Open-weight LLM releases

Ant Group's efficiency-focused Mixture-of-Experts model, released July 23 2026 by its inclusionAI lab: 124B total parameters activating only ~5.1B per token (1/64 expert activation). Ant claims it matches or beats the company's own ~1T-parameter Ling-2.6 flagship on most benchmarks it shows, at 1/8 the total and 1/12 the active parameters — a vendor claim with no public benchmark table or independent audit at launch, so treat it as unverified. Built for production-scale agents (MCP tool use, multi-agent coordination) rather than chat, with both thinking and non-thinking modes. Architecture is a native hybrid-linear attention stack interleaving Kimi Delta Attention (KDA) and Multi-head Latent Attention (MLA) at a reported 5:1 ratio, giving an economical 262,144-token (256K) context, with 1M cited as the scaling target. Ant docs claim peak inference up to 1,000 tokens/s and <100ms time-to-first-token on its own stack. Announced as open-weight under Apache 2.0, but as of July 24 no weights or model card were posted to the inclusionAI Hugging Face org — so the license and open-weight status are announced but unconfirmed (weights not yet downloadable; not self-hostable today). Usable now only via hosted API — free on OpenRouter (as inclusionai/ling-3.0-flash:free, hosted by Novita) and Vercel's AI Gateway through August 3 2026; no post-promo per-token price published at launch.

Poolside's open-weight agentic-coding model and a scale-up of the Laguna XS family (same pre-training data as XS 2.1): a 118B-total / 8B-active Mixture-of-Experts that activates only ~6.8% of its parameters per token, giving larger-model behavior while staying cheap to serve, with a 1M-token context in both thinking and no-thinking modes. Pitched by Poolside as 'the West's most capable open-weight model' — the claim is about its weight class, not the outright frontier. Two modes (off / max, max default; the model sets its own test-time compute budget). Vendor-reported: Terminal-Bench 2.1 70.2% and SWE-bench Multilingual 78.5% (tops the published open disclosed-size table), plus SWE-bench Pro 59.4%, DeepSWE v1.1 40.4%, SWE Atlas 46.2%, Toolathlon Verified 49.7% — matching or beating models several times its size, though closed frontier models still lead outright. Trained in under nine weeks on 4,096 NVIDIA H200 GPUs (pre-training began 22 May 2026); first Poolside model with RL in FP8. Knowledge cutoff November 2025. Weights on Hugging Face under the permissive OpenMDW-1.1 license in BF16/FP8/INT4/NVFP4 with GGUF/MLX conversions and DFlash draft models; at 4-bit it runs on a single NVIDIA DGX Spark. Day-one support for vLLM, SGLang, and Ollama; hosted free at 256K context via OpenRouter and paid at the full 1M context ($0.10 / $0.20 / $0.01 per 1M input / output / cache-read tokens), also on Baseten, Kilo, Prime Intellect, and ZML.

Moonshot's flagship open-weight agentic model and the largest open model released to date: a 2.8T-parameter MoE (896 experts, 16 active per token) using Kimi Delta Attention and Attention Residuals, with native multimodal input and a 1M-token context. Launched via API on Jul 16, 2026 at $3/$15 per Mtok (cached input $0.30); full open weights published to Hugging Face on Jul 26, 2026 — a day ahead of the announced Jul 27 target — under a Modified MIT license, making it freely downloadable and self-hostable.

Thinking Machines Lab's first model and the leading U.S. open-weights release: a natively multimodal Mixture-of-Experts with 975B total / 41B active parameters that reasons across text, image, and audio inputs and emits text. Pretrained on ~45T tokens; served with a 1M-token context from the Hugging Face weights (256K on the hosted Tinker API). Apache-2.0 licensed (BF16 + NVFP4 checkpoints on Hugging Face), built for developers fine-tuning on proprietary data — coding assistants, agents/tool use, chatbots, and RAG — with an explicit low-cost and censorship-resistance focus. Debuted at 41 on the Artificial Analysis Intelligence Index. Hosted pricing (256K) $3.74/$9.36 per Mtok reflects a limited-time 50% launch discount.

