwen2-VL-72B in certain areas. With improved image parsing and visual logic deduction, Qwen2.5-VL-32B provides a detailed, accurate analysis of images and can generate responses based on complex visual inputs. It has been optimized for both text and image tasks, making it ideal for applications requiring sophisticated reasoning and understanding across different media. -
34
QwQ-32B
Alibaba
QwQ-32B is an advanced reasoning model developed by Alibaba Cloud's Qwen team, designed to enhance AI's problem-solving capabilities. With 32 billion parameters, it achieves performance comparable to state-of-the-art models like DeepSeek's R1, which has 671 billion parameters. This efficiency is achieved through optimized parameter utilization, allowing QwQ-32B to perform complex tasks such as mathematical reasoning, coding, and general problem-solving with fewer resources. The model supports a context length of up to 32,000 tokens, enabling it to process extensive input data effectively. QwQ-32B is accessible via Alibaba's chatbot service, Qwen Chat, and is open sourced under the Apache 2.0 license, promoting collaboration and further development within the AI community.Starting Price: Free -
35
GPT-5.2 Thinking
OpenAI
GPT-5.2 Thinking is the highest-capability configuration in OpenAI’s GPT-5.2 model family, engineered for deep, expert-level reasoning, complex task execution, and advanced problem solving across long contexts and professional domains. Built on the foundational GPT-5.2 architecture with improvements in grounding, stability, and reasoning quality, this variant applies more compute and reasoning effort to generate responses that are more accurate, structured, and contextually rich when handling highly intricate workflows, multi-step analysis, and domain-specific challenges. GPT-5.2 Thinking excels at tasks that require sustained logical coherence, such as detailed research synthesis, advanced coding and debugging, complex data interpretation, strategic planning, and sophisticated technical writing, and it outperforms lighter variants on benchmarks that test professional skills and deep comprehension. -
36
Mathstral
Mistral AI
As a tribute to Archimedes, whose 2311th anniversary we’re celebrating this year, we are proud to release our first Mathstral model, a specific 7B model designed for math reasoning and scientific discovery. The model has a 32k context window published under the Apache 2.0 license. We’re contributing Mathstral to the science community to bolster efforts in advanced mathematical problems requiring complex, multi-step logical reasoning. The Mathstral release is part of our broader effort to support academic projects, it was produced in the context of our collaboration with Project Numina. Akin to Isaac Newton in his time, Mathstral stands on the shoulders of Mistral 7B and specializes in STEM subjects. It achieves state-of-the-art reasoning capacities in its size category across various industry-standard benchmarks. In particular, it achieves 56.6% on MATH and 63.47% on MMLU, with the following MMLU performance difference by subject between Mathstral 7B and Mistral 7B.Starting Price: Free -
37
MaxClaw
MiniMax
MaxClaw is a managed AI agent deployment environment created by MiniMax that allows users to launch autonomous AI agents instantly without needing to configure servers, infrastructure, or maintenance. It is designed to simplify the process of building and running intelligent agents by providing an always-on environment where agents can execute tasks, interact with tools, and respond to requests continuously. MaxClaw integrates with the broader MiniMax Agent ecosystem, which uses advanced AI models capable of multi-step planning, reasoning, and task execution across complex workflows. Instead of manually deploying agent frameworks or maintaining cloud infrastructure, users can deploy an operational AI agent within seconds, allowing the system to handle tasks such as automation, research, content generation, coding, or data analysis. -
38
Phi-4
Microsoft
Phi-4 is a 14B parameter state-of-the-art small language model (SLM) that excels at complex reasoning in areas such as math, in addition to conventional language processing. Phi-4 is the latest member of our Phi family of small language models and demonstrates what’s possible as we continue to probe the boundaries of SLMs. Phi-4 is currently available on Azure AI Foundry under a Microsoft Research License Agreement (MSRLA) and will be available on Hugging Face. Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations. Phi-4 continues to push the frontier of size vs quality. -
39
kluster.ai
kluster.ai
Kluster.ai is a developer-centric AI cloud platform designed to deploy, scale, and fine-tune large language models (LLMs) with speed and efficiency. Built for developers by developers, it offers Adaptive Inference, a flexible and scalable service that adjusts seamlessly to workload demands, ensuring high-performance processing and consistent turnaround times. Adaptive Inference provides three distinct processing options: real-time inference for ultra-low latency needs, asynchronous inference for cost-effective handling of flexible timing tasks, and batch inference for efficient processing of high-volume, bulk tasks. It supports a range of open-weight, cutting-edge multimodal models for chat, vision, code, and more, including Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3 . Kluster.ai's OpenAI-compatible API allows developers to integrate these models into their applications seamlessly.Starting Price: $0.15per input -
