Alternatives to Lumen Outpost
Compare Lumen Outpost alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Lumen Outpost in 2026. Compare features, ratings, user reviews, pricing, and more from Lumen Outpost competitors and alternatives in order to make an informed decision for your business.
-
1
AWS Outposts
Amazon
AWS Outposts is a fully managed service that offers the same AWS infrastructure, AWS services, APIs, and tools to virtually any datacenter, co-location space, or on-premises facility for a truly consistent hybrid experience. AWS Outposts is ideal for workloads that require low latency access to on-premises systems, local data processing, data residency, and migration of applications with local system interdependencies. AWS compute, storage, database, and other services run locally on Outposts, and you can access the full range of AWS services available in the Region to build, manage, and scale your on-premises applications using familiar AWS services and tools. Coming soon, a VMware variant of AWS Outposts will be available. VMware Cloud on AWS Outposts delivers a fully managed VMware Software-Defined Data Center (SDDC) running on AWS Outposts infrastructure on premises. -
2
GLM-5.3
Z.ai
GLM-5.3 is Z.ai’s frontier coding model designed for complex software engineering, long-horizon agent tasks, and advanced post-training research. The model uses the same base model as GLM-5.2, with improvements coming from scaled post-training across more environments, more diverse tasks, and larger compute investment. GLM-5.3 delivers stronger coding performance, better task ownership, improved benchmark results, and greater efficiency across realistic development workflows. It is built to handle complex coding tasks, production-style engineering work, research environments, automation tasks, and agentic workflows that require multi-step execution. The model also shows emergent cyber capabilities in vulnerability discovery and exploitation-chain reasoning, with safety evaluation and hardening planned before open-weight release.Starting Price: Free -
3
SWE-2
Cognition
SWE-2 is Cognition’s advanced coding model designed to improve software engineering performance while reducing the cost of agentic coding workflows. The model is post-trained from Kimi K3 and uses reinforcement learning to optimize multiple reasoning-effort levels within a single training run. SWE-2 is designed to explore codebases more selectively, begin implementation sooner, and complete tasks with fewer redundant reads and reasoning steps than earlier Cognition models. Its capabilities include code generation, debugging, test creation, verification, repository analysis, and complex terminal-based software engineering tasks. The model also emphasizes stronger engineering judgment, end-to-end test coverage, instruction following, and evidence-based verification of user assumptions. SWE-2 is available through Devin Desktop and Devin CLI, with broader rollout planned across Devin Web and Fusion.Starting Price: $20/month -
4
Kimi K2.7 Code
Moonshot AI
Kimi K2.7 Code is an open-source, coding-focused agentic AI model developed by Moonshot AI for long-horizon software engineering tasks. It is designed to improve coding performance, agent workflows, and real-world development assistance compared with earlier Kimi K2 versions. The model supports a 256K context window, making it useful for working with large codebases, long technical documents, and complex multi-step programming tasks. Kimi K2.7 Code is available through Kimi Code and API access, with OpenAI- and Anthropic-compatible options for easier integration into developer workflows. It is also listed on Hugging Face and supports deployment through inference engines such as vLLM, SGLang, and KTransformers. With improved agentic capabilities, long-context support, and reduced thinking-token usage compared with K2.6, Kimi K2.7 Code gives developers a flexible open-source option for AI-assisted coding.Starting Price: Free -
5
GPT-5.5-Cyber
OpenAI
GPT-5.5-Cyber is an advanced cybersecurity-focused AI model designed for verified defenders working on authorized security research, vulnerability discovery, and remediation. The model pairs stronger cyber capabilities with more permissive behavior for specialized workflows that require deep analysis across complex software environments. It can help identify security-relevant components, trace vulnerable code paths, validate likely issues in controlled settings, develop and test patches, and prepare evidence for human review. GPT-5.5-Cyber is built to support the full remediation loop rather than simply generating more findings. The model shows stronger benchmark performance than GPT-5.5 on CyberGym, ExploitGym, and SEC-bench Pro, reflecting improvements in vulnerability reproduction, exploit reasoning, and long-horizon security tasks. GPT-5.5-Cyber is intended for advanced, authorized cybersecurity work with verification, monitoring, scoped controls, and review. -
6
GLM-5
Z.ai
GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.Starting Price: Free -
7
Composer 2
Cursor
