Open-Source LLM

DeepSeek V4 Flash Vision Exp is an experimental multimodal model that accepts images alongside text, outputs text only, and pairs a 1M-token context window with an Intelligence Index score of 51 — ranked #3 out of 172 comparable models. At cache-hit input prices near $0.0028 per million tokens, it is shaping up as the cheapest capable vision path for high-frequency agent loops. Here is everything developers need to know before building with it. ...

Meta released the Llama 4 family in April 2025, and by mid-2026 these models have settled into clear roles. Llama 4 Scout is the long-context specialist with a 10 million token window and the cheapest per-token cost, while Llama 4 Maverick is the frontier-quality generalist that beats GPT-4o on several benchmarks. Both share the same 17B active parameters via Mixture-of-Experts architecture, but they’re built for very different jobs. Here’s exactly when to use each one, with real pricing, benchmark data, and deployment strategies. ...

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 is a model family, not a promise that every hosting provider offers the same endpoint, context limit or price. A developer needs two records: which model artifact is intended and what the chosen service actually supports. This guide uses Meta’s model card and license, then provides a provider-contract checklist. It does not claim a live inference test or a GPU fit measurement. Scout and Maverick are different artifacts Meta’s Llama 4 model card records the release date as April 5, 2025 and describes: ...

Qwen3’s original open-weight release arrived on April 29, 2025. It contains six dense models and two mixture-of-experts (MoE) models. This guide covers that release; later Qwen3 updates and Qwen3.x families must be checked against their own model cards. Source: Qwen’s release announcement. Correction — October 7, 2026: The previous version gave the wrong release year, dense-model count and size range. It also mixed advertised context limits with runtime memory requirements. Those claims have been corrected; unsupported benchmark rankings and hardware-speed promises have been removed. ...

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. ...