The general-availability release of Tencent's third-generation Hunyuan (Hunyuan 3.0), officially launched and open-sourced on July 6, 2026 after April's "Hy3 preview". A 295B-total / 21B-active Transformer MoE with an additional 3.8B multi-token-prediction (MTP) layer and a 256K-token context, offering three selectable inference modes that blend fast and slow thinking. Positioned as a leading open model for its size and cost efficiency, with standout results in coding, search, and scientific reasoning: Tencent reports it rivals flagship open models such as GLM-5.2 and DeepSeek-V4 (at 2-5x the active parameters) and matches or surpasses GPT-5.5 on several science benchmarks. Vendor-reported scores include 78.0 on SWE-bench Verified and 57.9 on SWE-bench Pro. Now Apache-2.0 licensed (the preview used Tencent's community license), with weights on Hugging Face (tencent/Hy3) and ModelScope and a free two-week API route on OpenRouter (tencent/hy3:free) through July 21, 2026. Deeply integrated into WeChat and Tencent's core products.

A deployment-optimized open-weight model from NVIDIA, released July 6, 2026 — a compressed variant of Nemotron-3-Super-120B-A12B produced with "Iterative Puzzle", a post-training compression framework that jointly prunes MoE experts, active-parameter budget, and Mamba state to boost inference efficiency while preserving accuracy. Reduces the parent from 120.7B total / 12.8B active to 75.3B total / 9.3B active, keeping the hybrid Mamba-Transformer LatentMoE architecture with Multi-Token Prediction. Delivers ~2x higher server throughput than Nemotron-3-Super on a single 8xB200 node at matched user throughput and raises sustainable 1M-token single-H100 concurrency from 1 to 8 requests. Targets collaborative agents, chatbots, RAG, complex instruction-following, and long-context reasoning across English, code, and six other languages. Shipped in BF16, FP8, and NVFP4 variants under the OpenMDW-1.1 license.

Rumored / early-access: Mistral AI has confirmed a new open-weight Mixture-of-Experts family — described by CEO Arthur Mensch as "fat but sparse" — aimed at closing the gap with frontier open-weight releases, with early access beginning in July 2026. Mensch confirmed the intent but disclosed almost nothing concrete: no parameter count, no benchmarks, no license terms, and no release date. Tracked as rumored until weights or an official product page land.

Poolside's open-weight small coding model: a 33B-total / 3B-active Mixture-of-Experts built for agentic coding and long-horizon work on a local machine, served at 256K context. An upgraded XS.2 (same architecture) that lifts SWE-bench Multilingual by 5.4 points to 63.1% and improves terminal-style tasks. Ships with open-weighted DFlash speculator (draft) models for each checkpoint that roughly double local tokens/sec, plus BF16/FP8/INT4/NVFP4 quantized checkpoints; supported in vLLM, SGLang, TensorRT-LLM, HF transformers, and Ollama (llama.cpp coming). Newly relicensed under the fully permissive OpenMDW-1.1. Available free on Hugging Face and via a free OpenRouter tier, with paid API pricing of $0.10 / $0.20 / $0.05 per 1M input / output / cache-read tokens. Its predecessor Laguna XS.2 sunsets on Poolside's API one week after launch.