Composer 2 is an advanced AI coding model integrated into Cursor, designed to deliver high-level programming performance at a cost-efficient price. It is trained on long-horizon coding tasks, enabling it to solve complex problems that require multiple steps and actions. The model demonstrates strong improvements across key benchmarks, including Terminal-Bench and SWE-bench Multilingual. With enhanced intelligence and efficiency, it provides faster and more accurate code generation. Composer 2 combines strong performance with affordable pricing, making it accessible for developers and teams.Starting Price: $0.50/M input -
8
Qwen Code
Qwen
Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results on Agentic Coding, Browser‑Use, and Tool‑Use tasks comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and more.Starting Price: Free -
9
Qwen3-Coder
Qwen
Qwen3‑Coder is an agentic code model available in multiple sizes, led by the 480B‑parameter Mixture‑of‑Experts variant (35B active) that natively supports 256K‑token contexts (extendable to 1M) and achieves state‑of‑the‑art results comparable to Claude Sonnet 4. Pre‑training on 7.5T tokens (70 % code) and synthetic data cleaned via Qwen2.5‑Coder optimized both coding proficiency and general abilities, while post‑training employs large‑scale, execution‑driven reinforcement learning, scaling test‑case generation for diverse coding challenges, and long‑horizon RL across 20,000 parallel environments to excel on multi‑turn software‑engineering benchmarks like SWE‑Bench Verified without test‑time scaling. Alongside the model, the open source Qwen Code CLI (forked from Gemini Code) unleashes Qwen3‑Coder in agentic workflows with customized prompts, function calling protocols, and seamless integration with Node.js, OpenAI SDKs, and environment variables.Starting Price: Free -
10
Athene-V2
Nexusflow
Athene-V2 is Nexusflow's latest 72-billion-parameter model suite, fine-tuned from Qwen 2.5 72B, designed to compete with GPT-4o across key capabilities. This suite includes Athene-V2-Chat-72B, a state-of-the-art chat model that matches GPT-4o in multiple benchmarks, excelling in chat helpfulness (Arena-Hard), code completion (ranking #2 on bigcode-bench-hard), mathematics (MATH), and precise long log extraction. Additionally, Athene-V2-Agent-72B balances chat and agent functionalities, offering concise, directive responses and surpassing GPT-4o in Nexus-V2 function calling benchmarks focused on complex enterprise-level use cases. These advancements underscore the industry's shift from merely scaling model sizes to specialized customization, illustrating how targeted post-training processes can finely optimize models for distinct skills and applications. -
11
GPT-5.2-Codex
OpenAI
GPT-5.2-Codex is OpenAI’s most advanced agentic coding model, built for complex, real-world software engineering and defensive cybersecurity work. It is a specialized version of GPT-5.2 optimized for long-horizon coding tasks such as large refactors, migrations, and feature development. The model maintains full context over extended sessions through native context compaction. GPT-5.2-Codex delivers state-of-the-art performance on benchmarks like SWE-Bench Pro and Terminal-Bench 2.0. It operates reliably across large repositories and native Windows environments. Stronger vision capabilities allow it to interpret screenshots, diagrams, and UI designs during development. GPT-5.2-Codex is designed to be a dependable partner for professional engineering workflows. -
12
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 -
13
Qwen2.5-Max
Alibaba
Qwen2.5-Max is a large-scale Mixture-of-Experts (MoE) model developed by the Qwen team, pretrained on over 20 trillion tokens and further refined through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In evaluations, it outperforms models like DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro. Qwen2.5-Max is accessible via API through Alibaba Cloud and can be explored interactively on Qwen Chat.Starting Price: Free -
14
BenchPrep
BenchPrep
BenchPrep is a configurable cloud-based learning platform that delivers the best learning experience and drives revenue for nonprofits (credentialing bodies & associations), corporations, and training companies. With an award-winning learner-centric platform, BenchPrep increases learner engagement, improves long-term learner retention, and reduces dropout rates. BenchPrep Ascend increases operating revenue and reduces expenses by enabling learning organizations to support multiple business models and streamline the delivery of online courses. By creating a personalized experience that improves knowledge retention and drives better outcomes, BenchPrep Ascend amplifies the value of your learning program in a highly competitive market. -
15
Koa
Salesforce