40
Grok 3 DeepSearch is an advanced model and research agent designed to improve reasoning and problem-solving abilities in AI, with a strong focus on deep search and iterative reasoning. Unlike traditional models that rely solely on pre-trained knowledge, Grok 3 DeepSearch can explore multiple avenues, test hypotheses, and correct errors in real-time by analyzing vast amounts of information and engaging in chain-of-thought processes. It is designed for tasks that require critical thinking, such as complex mathematical problems, coding challenges, and intricate academic inquiries. Grok 3 DeepSearch is a cutting-edge AI tool capable of providing accurate and thorough solutions by using its unique deep search capabilities, making it ideal for both STEM and creative fields.Starting Price: $30/month
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41
Claude Sonnet 3.7
Anthropic
Claude Sonnet 3.7, developed by Anthropic, is a cutting-edge AI model that combines rapid response with deep reflective reasoning. This innovative model allows users to toggle between quick, efficient responses and more thoughtful, reflective answers, making it ideal for complex problem-solving. By allowing Claude to self-reflect before answering, it excels at tasks that require high-level reasoning and nuanced understanding. With its ability to engage in deeper thought processes, Claude Sonnet 3.7 enhances tasks such as coding, natural language processing, and critical thinking applications. Available across various platforms, it offers a powerful tool for professionals and organizations seeking a high-performance, adaptable AI.Starting Price: Free -
42
Tencent Yuanbao
Tencent
Tencent Yuanbao is an AI-powered assistant that has quickly become popular in China, leveraging advanced large language models, including Tencent's proprietary Hunyuan model, and integrating with DeepSeek. The application excels in areas like Chinese language processing, logical reasoning, and efficient task execution. Yuanbao's popularity has surged in recent months, even surpassing competitors such as DeepSeek to top the Apple App Store download charts in China. A key driver of its growth is its deep integration into the Tencent ecosystem, particularly within WeChat, further enhancing its accessibility and functionality. This rapid rise highlights Tencent's growing ambition in the competitive AI assistant market. -
43
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. -
44
MiniMax M2.7
MiniMax
MiniMax M2.7 is an advanced AI model designed to enhance real-world productivity across coding, search, and office workflows. It is trained with reinforcement learning across numerous real-world environments, enabling it to handle complex, multi-step tasks effectively. The model excels in problem-solving by breaking down challenges before generating solutions across multiple programming languages. It delivers high-speed performance with rapid token generation, allowing tasks to be completed efficiently. With optimized reasoning and cost-effective pricing, it provides powerful capabilities while minimizing resource usage. It also achieves strong performance in software engineering benchmarks, reducing incident response time and improving development efficiency. Additionally, it supports advanced agentic workflows and professional-grade office tasks, making it highly versatile for modern work environments.Starting Price: Free -
45
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 -
46
Galactica
Meta
Information overload is a major obstacle to scientific progress. The explosive growth in scientific literature and data has made it ever harder to discover useful insights in a large mass of information. Today scientific knowledge is accessed through search engines, but they are unable to organize scientific knowledge alone. Galactica is a large language model that can store, combine and reason about scientific knowledge. We train on a large scientific corpus of papers, reference material, knowledge bases and many other sources. We outperform existing models on a range of scientific tasks. On technical knowledge probes such as LaTeX equations, Galactica outperforms the latest GPT-3 by 68.2% versus 49.0%. Galactica also performs well on reasoning, outperforming Chinchilla on mathematical MMLU by 41.3% to 35.7%, and PaLM 540B on MATH with a score of 20.4% versus 8.8%. -
47
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: Free -
48
QVQ-Max
Alibaba
QVQ-Max is a visual reasoning model designed to analyze and understand visual content, allowing users to solve complex problems with the help of images, videos, and diagrams. By combining deep reasoning and detailed observation, QVQ-Max can identify objects in photos, process mathematical problems, and even predict the next scene in a video. It also aids in creative tasks, from generating illustrations to writing video scripts, offering a versatile tool for both work and personal use. This first iteration, though still evolving, demonstrates impressive potential in various fields like education, professional work, and everyday problem-solving.Starting Price: Free -
49
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. -
50
DeepSeek-V2
DeepSeek
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.Starting Price: Free