Meituan's open-weight flagship: a 1.6-trillion-parameter Mixture-of-Experts model (~48B active per token, dynamically routed between ~33B and ~56B) with a 1M-token context, built for agentic coding. Notable as the largest Chinese model trained — for both pre-training and inference — entirely on a ~50,000-card cluster of domestic Chinese AI chips (Meituan's use of the Huawei Collective Communication Library points to Huawei Ascend hardware), and the first trillion-parameter model Meituan claims completed full-process training on home-grown compute. Vendor-reported software-engineering results: 59.5 on SWE-bench Pro (ahead of GPT-5.5's 58.6), 70.8 on Terminal-Bench 2.1, and 77.3 on SWE-bench Multilingual, with overall quality positioned as comparable to Gemini 3.1 Pro (self-reported, not yet independently verified). Open-sourced under the MIT license with weights on Hugging Face and GitHub; follows LongCat-Flash (560B, Sep 2025) and the multimodal LongCat-Next (Mar 2026).

Moonshot's open coding-focused agentic model built on K2.6, with native vision/video input, forced thinking mode, and stronger long-horizon software-engineering performance.

Z.ai's latest open flagship for long-horizon coding, agentic engineering, and million-token workflows, adding IndexShare sparse-attention reuse over GLM-5.1.

Native multimodal MiniMax model with a one-million-token context, sparse attention, and agentic coding/cowork positioning.

An open-weight text-diffusion model built on the Gemma 4 26B-A4B MoE backbone (25.2B total / 3.8B active). Denoises text in parallel 256-token blocks for up to ~4x faster generation (1,000+ tok/s on an H100), with a 256K context and text, image, and video input. Apache-2.0.

Cohere's first developer-focused model and the first in its North family of code agents. A 30B-total / 3B-active MoE for agentic coding with a 256K context and up to 64K output, sized to run locally for enterprise coding agents. Apache-2.0.

NVIDIA's largest Nemotron 3 open-weight hybrid Mamba-Transformer MoE, tuned for agentic reasoning, coding, planning, and tool calling.

A dense 12B member of the Gemma 4 family with a unified, encoder-free multimodal architecture: vision and audio are projected straight into the LLM backbone. First medium-size Gemma to natively ingest audio; runs on a 16GB laptop. 256K context, Apache-2.0.

Nex AGI's open-weight agentic flagship, post-trained on Qwen3.5-397B-A17B (397B total / ~17B active MoE) by the Shanghai Innovation Institute-led Nex alliance. Built around an "Agentic Thinking" framework for long-horizon coding, deep research, tool calling, and terminal execution; accepts text and image input and emits text with explicit reasoning traces and function calling. Apache-2.0, ~262K context. Nex reports parity with GPT-5.5 and Claude Opus 4.7 on several agentic and coding evals. A smaller Nex-N2-mini (35B/3B-active) was announced but is not yet open-sourced.

StepFun's high-efficiency multimodal sparse-MoE successor to Step-3.5-Flash: a ~196B-total / ~11B-active vision-language model with native image and video understanding, a 256K context, and selectable reasoning tiers (high/medium/low). Tuned for coding agents and search workflows.

Liquid AI's on-device Mixture-of-Experts model: 8.3B total parameters with only ~1.5B active per forward pass (32 experts, 4 active per token). Uses Liquid's hybrid architecture — 18 double-gated LIV convolution blocks plus 6 grouped-query-attention layers — for a 131K-token context that runs in under ~6GB of memory on consumer hardware. A reasoning-only model that emits an explicit chain of thought before its answer, with strong tool-calling and agentic performance for its size. Builds on the October 2025 LFM2-8B-A1B, expanding the context window to 128K and scaling pretraining from 12T to 38T tokens. Released May 28 2026 under the LFM Open License; caught in a July catalog-gap sweep.

Open-weight agentic model from MiniMax focused on real-world software engineering, office tasks, tool use, and self-improving training workflows.

Dense 27B that punches far above its weight on agentic coding — easy to self-host on a single GPU node.

Efficient V4 companion model with 284B total / 13B active parameters and the same one-million-token context window.

Preview-series sparse MoE flagship with a one-million-token context window and 1.6T total / 49B active parameters.