Salesforce Koa is Salesforce’s first CRM reasoning model for Agentforce, built on NVIDIA Nemotron and trained on 27 years of Salesforce CRM intelligence to reason through complex, multi-step enterprise work. It is grounded in nearly three decades of CRM deployments and post-trained on a proprietary synthetic dataset modeled on real business processes, workflows, and operational policies. Its training scenarios simulate the reasoning, tool use, and decision-making Agentforce agents perform across the customer lifecycle, from generating leads and qualifying opportunities to resolving service cases, spanning more than 14 industries. Koa is purpose-built for targeted CRM tasks and is evaluated on Salesforce CRM Bench using real-world workflows such as updating opportunities, routing cases, and scheduling follow-ups. Salesforce reports that it is 11% more precise at calling the right action, recalls customer context with 2.1 times greater reliability. -
16
GLM-4.7
Z.ai
GLM-4.7 is an advanced large language model designed to significantly elevate coding, reasoning, and agentic task performance. It delivers major improvements over GLM-4.6 in multilingual coding, terminal-based tasks, and real-world software engineering benchmarks such as SWE-bench and Terminal Bench. GLM-4.7 supports “thinking before acting,” enabling more stable, accurate, and controllable behavior in complex coding and agent workflows. The model also introduces strong gains in UI and frontend generation, producing cleaner webpages, better layouts, and more polished slides. Enhanced tool-using capabilities allow GLM-4.7 to perform more effectively in web browsing, automation, and agent benchmarks. Its reasoning and mathematical performance has improved substantially, showing strong results on advanced evaluation suites. GLM-4.7 is available via Z.ai, API platforms, coding agents, and local deployment for flexible adoption.Starting Price: Free -
17
Outpost
Outpost
Outpost is an AI platform designed to help organizations optimize their visibility and influence within AI-driven search environments by enabling what is known as AI Engine Optimization (AEO). As large language models and AI assistants increasingly become the primary way users discover information, traditional search engine optimization strategies no longer guarantee brand visibility in AI-generated answers. Outpost addresses this shift by providing tools that allow businesses to programmatically manage how their brand appears within responses generated by AI systems. It enables companies to purchase and manage brand mentions within AI-generated outputs, helping ensure their products, services, or domain references appear prominently when users interact with AI assistants or AI search systems. It includes API access for automating campaigns that influence AI citation placement and brand references across different AI platforms. -
18
Pokee-Isaac
Pokee AI
Pokee-Isaac text-only agentic model with a usable context window of up to 10 million tokens. It is designed to reason, plan, call tools, and execute long-horizon tasks while remaining small enough to deploy inside a VPC, on customer premises, on a workstation, or on-device. Pokee reports that Isaac maintains strong long-context performance across RULER from 256K through 10M tokens and leads the evaluated panel on multi-needle retrieval at 256K, 512K, and 1M. Its agentic architecture is built for deterministic function calling, sustained multi-turn coherence, real-shell execution, and discovering and composing tools across live MCP servers. In Pokee’s controlled benchmarks, Isaac ranked first on BFCL v4 and τ³-bench, second on the Terminal-Bench 2.1 text-only subset, and third on MCP-Atlas. Security testing with DTAP also showed the lowest combined attack success rate in the comparison panel while retaining strong benign-task performance.Starting Price: $0.15 per 1M tokens -
19
Fugu-Ultra v1.1
Sakana AI
Fugu-Ultra v1.1 is Sakana AI’s upgraded multi-agent orchestration model for complex coding, agentic work, and advanced reasoning. Rather than relying on one model, it dynamically coordinates a diverse pool of frontier models, selecting and combining specialized agents for each task while presenting the system through a single model interface. The v1.1 orchestration upgrade incorporates newer frontier models and improves performance across every tracked benchmark, with gains of up to 7.9 points over v1.0 and particularly strong results on ProgramBench and Terminal Bench 2.1. Fugu can now be used directly inside Claude Code through Claude Code-compatible endpoints, bringing a coordinated team of models into familiar terminal workflows for writing, debugging, reviewing, and executing code. A one-command installer configures the integration on Ubuntu and macOS, while manual setup is available for Windows and other environments.Starting Price: $6 per 1M tokens (input) -
20
Claude Sonnet 4.5
Anthropic
Claude Sonnet 4.5 is Anthropic’s latest frontier model, designed to excel in long-horizon coding, agentic workflows, and intensive computer use while maintaining safety and alignment. It achieves state-of-the-art performance on the SWE-bench Verified benchmark (for software engineering) and leads on OSWorld (a computer use benchmark), with the ability to sustain focus over 30 hours on complex, multi-step tasks. The model introduces improvements in tool handling, memory management, and context processing, enabling more sophisticated reasoning, better domain understanding (from finance and law to STEM), and deeper code comprehension. It supports context editing and memory tools to sustain long conversations or multi-agent tasks, and allows code execution and file creation within Claude apps. Sonnet 4.5 is deployed at AI Safety Level 3 (ASL-3), with classifiers protecting against inputs or outputs tied to risky domains, and includes mitigations against prompt injection. -
