Open-source large language models (LLMs) have made huge strides in 2025. Here we introduce five of the leading open models and compare their specs, performance, and ideal use cases. We also discuss how to deploy these models on platforms like Hugging Face, Groq, Fireworks AI, Together AI, and Nebius AI Studio.

GPT-OSS-120B (OpenAI)

• Overview: gpt-oss-120b is OpenAI’s flagship open-weight LLM (Apache 2.0, Aug 2025), optimized for advanced reasoning and tool use.

• Architecture: 117B parameters (5.1B active at inference), Mixture-of-Experts design for high efficiency, runs on a single 80GB GPU. Supports up to 128K context, 4-bit quantization, and adjustable reasoning modes.

• Performance: ~90% on MMLU (matches o4-mini), ~80% on GPQA, ~62% coding pass@1, Safety and tool use are on par with closed models.

• Best Use Cases: Ideal for enterprises needing strong reasoning, coding, and tool-augmented workflows on-premises or in a private cloud. Enables high performance, privacy, and full customization.

👉 GPT-OSS-120B GitHub

GPT-OSS-20B (OpenAI)

• Overview: gpt-oss-20b is a compact, open-weight LLM from OpenAI (Apache 2.0, Aug 2025) for efficient on-device and edge deployments.

• Architecture:: 21B parameters (3.6B active at inference), Mixture-of-Experts design, runs on a single 16GB GPU or laptop. Supports up to 128K context and flexible reasoning modes.

• Performance: ~85% on MMLU (comparable to o3-mini), Strong coding and tool use abilities, Efficient and safety-tested for wide deployment.

• Best Use Cases: Great for running local chatbots, assistants, or tools on laptops, edge, or mobile devices. Delivers solid performance for reasoning and coding with low resource requirements and privacy.

👉 GPT-OSS-20B GitHub

Qwen3-235B-A22B (Alibaba Qwen3 Series)

• Overview: Qwen3-235B-A22B is Alibaba’s flagship open model, featuring 235B total parameters in a Mixture-of-Experts (MoE) design with 22B parameters active per inference. It’s released under Apache 2.0, making it fully open-weight.

• Key Features: Offers hybrid “Thinking Mode” vs “Non-Thinking Mode” to toggle deep reasoning when needed. It supports 119 languages for broad multilingual capability and handles up to 128K context length for long inputs.

• Performance: Competitive with top-tier proprietary models on many benchmarks. For example, Qwen3’s instruct model (no extended reasoning) scores ~87% on MMLU (knowledge exam benchmark). It’s strong at coding and math; on code challenges it achieved ~70% pass@1 on a LiveCode test, nearly matching closed models.

• Best Use Cases: A general-purpose powerhouse for content generation, multilingual chat, and complex Q&A. The “thinking mode” makes it adept at step-by-step reasoning (math proofs, code debugging) when needed, while “non-thinking” mode enables fast responses for simple tasks. Its broad knowledge and language support suit it for content writing, translation, and general AI assistant tasks.

👉 Qwen3-235B-A22B Overview

Kimi K2 (Moonshot AI)

• Overview: Kimi K2 is a 1 trillion parameter MoE model (with 32B active per token) developed by Moonshot AI. It’s one of the largest open models and is optimized for “agentic intelligence”, meaning it excels at using tools and breaking down complex tasks.

• Key Features: Trained on 15.5T tokens with a custom MuonClip optimizer to stabilize ultra-large MoE training. It uses 384 experts (8 active) per token for efficiency. Context window is 128K tokens with a specialized MLA attention mechanism. K2 is finely tuned for tool use and autonomous problem solving, enabling it to call APIs or execute code as part of its responses.

• Performance: Top-tier coding performance among open models. Kimi K2 Instruct achieved 85.7% pass@1 on MultiPL-E (a multilingual HumanEval coding benchmark), nearly matching GPT-4.1’s ~86.7%. On general knowledge, it scores ~89–93% on MMLU depending on the eval setting, rivaling many closed models. It also boasts 65.8% pass@1 on SWE-Bench coding challenges with agentic tool use. In Moonshot’s internal tests, K2’s HumanEval pass@1 was around 80–90%, closing the gap to GPT-4.

• Best Use Cases: Coding assistant and AI agent tasks. Kimi K2 is best for developers needing code generation, debugging, and tool automation (e.g. writing code, then executing it or calling external APIs). It also shines in complex problem solving where multiple steps and tool integrations are required (for example, an AI that can solve a task by searching information or running code). Its strong instruction-following and coding skill make it ideal for software development help, technical writing, and as a back-end for agentic chatbots.

👉 Kimi K2 Overview

DeepSeek R1 (DeepSeek)

• Overview: DeepSeek R1 is an open-source, reasoning-focused LLM (MIT license) released in January 2025. It uses a 671B-parameter Mixture-of-Experts architecture (37B active per step) and was trained primarily with reinforcement learning to promote step-by-step logical reasoning and chain-of-thought abilities.