Tencent's third-generation Hunyuan, rebuilt from scratch in ~90 days and open-sourced as the "Hy3 preview". A 295B-total / 21B-active Transformer MoE (80 layers, 192 experts with top-8 routing, plus a 3.8B multi-token-prediction layer) with a 256K-token context, positioned as a leading open reasoning-and-agent model for its size with strong cost efficiency. Vendor-reported results: 74.4 on SWE-bench Verified, 54.4 on Terminal-Bench 2.0, and 70.2 on WideSearch, with strong STEM-olympiad performance. Open weights on GitHub and Hugging Face under Tencent's community license.

Tencent Hunyuan open-weight fine-grained MoE model with 80B total parameters and 13B active parameters, optimized for agentic tool use.

Xiaomi's open-weight flagship: a 1.02T-parameter Mixture-of-Experts model with ~42B active parameters, a hybrid-attention architecture, and a 1M-token context window. Tuned for frontier-class agentic coding and long-horizon tasks (sustaining 1000+ tool calls with a proper harness). Open-sourced under the MIT license with weights and tokenizer on Hugging Face.

Xiaomi's open-weight sparse-MoE model: ~310B total parameters with ~15B active, trained on ~48T tokens, with a 1M-token context window. Shipped alongside the larger MiMo-V2.5-Pro under the MIT license.

Z.ai agentic-engineering follow-up to GLM-5, with stronger coding performance and better long-horizon tool-use behavior.

Google DeepMind's Gemma 4 advanced-reasoning open model for personal computers, part of the April 2026 Gemma 4 family.

Moonshot's open native multimodal agentic model for long-horizon coding, visual interface generation, and autonomous tool orchestration.

Dense 128B open-weight model with a 256k context and strong coding performance for its size.

Open-weight hybrid Mamba-Transformer MoE designed for collaborative agents and high-volume enterprise workflows.

Mistral's March 2026 Small release: the first Mistral model to unify reasoning (Magistral), multimodal understanding (Pixtral), and agentic coding (Devstral) into one Apache 2.0 model. A 119B-total / ~6B-active Mixture-of-Experts (128 experts, 4 active per token) with native text+image input, a 256K context, and a configurable reasoning_effort toggle for fast or deep responses. API pricing is $0.15 / $0.60 per million input/output tokens.

StepFun's Apache-licensed sparse MoE model for fast agentic execution, coding, math, browsing, and tool-use workflows.

Apache-licensed Indian-context MoE from Sarvam AI, optimized for reasoning, coding, agentic tasks, and 22 Indian languages.

The flagship of Alibaba's small dense Qwen3.5 models. Independent analysis (Artificial Analysis) rated it the most intelligent model under 10B parameters at launch — roughly double the score of the next-closest sub-10B models — and the most intelligent multimodal model under 15B, leading peers on MMMU-Pro (~69%). A dense 9B with native vision, a 262K-token context, and the Qwen3.5 family's unified hybrid thinking / non-thinking mode. Native weights are BF16; in 4-bit it needs ~6GB, within reach of consumer laptops. High intelligence comes with heavy reasoning token usage (~260M output tokens to run the Intelligence Index).

A dense 4B in Alibaba's small Qwen3.5 family, rated by Artificial Analysis as the most intelligent model under 5B parameters at launch — outscoring several 7B–9B peers despite roughly half the parameters. Native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; Apache-2.0 licensed. Scores ~65% on MMMU-Pro multimodal reasoning and runs in ~3GB at 4-bit, suitable for lightweight on-device agents.

A dense 2B Qwen3.5 model built for high-throughput, low-latency edge and on-device use. Despite its size it matches a 7B-class peer on Artificial Analysis's Intelligence Index. Apache-2.0, with native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; runs in under 2GB at 4-bit, fitting laptops and smartphones.

