Open-Source-Llm

GLM-5.1 is the first open-weight model to top SWE-Bench Pro, scoring 58.4 against GPT-5.4 (57.7) and Claude Opus 4.6 (57.3) — at API prices 5–10x lower than Anthropic’s flagship. It is not a universal winner, but for coding and agentic tasks, it has genuinely closed the gap with frontier closed models. What Is GLM-5.1? The Open-Weight Model That Shocked the Leaderboard GLM-5.1 is an open-weight large language model released by Zhipu AI (Z.ai) in April 2026, built on a 754-billion-parameter Mixture-of-Experts (MoE) architecture that activates only 40 billion parameters per token — the same efficiency design used by Mixtral and DeepSeek-V3. On April 7, 2026, GLM-5.1 became the first open-source model to claim the global #1 position on Scale AI’s SWE-Bench Pro leaderboard, scoring 58.4% against GPT-5.4 at 57.7% and Claude Opus 4.6 at 57.3%. That ranking held for 9 days before Claude Opus 4.7 reclaimed the top spot at 64.3%. The model ships under an MIT license, runs on vLLM and SGLang, supports a 200K-token context window with up to 128K output tokens, and was trained entirely on Huawei Ascend 910B chips — zero Nvidia GPU involvement. As of May 2026, it sits at #18 overall on Chatbot Arena and holds the #1 open-source model slot. For teams doing high-volume code generation or autonomous agent workflows, GLM-5.1 is the first open-weight option worth taking seriously against paid frontier APIs. ...

Gemma 4 31B scores 89.2% on AIME 2026 — a 330% improvement over Gemma 3 27B’s 20.8% — while Qwen3-235B-A22B leads on GPQA Diamond at 77.2% and Llama 4 Scout holds the record with a 10 million token context window. Three competitive open-source model families launched in 2026, each with distinct architectural advantages that make the choice non-obvious. Gemma 4 leads on reasoning-per-parameter efficiency. Llama 4’s Scout model offers an unmatched context window for processing entire codebases. Qwen 3 provides the strongest raw coding performance at full size. This guide covers the technical and practical differences for developers choosing which family to run locally or deploy in production. ...

GLM-4.7 from Zhipu AI scores 73.8% on SWE-bench and 84.9% on LiveCodeBench V6 — numbers that match or beat Claude Sonnet 4.5 on coding benchmarks. It’s fully open-source (Apache 2.0), runs locally, and costs $0 per token. If you’re paying $20+/month for a commercial coding assistant and your use case is standard development tasks, GLM-4.7 deserves a serious look. What Is GLM-4.7 and Why Are Developers Switching? GLM-4.7 is Zhipu AI’s flagship open-source large language model, optimized for multi-turn reasoning and software development tasks. Launched in early 2026, it sits at the top of the open-source coding benchmark leaderboard: 73.8% on SWE-bench and 84.9% on LiveCodeBench V6, putting it within 2-3 percentage points of Claude Sonnet 4.5. What makes GLM-4.7 different from previous open-source coding models isn’t just benchmark scores — it’s the “Preserved Thinking” architecture that maintains reasoning quality across extended, multi-turn coding sessions. Most open-source models degrade noticeably after 5-6 back-and-forth exchanges as context fills up. GLM-4.7 scores 8.5/10 for complex reasoning consistency across 10+ turns, a gap that shows up directly when you’re doing iterative refactoring or debugging complex systems. Zhipu AI also made a hardware bet: GLM series models are trained entirely on Huawei Ascend chips, not NVIDIA, which matters for organizations concerned about supply chain dependencies. The combination of competitive benchmarks, zero licensing costs, and hardware independence is driving 40% year-over-year growth in open-source coding model adoption according to GitHub’s 2026 developer survey. ...

DeepSeek V4-Pro, released April 24, 2026 under an MIT license, tops LiveCodeBench at 93.5% and costs $1.74/M input tokens — roughly 70-80x less than GPT-5.4 Pro’s $30/M. For most coding workloads, it’s the strongest cost-performance trade-off available today. What Is DeepSeek V4? (April 2026 Release Overview) DeepSeek V4 is a family of large language models released on April 24, 2026 by DeepSeek, a Chinese AI research lab. The family includes two variants: V4-Pro, a 1.6 trillion-parameter Mixture-of-Experts (MoE) model with 49 billion active parameters per token, and V4-Flash, a lighter 284 billion-parameter model with 13 billion active parameters. Both models support a 1 million token context window and are released under an MIT open-source license, making them freely available on Hugging Face for self-hosted deployments. DeepSeek has also merged its prior “R” (reasoning) series into V4, which means both variants ship with switchable thinking mode — you can toggle extended chain-of-thought reasoning on or off per request. NIST’s CAISI evaluation published in May 2026 found V4-Pro performs comparably to GPT-5, a model released roughly eight months earlier. The MIT license combined with Hugging Face availability fundamentally changes the economics for enterprises that can run inference in-house: the hosted API price advantage becomes a floor, not a ceiling. ...

