Best Open Source Llms May 2026

Best Open Source LLMs of May 2026: We Reviewed 7 Models

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Best Open Source Llms May 2026 Best Open Source LLMs of May 2026: We Reviewed 7 Models

PUBLISHED 5/19/2026

Table of Contents The best open source LLMs at a glance What makes a great open source LLM?

How we evaluated these models How these models compare on benchmarks

Reasoning and knowledge

Software engineering performance

Key takeaways Kimi K2.5

What is Kimi K2.5?

Who should use Kimi K2.5?

Standout features

Pros and cons

FAQ Kimi K2 Thinking

What is Kimi K2 Thinking?

Who should use Kimi K2 Thinking?

Standout features

Pros and cons Qwen3 VL 235B A22B (Vision + LLM)

What is Qwen3 VL 235B?

Who should use Qwen3 VL 235B?

Standout features

Pros and cons

FAQ GLM-5

What is GLM-5?

Who should use GLM-5?

Standout features

Pros and cons

FAQ DeepSeek v3.2

What is DeepSeek v3.2?

Who should use DeepSeek v3.2?

Standout features

Pros and cons

FAQ Google Gemma 3

What is Google Gemma 3?

Who should use Google Gemma 3?

Standout features

Pros and cons

FAQ MiniMax-M2.5

What is MiniMax-M2.5?

Who should use MiniMax-M2.5?

Standout features

Pros and cons

FAQ Why run these models on Fireworks

Performance at scale

Deployment options

Why it matters for these models

Table of Contents

With new open source LLMs launching nearly every week, figuring out which model actually fits your use case has become its own research project. Models like DeepSeek v3.2, Kimi K2.5, and Qwen3 VL now compete at the frontier. Each brings distinct strengths in reasoning, multimodal understanding, and efficiency. The stakes are higher than benchmark scores suggest. Your choice of foundation model shapes inference costs, response latency, and the quality your users experience in production. This roundup compares the top open source LLMs available today, breaking down their trade-offs and ideal applications so you can make an informed decision without running your own eval suite. TL;DR • Choose Kimi K2.5 if you need visual-to-code generation, agent swarms, and multimodal coding at scale. • Choose Qwen3 VL 235B if you need frontier-grade vision, OCR across 32 languages, and GUI automation. • Choose DeepSeek v3.2 if you need elite math reasoning and cost-efficient coding at scale. • Choose Google Gemma 3 if you need multimodal capabilities on a single consumer GPU.

MoE architectures dominate this generation: trillion-parameter models that activate only 10B to 40B parameters per token. All seven models in this roundup are available on Fireworks for instant serverless inference.

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The best open source LLMs at a glance

Model Release date Params Context window Best for On Fireworks MiniMax-M2.5 February 12, 2026 229B total (10B active) 196,608 tokens Real-world productivity and software engineering workflows Try in playground GLM-5 February 12, 2026 744B total (40B active) 200K input tokens Complex systems engineering and long-horizon agentic tasks Try in playground Kimi K2.5 January 27, 2026 1 Trillion total (32B active) 256K tokens Front-end visual coding and distributed market research Try in playground DeepSeek v3.2 December 1, 2025 671B total (37B active) 128K - 163K tokens Math reasoning, enterprise agents, and coding (96.0% GSM8K, 67.8% SWE-Bench) Try in playground Kimi K2 Thinking November 6, 2025 1 Trillion total (32B active) 256,000 tokens Building autonomous AI agents and coding assistants Try in playground Qwen3 VL 235B A22B (Vision + LLM) September 22, 2025 235B total, 22B active 256K tokens natively Deep visual comprehension and agentic GUI automation On-demand deployment Google Gemma 3 March 12, 2025 270M, 1B, 4B, 12B, 27B 128K tokens Local agentic workflows and on-device vision tasks On-demand deployment

What makes a great open source LLM?

Architecture efficiency is the single biggest differentiator in this generation. Six of seven models here use Mixture-of-Experts (MoE), activating a fraction of their total parameters per token. Kimi K2 Thinking has 1 trillion total parameters but only fires 32B per inference step. DeepSeek v3.2 runs 37B of its 671B. That ratio directly determines your GPU bill. Context window size matters, but only if the model holds coherence at length. A 256K window is useless if the model hallucinates at 100K. DeepSeek's Sparse Attention mechanism and GLM-5's 128K output limit represent distinct engineering approaches to the same problem: maintaining quality across long sequences while keeping memory consumption manageable. Licensing determines what you can ship. Apache 2.0 (Qwen3 VL) and MIT (DeepSeek v3.2, GLM-5) impose minimal restrictions. Modified MIT licenses (Kimi K2.5, MiniMax) add usage constraints worth reading before you build a product. For example: The Kimi K2.5 MIT License modification adds a commercial attribution clause for hyperscale usage — if your product exceeds certain thresholds (commonly cited as >100 million monthly active users or >$20 million monthly revenue), you must prominently display “Kimi K2.5” in your product’s interface or marketing. How we evaluated these models

We reviewed technical specifications, architecture papers, and official benchmark results from model cards and HuggingFace repositories. Community feedback from r/LocalLLaMA, Hacker News, and GitHub issues informed our assessment of real-world strengths and failure modes that benchmarks alone miss. We compared models across reasoning quality, coding performance, hardware requirements, licensing terms, and production readiness. Benchmark scores come from official sources and verified third-party evaluations, not self-reported marketing claims. How these models compare on benchmarks

For evaluating the best open source LLMs, we focus on benchmarks that test core reasoning, coding ability, and general knowledge: the fundamental capabilities users expect from top-tier language models. Claude Opus 4.6 benchmarks are provided for reference. Reasoning and knowledge

Model GSM8K (Math) MMLU (Knowledge) Kimi K2.5 94.1% Not available Qwen3 VL 235B 90.3% 87.1% GLM-5 Not available Not available Kimi K2 Thinking 93.9% 83.2% DeepSeek v3.2 96.0% 80.9% - 81.9% Google Gemma 3 (27B) 95.9% 76.9% MiniMax-M2.5 Not available Not available Opus 4.6 99% 91%

What these benchmarks measure: • GSM8K : Mathematical reasoning through grade school math word problems • MMLU : Broad knowledge across 57 academic subjects from elementary to professional level...