The smallest Qwen3.5 model — a dense 0.8B designed for the most constrained on-device deployments, operating in non-thinking (instruct) mode by default. Apache-2.0, with native vision, a 262K-token context, and the family's hybrid thinking / non-thinking mode; needs roughly 2GB of VRAM and runs under 2GB at 4-bit, targeting smartphones and embedded hardware. Notable for a sub-1B model, it still scores ~26% on MMMU-Pro multimodal reasoning.

Native vision-language MoE supporting 201 languages with a 1M-token context.

Z.ai flagship for complex systems engineering and long-horizon agentic tasks, scaling the GLM line to 744B total / 40B active parameters.

Apache-licensed Qwen3-Next coding-agent model with 80B total / 3B active parameters, 256K context, and long-horizon tool-use training.

Open multimodal Kimi model that adds native visual agentic intelligence, instant and thinking modes, and agent-swarm workflows on top of the K2 base.

Coding-focused GLM release with improved multilingual agentic coding, terminal tasks, tool use, and interface generation.

Ai2's fully open thinking model with public weights, code, data, checkpoints, and training details across the OLMo 3 pipeline.

Efficient Nemotron 3 MoE checkpoint for agentic reasoning and coding, activating about 3B parameters while supporting 1M-token contexts.

Open 106B-class vision-language model with native multimodal function calling for visual agents.

Mistral's largest open-weight MoE, aimed at frontier reasoning while remaining self-hostable.

Reasoning-first agent model that adds DeepSeek Sparse Attention and thinking directly inside tool-use workflows.

High-compute reasoning variant of V3.2, positioned for olympiad-level math, programming, and other deep reasoning tasks.

Liquid AI hybrid model for efficient CPU/GPU/NPU local deployment, using short convolutions plus attention blocks.

Open K2 reasoning-agent variant that interleaves step-by-step thinking with tool calls and supports stable 200-300 step tool-use trajectories.

MIT-licensed hybrid linear-attention model using Kimi Delta Attention, built for million-token contexts with much lower KV-cache usage.

Agentic reasoning and coding upgrade over GLM-4.5, expanding the text context window from 128K to 200K tokens.

Experimental checkpoint that introduced DeepSeek Sparse Attention as an efficiency bridge between V3.1-Terminus and V3.2.

Stability update to V3.1 focused on language consistency, code-agent reliability, and search-agent behavior.

September 2025 K2 update with stronger agentic coding, better frontend generation, and a doubled 256K context window.

Google's open multimodal model: 128k context, 140+ languages, runs on a single GPU.

Hybrid thinking/non-thinking release that upgraded tool calling, long-context training, and agent task performance.

ByteDance Seed's Apache-licensed long-context reasoning and agent model, with controllable thinking budgets and a native 512K context.

Rumored successor to DeepSeek R1. Reports say development and launch timing were affected by hardware constraints around Huawei Ascend training and Nvidia availability; final specs, license, and release date remain unconfirmed.

Vision-language GLM based on GLM-4.5-Air, covering image, video, document, grounding, and GUI-agent tasks.

Smaller gpt-oss reasoning model optimized for local inference on systems with about 16GB of memory.

OpenAI's larger open-weight reasoning model, a 117B-total / 5.1B-active MoE with 128K context for local and self-hosted deployment.

A hybrid attention + state-space-model (SSM) design that matches 70B-class models with fewer parameters.

Open agentic, reasoning, and coding foundation model that marked Z.ai international rebrand and MIT-licensed GLM push.

Compact GLM-4.5 companion with 106B total / 12B active parameters for efficient agentic reasoning and coding.

Alibaba Qwen's large open coding-agent model: a 480B-total / 35B-active MoE released under Apache-2.0, tuned for code generation, repository-level software engineering, tool calling, and long-horizon agent workflows with a 256K-token native context.

LG AI Research's unified model with non-reasoning and reasoning modes, agentic tool use, and English, Korean, and Spanish support.

Original open K2 post-trained model: a 1T-parameter MoE optimized for coding, reasoning, and tool-using agentic workflows.