Llama 4 Scout and Maverick are Meta’s open-weight multimodal models — available today via multiple API providers with OpenAI-compatible endpoints. Scout offers a 10M-token context window at $0.08–$0.15 per 1M input tokens; Maverick beats GPT-4o on MMLU, HumanEval, and SWE-bench. Here’s how to integrate both. What Is Llama 4? Scout, Maverick, and Behemoth Explained Llama 4 is Meta’s fourth-generation open-weight large language model family, released in April 2026 as a multimodal, Mixture-of-Experts architecture covering three tiers: Scout, Maverick, and the research-preview Behemoth. Scout has 17B active parameters out of ~109B total across 16 experts, with a groundbreaking 10-million-token context window — the largest available in any production API as of May 2026. Maverick scales to ~400B total parameters (still 17B active per forward pass) across 128 experts and delivers benchmark scores of 91.8% MMLU, 91.5% HumanEval, and 74.2% SWE-bench, outperforming GPT-4o and Gemini 2.0 Flash. Behemoth sits at ~2 trillion total parameters with 288B active — still in training and research preview, not yet available via public API. All three models support multimodal inputs (text + images), structured output, function calling, and streaming. The key architectural insight is that active parameter count — not total — determines inference cost, which is why both Scout and Maverick run at the speed of a ~17B dense model while achieving quality far above their class. Meta released these models under a custom Llama 4 Community License that permits commercial use with attribution for most use cases. ...

Qwen 3 is Alibaba’s open-source LLM family released in 2026, spanning eight dense models (0.6B to 32B) and two MoE models (30B-A3B, 235B-A22B). All models run in both thinking and non-thinking modes, are licensed Apache 2.0, and were trained on 36 trillion tokens across 119 languages. What Is Qwen 3? Alibaba’s Biggest Open-Source LLM Family Yet Qwen 3 is a family of open-weight large language models developed by Alibaba’s Qwen team, spanning from ultra-lightweight 0.6B edge models to the 235B-parameter MoE flagship that competes head-to-head with GPT-4o and Gemini 2.5 Pro. Unlike previous generations that separated chat models from reasoning models, every Qwen 3 model ships with a built-in dual-mode thinking system: flip a soft switch in your prompt and the same model either engages deep chain-of-thought reasoning or returns fast responses like a traditional assistant. Trained on 36 trillion tokens across 119 languages and dialects — up from 29 in Qwen 2.5 — the family covers code, math, STEM reasoning, and multilingual tasks under a single Apache 2.0 license. The flagship Qwen3-235B-A22B scores 95.6 on ArenaHard and 2056 on CodeForces Elo, outperforming DeepSeek-R1 on 17 of 23 benchmarks. For developers, this is the first open-source family where one model can genuinely replace both a reasoning specialist and a general-purpose chat model. ...

Qwen3-Coder is Alibaba’s open-source coding LLM family that scores 69–70% on SWE-bench Verified while costing 85x less than Claude Opus 4.6 — and the 80B Next variant runs on a single MacBook Pro with 48GB unified memory. If you’re running multi-model coding pipelines or need a cost-effective alternative for overnight refactors and batch PR triage, this is the model to benchmark first. What Is Qwen3-Coder and Why Does It Matter in 2026? Qwen3-Coder is a family of open-source Mixture-of-Experts (MoE) coding language models released by Alibaba’s Qwen team under the Apache 2.0 license. The lineup spans from a 1.5B model for IDE autocomplete all the way to a 480B MoE model for maximum benchmark performance. What makes the 2026 release significant is the convergence of two trends: open-source models have closed the SWE-bench gap to within single-digit percentage points of Claude Opus 4.6 (80.8%), while API pricing has dropped so dramatically that $0.22 per million input tokens is now viable for continuous coding workloads that would cost hundreds of dollars per day with GPT-5. The February 2026 wave saw six models released — MiniMax M2.5 (80.2%), GLM-5 (77.8%), Qwen3-Coder-Next (70.6%), among others — that would have each led all public benchmarks just 12 months earlier. For developers who self-host or use cost-sensitive pipelines, Qwen3-Coder is no longer a compromise. It is a first-choice option backed by serious infrastructure: RL training across 20,000 parallel environments on Alibaba Cloud using real GitHub issues, LeetCode challenges, and Codeforces problems. ...