21
e-Bench
CarbonEES
CarbonEES®’s powerful energy and utility management cloud platform e-Bench® will track and benchmark the total energy and carbon emission performance of any building, making management faster and easier. An impressive range of functionality – including targeting and monitoring , invoice reconciliation, management reporting, carbon emission tracking and reporting, continuous commissioning, benchmarking and simulation – in a single integrated software system makes e-Bench® internationally unique. -
22
Hyta
Hyta
Hyta is a platform designed to scale and operationalize AI post-training workflows by creating always-on pipelines of specialized human intelligence and tracking trusted contributions so model improvement is continuous rather than a one-off project. It unifies a community of domain specialists and machine-learning contributors to supply high-quality human signals that support long-horizon, domain-specific model training and reinforcement learning pipelines, with mechanisms to retain contributor trust and context across projects and models. It emphasizes reliable trajectories by tailoring pipelines to organizational and project demands, preserving verified contributions, and enabling persistent feedback that compounds capabilities across industries. Hyta connects contributors, labs, enterprises, and post-training teams in a broader ecosystem, allowing organizations to orchestrate human-in-the-loop workflows at scale and integrate human feedback into model development processes. -
23
Lumen
Lumen Research
Lumen combines state of the art eye tracking technology, deployed on the largest attention panels in the world, with cutting edge ad tech capabilities Lumen’s eye tracking tech converts you phone or computer’s webcam into a high quality eye tracking sensor. This enables us to collect passive eye tracking data at scale and speed, both for media valuation and creative tests. Lumen’s predictive models employ advanced machine learning techniques to estimate attention to ads given their viewability characteristics. Lumen’s innovative approach to eye tracking was recently voted a conference favorite at the conference of the prestigious institute of electrical and electronics engineers. Lumen is an attention technology company that uses eye tracking to help brands measure, buy and amplify attention to their marketing. -
24
Ante
Antigma Labs
Ante is a self-contained coding agent that lives in your terminal and self-organizes. One ~15MB Rust binary, zero runtime dependencies, works with 12+ providers or fully offline with local GGUF models. Continuously evaled in public: #1 same-model agent on Terminal-Bench 2.1, every result pinned to a build you can download and audit.Starting Price: $0 -
25
gpt-realtime
OpenAI
GPT-Realtime is OpenAI’s most advanced, production-ready speech-to-speech model, now accessible through the fully available Realtime API. It delivers remarkably natural, expressive audio with fine-grained control over tone, pace, and accent. The model can comprehend nuanced human audio, including laughter, switch languages mid-sentence, and accurately process alphanumeric details like phone numbers across multiple languages. It significantly improves reasoning and instruction-following (achieving 82.8% on the BigBench Audio benchmark and 30.5% on MultiChallenge) and boasts enhanced function calling, now more reliable, timely, and accurate (scoring 66.5% on ComplexFuncBench). The model supports asynchronous tool invocation so conversations remain fluid even during long-running calls. The Realtime API also offers innovative capabilities such as image input support, SIP phone network integration, remote MCP server connection, and reusable conversation prompts.Starting Price: $20 per month -
26
Lumen by Talkwalker
Talkwalker
Lumen by Talkwalker is a social listening, media monitoring, and social benchmarking platform that helps brands monitor conversations, measure performance, and act on emerging trends. Formerly Talkwalker, the platform provides the same data, reports, workflows, and integrations under its new Lumen by Talkwalker name. It helps organizations track millions of conversations across social, digital, media, and AI channels. Teams can use Lumen by Talkwalker to protect brand reputation, monitor sentiment, benchmark competitors, guide product development, and improve PR and marketing strategies. The platform supports use cases across PR and communications, social marketing, consumer insights, and agencies. Built for brands that need real-time consumer and media intelligence, Lumen by Talkwalker helps teams see what others miss and make smarter decisions faster.Starting Price: $9,600 per year -
27
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 -
28
BenchGen
BenchGen