Hugging Face's fully open 3B multilingual long-context model with optional reasoning mode and 128K context.

Baidu's open ERNIE 4.5 language MoE, part of a 10-variant Apache-licensed model family built with heterogeneous multimodal MoE training.

Baidu's largest ERNIE 4.5 vision-language MoE, supporting text, image, and video inputs with thinking and non-thinking modes.

Updated MIT-licensed Kimi-VL reasoning model with better multimodal reasoning, video understanding, high-resolution perception, and lower thinking-token use.

MIT-licensed coding LLM trained with repository-level reinforcement learning for software issue resolution.

Open Apache-licensed hybrid-attention reasoning model with 456B total / 45.9B active parameters and a native 1M-token context.

Open-weight 24B reasoning model from Mistral's Magistral family, popular for local reasoning experiments.

Major R1 reasoning update with stronger math, programming, general logic, function calling, and reduced hallucinations.

Sarvam's medium-scale open model for multilingual Indian-language chat, reasoning, and translation tasks.

Mistral and All Hands AI's open coding-agent model, released as a 24B Apache-2.0 research preview for software engineering tasks. Devstral is optimized for repository navigation, issue resolution, and agentic coding and is available via Hugging Face and Mistral's API.

Google's mobile-first Gemma 3n model variant, built with a MatFormer-style architecture for efficient on-device multimodal inference. The E4B variant has roughly 4B effective parameters, supports text, vision, audio, and video-oriented use cases, and is released under Gemma terms.

Phi-4 reasoning-specialized model family for math, science, and chain-of-thought style tasks.

Granite 3.3 text update for enterprise chat, RAG, and instruction-following workflows.

Largest open Qwen3 MoE, introducing hybrid thinking/non-thinking modes and 119-language coverage.

Open audio foundation model for audio understanding, generation, speech recognition, audio QA, captioning, and speech conversation.

Efficient MIT-licensed vision-language MoE for OCR, image/video understanding, long documents, and OS-style agent tasks.

Meta's flagship open-weight MoE; highest MMLU among open models at release.

Efficient open-weight MoE designed for very long context on modest hardware.

Meta's announced but unreleased Llama 4 teacher model: a multimodal MoE with 288B active parameters and nearly 2T total parameters. Meta says it was still training when Scout and Maverick shipped and that those released models were distilled from Behemoth.

Open Llama Nemotron reasoning model from NVIDIA's 2025 Nemotron family.

Local omni-modal Qwen model that supports text, image, audio, video, and speech generation in a 7B package.

Post-R1 V3 update with improved reasoning, front-end coding, Chinese writing, search, and function calling.

Apache-licensed Small update adding vision and a 128K context window to the efficient 24B line.

A fully open model — weights, data, and training code all public — and the first such to beat GPT-3.5 / GPT-4o mini.

Enterprise-grade model tuned for RAG, tool use, and multilingual business workloads.

Granite 3.2 update with reasoning controls and multimodal/document-oriented Granite variants.

MIT-licensed 16B/3B-active MoE trained with Moonshot's scalable Muon optimizer experiments.

Nous reasoning-oriented Hermes model trained to combine concise answers with optional deep reasoning traces.

Popular local assistant model tuned for coding, math, function calling, and agentic workflows.

A latency-optimized 24B dense model under Apache-2.0 — a popular local-deployment workhorse.

Vision-language Qwen2.5 model for image, document, video, and agentic visual grounding tasks.

Breakout open reasoning model trained with large-scale reinforcement learning and released with weights under MIT.

Open MiniMax generation with MiniMax-Text-01 and MiniMax-VL-01 long-context models.

The 671B/37B-active MoE release that made DeepSeek a central open-model lab before the R1 breakthrough.

IBM's enterprise-focused open model with a 128k context, Apache-2.0 licensed.

UAE's TII open model designed to run on light infrastructure, including laptops.