• Key Features:: Excels at complex reasoning, math, and code generation, Developed via an RL-first pipeline, enabling self-checking and iterative reflection, 128K token context for long or multi-document tasks, Smaller distilled models (1.5B–70B) available for lighter use cases

• Performance: ~90–91% on MMLU (matches GPT-4 level), 97.3% on MATH-500, ~80% on AIME (math), 2029 Elo on Codeforces (coding), ~65% pass@1 on LiveCode, Outperforms most open models at reasoning and coding with efficient active parameter use

• Best Use Cases: Ideal for advanced reasoning, complex problem-solving, and research—such as solving difficult math problems, writing and verifying code, or supporting expert assistants in science, engineering, and finance. DeepSeek R1 is especially strong where step-by-step logic and reliability matter most.

👉 DeepSeek R1 Overview

Specs Comparison at a Glance

Below is a quick comparison of the technical specs of these open LLMs:

• Architecture & Size: All models use Mixture-of-Experts architectures to achieve extreme scale. Qwen3 has 235B parameters (22B active). Kimi K2 pushes to 1T total (32B active, 384 experts). GPT-OSS-120B/20B are MoEs with 117B (5.1B active) and 21B (3.6B active) respectively. DeepSeek R1 uses 671B total (37B active) across 128 experts. Despite the huge total parameters, the active (used) parameters per token are much lower, which helps keep runtime cost reasonable for each model.

• Context Length: All these models support long contexts (great for long documents or conversations). Qwen3 and Kimi K2 can handle up to 128K tokens context. GPT-OSS models also support 128K context with specialized attention windowing. DeepSeek R1 likewise supports 128K tokens context. This far exceeds the 4K–32K context of older models, enabling use cases like processing book-length texts or multi-document QA.

• License: All five are truly open-source. Qwen3 and GPT-OSS use Apache 2.0; Kimi K2 uses a modified MIT license; DeepSeek R1 is MIT licensed. This means they can be used commercially and modified freely (with minimal usage policies in some cases).

• Specialization: Qwen3 is a generalist model (balanced across tasks, with multilingual and chat tuning). Kimi K2 is specialized for coding, tool use, and agent tasks. GPT-OSS models are optimized for reasoning and efficient deployment on consumer hardware. DeepSeek R1 is heavily geared towards reasoning, math, and logic via RL training. These design goals influence which tasks each excels at (as we’ll see next).

Benchmark Performance Comparison

Best Use Cases for Each Model

Qwen3-235B-A22B: Great all-purpose assistant for writing, multilingual chat, and general Q&A. Flexible for both quick responses and in-depth reasoning. Ideal as an open-source ChatGPT alternative with strong language support.

Kimi K2: Best for coding and agentic tasks. Excels at code generation, debugging, and tool use. Ideal for developer assistants or autonomous agents. Requires powerful hardware but delivers top performance for technical workflows.

GPT-OSS 120B & 20B: Best for cost-effective and private deployments. 120B offers strong reasoning on affordable hardware, perfect for in-house or privacy-focused applications. 20B runs on even lighter setups, great for on-device AI or fast chatbots. Both are versatile for interactive assistants and tool use.

DeepSeek R1: Best for advanced reasoning, research, and math-heavy tasks. Excels at step-by-step logic, making it ideal for research assistants, advanced tutoring, or decision-support in complex domains.

Hosting and Inference Platforms for Open Source LLMs

By 2025, hosting open-source LLMs is easy and flexible, with several major platforms:

Keywords AI: Keywords AI provides an AI gateway to over 300+ LLMs where you can find the best open-source or closed-source LLMs for your use case. With a single API call, you can switch between models and get the best performance for your use case.

Hugging Face Inference Endpoints: One-click model deployment with secure APIs, auto-scaling, and broad model support. Simple setup but can get expensive for large models run continuously.

Groq Cloud: Ultra-fast, low-latency inference using custom hardware. Very affordable token-based pricing (e.g., $0.60–$4 per million tokens). Great for real-time and large-scale use.

Fireworks AI: Managed, high-performance serving for open models. Competitive pricing (e.g., DeepSeek R1 at $8 per million tokens vs. $75 for similar closed models), plus support for multimodal and fine-tuning. Emphasizes enterprise features.

Together AI: Flexible multi-model API platform for open LLMs, with fast prototyping, easy model swapping, and fine-tuning. Usage-based pricing and strong performance optimizations.

Nebius AI Studio: Full-stack GenAI platform from Europe, with cost-effective per-token pricing, OpenAI-compatible API, strong compliance, and fine-tuning options. Good for companies needing EU data hosting.

Cost Perspective

Hosting open LLMs on these platforms is much cheaper than using closed APIs—often single-digit dollars per million tokens, with more control and flexibility. Choose based on your priorities: ease of use (Hugging Face), speed and price (Groq, Fireworks), flexibility (Together), or compliance (Nebius). Today, deploying open LLMs is almost as easy as using an API, but with more options and lower costs.