BenchGen is the learning infrastructure for AI agents: an open platform where developers discover benchmarks and RL environments, evaluate their complete agent system — model and harness together — against verifiable rewards, and export clean trajectory data for fine-tuning. One loop: benchmark → evaluate → fine-tune → re-evaluate. -
29
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 -
30
LongCat-2.0
LongCat
LongCat-2.0 is a 1.6 trillion total-parameter Mixture-of-Experts language model built on AI ASIC superpods, with about 48 billion parameters activated per token and strong performance across coding and agentic tasks. It is a substantial step up from previous LongCat models, combining large-scale sparse architecture with dedicated post-training for real-world software engineering, tool use, long-context reasoning, and multi-step agent workflows. LongCat-2.0 is trained and deployed entirely on AI ASIC superpods, with pretraining spanning more than 35 trillion tokens and millions of accelerator-hours, demonstrating frontier-scale training on alternative hardware platforms. To strengthen long-horizon tasks, the model introduces LongCat Sparse Attention and is trained on hundreds of billions of tokens of 1M-context data, giving it native support for ultra-long context tasks and reliable long-document understanding. -
31
Orchids
Orchids.app
Orchids is an AI-powered app builder designed to help developers create any type of application across any tech stack. It supports building web apps, mobile apps, games, CLI tools, Slack bots, AI agents, and more using popular frameworks like React, Next.js, Python, Swift, and Flutter. The platform works with existing AI subscriptions such as ChatGPT, Claude Code, Gemini, and GitHub Copilot, or any API key. Positioned as a full-stack coding agent, Orchids assists with end-to-end app development from idea to execution. It is trusted by over one million users and Fortune 500 teams worldwide. Orchids ranks highly on industry benchmarks, including #1 positions on App Bench and UI Bench. Available for macOS, it provides developers with a flexible and powerful environment for building applications quickly.Starting Price: $21 per month -
32
Tülu 3
Ai2
Tülu 3 is an advanced instruction-following language model developed by the Allen Institute for AI (Ai2), designed to enhance capabilities in areas such as knowledge, reasoning, mathematics, coding, and safety. Built upon the Llama 3 Base, Tülu 3 employs a comprehensive four-stage post-training process: meticulous prompt curation and synthesis, supervised fine-tuning on a diverse set of prompts and completions, preference tuning using both off- and on-policy data, and a novel reinforcement learning approach to bolster specific skills with verifiable rewards. This open-source model distinguishes itself by providing full transparency, including access to training data, code, and evaluation tools, thereby closing the performance gap between open and proprietary fine-tuning methods. Evaluations indicate that Tülu 3 outperforms other open-weight models of similar size, such as Llama 3.1-Instruct and Qwen2.5-Instruct, across various benchmarks.Starting Price: Free -
33
AgentBench
AgentBench
AgentBench is an evaluation framework specifically designed to assess the capabilities and performance of autonomous AI agents. It provides a standardized set of benchmarks that test various aspects of an agent's behavior, such as task-solving ability, decision-making, adaptability, and interaction with simulated environments. By evaluating agents on tasks across different domains, AgentBench helps developers identify strengths and weaknesses in the agents’ performance, such as their ability to plan, reason, and learn from feedback. The framework offers insights into how well an agent can handle complex, real-world-like scenarios, making it useful for both research and practical development. Overall, AgentBench supports the iterative improvement of autonomous agents, ensuring they meet reliability and efficiency standards before wider application. -
34
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 -
35
Codestral Embed
Mistral AI
Codestral Embed is Mistral AI's first embedding model, specialized for code, optimized for high-performance code retrieval and semantic understanding. It significantly outperforms leading code embedders in the market today, such as Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model. Codestral Embed can output embeddings with different dimensions and precisions; for instance, with a dimension of 256 and int8 precision, it still performs better than any model from competitors. The dimensions of the embeddings are ordered by relevance, allowing users to choose the first n dimensions for a smooth trade-off between quality and cost. It excels in retrieval use cases on real-world code data, particularly in benchmarks like SWE-Bench, which is based on real-world GitHub issues and corresponding fixes, and Text2Code (GitHub), relevant for providing context for code completion or editing. -
36
OUTSCAN
Outpost24
Outpost24 Netsec solutions provide capabilities to identify, categorize, manage, and report on network-attached Information Technology (IT) assets and their security vulnerabilities such as insecure system configurations or missing security updates. Customers may choose how frequently they assess their IT assets. Results of assessments are typically used to inform supporting operations teams of recommendations for remediation and mitigation. Once remediated, users can choose to verify the vulnerability has been resolved with a focused re-assessment of the IT asset. Additionally, results are used by security teams to measure compliance and reduce cyber exposure or enterprise risk. Outpost24 customers contract for an annual subscription to use the Netsec service. The scope of service scales based on the number of IP addresses to be assessed, the frequency of assessment, and optionally on the number of HIAB virtual appliances that are licensed. -
37
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 -
38
FutureHouse
FutureHouse
FutureHouse is a nonprofit AI research lab focused on automating scientific discovery in biology and other complex sciences. FutureHouse features superintelligent AI agents designed to assist scientists in accelerating research processes. It is optimized for retrieving and summarizing information from scientific literature, achieving state-of-the-art performance on benchmarks like RAG-QA Arena's science benchmark. It employs an agentic approach, allowing for iterative query expansion, LLM re-ranking, contextual summarization, and document citation traversal to enhance retrieval accuracy. FutureHouse also offers a framework for training language agents on challenging scientific tasks, enabling agents to perform tasks such as protein engineering, literature summarization, and molecular cloning. Their LAB-Bench benchmark evaluates language models on biology research tasks, including information extraction, database retrieval, etc. -
39
Olmo 2
Ai2
Olmo 2 is a family of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with transparent access to training data, open-source code, reproducible training recipes, and comprehensive evaluations. These models are trained on up to 5 trillion tokens and are competitive with leading open-weight models like Llama 3.1 on English academic benchmarks. Olmo 2 emphasizes training stability, implementing techniques to prevent loss spikes during long training runs, and utilizes staged training interventions during late pretraining to address capability deficiencies. The models incorporate state-of-the-art post-training methodologies from AI2's Tülu 3, resulting in the creation of Olmo 2-Instruct models. An actionable evaluation framework, the Open Language Modeling Evaluation System (OLMES), was established to guide improvements through development stages, consisting of 20 evaluation benchmarks assessing core capabilities. -
40
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 -
41
Claude Opus 4.5
Anthropic
Claude Opus 4.5 is Anthropic’s newest flagship model, delivering major improvements in reasoning, coding, agentic workflows, and real-world problem solving. It outperforms previous models and leading competitors on benchmarks such as SWE-bench, multilingual coding tests, and advanced agent evaluations. Opus 4.5 also introduces stronger safety features, including significantly higher resistance to prompt injection and improved alignment across sensitive tasks. Developers gain new controls through the Claude API—like effort parameters, context compaction, and advanced tool use—allowing for more efficient, longer-running agentic workflows. Product updates across Claude, Claude Code, the Chrome extension, and Excel integrations expand how users interact with the model for software engineering, research, and everyday productivity. Overall, Claude Opus 4.5 marks a substantial step forward in capability, reliability, and usability for developers, enterprises, and end users. -
42
Devstral
Mistral AI
Devstral is an open source, agentic large language model (LLM) developed by Mistral AI in collaboration with All Hands AI, specifically designed for software engineering tasks. It excels at navigating complex codebases, editing multiple files, and resolving real-world issues, outperforming all open source models on the SWE-Bench Verified benchmark with a score of 46.8%. Devstral is fine-tuned from Mistral-Small-3.1 and features a long context window of up to 128,000 tokens. It is optimized for local deployment on high-end hardware, such as a Mac with 32GB RAM or an Nvidia RTX 4090 GPU, and is compatible with inference frameworks like vLLM, Transformers, and Ollama. Released under the Apache 2.0 license, Devstral is available for free and can be accessed via Hugging Face, Ollama, Kaggle, Unsloth, and LM Studio.Starting Price: $0.1 per million input tokens -
43
BenchLLM
BenchLLM
Use BenchLLM to evaluate your code on the fly. Build test suites for your models and generate quality reports. Choose between automated, interactive or custom evaluation strategies. We are a team of engineers who love building AI products. We don't want to compromise between the power and flexibility of AI and predictable results. We have built the open and flexible LLM evaluation tool that we have always wished we had. Run and evaluate models with simple and elegant CLI commands. Use the CLI as a testing tool for your CI/CD pipeline. Monitor models performance and detect regressions in production. Test your code on the fly. BenchLLM supports OpenAI, Langchain, and any other API out of the box. Use multiple evaluation strategies and visualize insightful reports. -
44
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 -
45
LumenRT
Bentley Systems
Use LumenRT to enliven models with life and nature, and produce attention-grabbing real-time visualizations. No matter your technical experience, you can easily use LumenRT to render cinematic quality in real time, animate models, incorporate digital nature, integrate seamlessly within CAD and GIS workflows, and share your creations with other stakeholders and clients. With Bentley LumenRT you no longer have to be a computer graphics expert in order to integrate life-like digital nature into your simulated infrastructure designs, and create high-impact visuals for stakeholders. This revolutionary real-time visualization medium is both easy for any professional in the AECO industry to use and able to produce stunningly beautiful and easily understandable visualizations. Bentley users engaged in the capture of existing conditions to provide context for their designs can further benefit from reality modeling “enlivened’ with digital nature. -
46
Gemini 2.5 Deep Think
Google
Gemini 2.5 Deep Think is an enhanced reasoning mode within the Gemini 2.5 family that uses extended, parallel thinking and novel reinforcement learning techniques to tackle complex, multi-step problems in areas like math, coding, science, and strategic planning by generating and evaluating multiple lines of thought before responding, producing more detailed, creative, and accurate answers with support for longer replies and built-in tool integration (e.g., code execution and web search). Its performance shows state-of-the-art results on rigorous benchmarks, including LiveCodeBench V6 and Humanity’s Last Exam, and it demonstrates notable gains over previous versions in challenging domains, with internal evaluations also indicating improved content safety and tone-objectivity, though with a higher tendency to decline benign requests; Google is conducting frontier safety evaluations and implementing mitigations to manage risks as the model’s capabilities advance. -
47
Claude Opus 4.1
Anthropic
Claude Opus 4.1 is an incremental upgrade to Claude Opus 4 that boosts coding, agentic reasoning, and data-analysis performance without changing deployment complexity. It raises coding accuracy to 74.5 percent on SWE-bench Verified and sharpens in-depth research and detailed tracking for agentic search tasks. GitHub reports notable gains in multi-file code refactoring, while Rakuten Group highlights its precision in pinpointing exact corrections within large codebases without introducing bugs. Independent benchmarks show about a one-standard-deviation improvement on junior developer tests compared to Opus 4, mirroring major leaps seen in prior Claude releases. -
48
Lumen Vyvx
Lumen
Live sporting events. 24/7 TV channels. Breaking news. Huge broadcast events. Whatever your content is and wherever it needs to go, Lumen VyvxLumen Vyvx can help get it there. Lumen Vyvx is the trusted global provider of video acquisition, distribution and delivery for many of the world’s largest and most innovative broadcasting and media companies. From sports events to full-time channel distribution, Lumen Vyvx brings 30+ years of broadcast video experience and a full portfolio of end-to-end video services and solutions on top of our global network. -
49
Ferret
Apple
An End-to-End MLLM that Accept Any-Form Referring and Ground Anything in Response. Ferret Model - Hybrid Region Representation + Spatial-aware Visual Sampler enable fine-grained and open-vocabulary referring and grounding in MLLM. GRIT Dataset (~1.1M) - A Large-scale, Hierarchical, Robust ground-and-refer instruction tuning dataset. Ferret-Bench - A multimodal evaluation benchmark that jointly requires Referring/Grounding, Semantics, Knowledge, and Reasoning.Starting Price: Free -
50
Gen-4.5
Runway
Runway Gen-4.5 is a cutting-edge text-to-video AI model from Runway that delivers cinematic, highly realistic video outputs with unmatched control and fidelity. It represents a major advance in AI video generation, combining efficient pre-training data usage and refined post-training techniques to push the boundaries of what’s possible. Gen-4.5 excels at dynamic, controllable action generation, maintaining temporal consistency and allowing precise command over camera choreography, scene composition, timing, and atmosphere, all from a single prompt. According to independent benchmarks, it currently holds the highest rating on the “Artificial Analysis Text-to-Video” leaderboard with 1,247 Elo points, outperforming competing models from larger labs. It enables creators to produce professional-grade video content, from concept to execution, without needing traditional film equipment or expertise.