Cohere's smallest, fastest R-series model, tuned for RAG and tool use on modest hardware.

A 14B dense model that rivals far larger ones on math and reasoning, under a permissive MIT license.

EXAONE 3.5 32B open-weight model for bilingual reasoning, coding, and long-context tasks.

Late-2024 70B Llama update delivering much of the 405B instruction-following quality at lower serving cost.

Qwen's first public reasoning-preview model, aimed at math, coding, and deliberate problem solving.

Ai2's post-trained open instruction model line, scaling the Tulu recipe to Llama 3.1 405B.

Code-specialized Qwen2.5 model family, with the 32B checkpoint as the flagship open coding model.

Tencent's 389B total / 52B active open-weight Transformer MoE, released with a 256K pretraining context and 128K instruct context.

Compact on-device model family trained on 11T tokens, popular for lightweight local chat and experimentation.

Sarvam's 2B open model trained for ten major Indian languages.

Apache-licensed Granite 3.0 text model, part of IBM's push toward enterprise-friendly open models.

NVIDIA-tuned Llama 3.1 70B instruction model optimized with Nemotron reward and alignment recipes.

First Llama family release with native vision models, alongside smaller edge-oriented 1B and 3B text models.

Open multimodal model family trained for strong image understanding, pointing, and visual grounding.

Broad Qwen2.5 foundation-model update spanning general, coding, math, and multimodal descendants.

Mistral's first open multimodal model, adding image understanding to a Mistral text backbone.

01.AI's compact code model trained for repository-scale programming and code completion tasks.

Unified DeepSeek V2 generation combining general-chat and coding strengths before the V3 series.

Fully open sparse MoE model with 7B total and about 1B active parameters.

Israel's AI21 hybrid Mamba-Transformer MoE, with a 256k context and strong long-document throughput.

Phi-3.5 mixture-of-experts model, scaling Microsoft's small-model line while preserving efficient active parameters.

Large Hermes 3 instruction-tuned model built on Meta's Llama 3.1 405B.

LG's first open-weight EXAONE model, a compact bilingual instruction model for Korean and English.

8B vision-language model for local image, multi-image, OCR, and video understanding, with llama.cpp and Ollama support.

Meta's first frontier-scale open Llama model, with 405B parameters, 128K context, multilingual support, and tool-use improvements.

Apache-licensed 12B model co-developed with NVIDIA, including a 128K context window and strong multilingual tokenization.

Second-generation Gemma model, improving open-weight quality and efficiency at 9B and 27B sizes.

Open code-focused MoE built from DeepSeek-V2, expanding programming-language coverage and coding benchmark performance.

NVIDIA's large open model family for synthetic data generation and reward modeling.

Qwen2's largest dense model, introducing stronger multilingual support, coding/math gains, and long-context variants.

Open GLM-4 9B model family, covering chat, long-context, and code-oriented variants.

Mistral's first code-specialized model, trained for code generation, fill-in-the-middle, and multi-language programming tasks.

Open multilingual research model covering 23 languages, released by Cohere For AI.

Yi 1.5 update with stronger instruction following, coding, math, and multilingual performance.

Falcon 2 generation, including text and vision-language 11B models under a permissive TII license.

DeepSeek's first major MoE general model with Multi-head Latent Attention and low-cost API positioning.

Apache-2.0 code model from IBM's Granite Code family, used for local code generation and enterprise coding assistants.

Apache-2.0 enterprise LLM with 480B total / 17B active parameters, optimized for SQL, code, and instruction following.

3.8B-parameter Phi-3 model released as a phone-capable small model with 4K and 128K variants.

First Llama 3 release, with 8B and 70B open models and a stronger tokenizer, data mix, and post-training stack.

Larger open Mixtral sparse MoE with 141B total and 39B active parameters, released under Apache-2.0.

Microsoft's WizardLM-2 MoE chat model, widely mirrored and run locally after its model-card release.

Open code-specialized Gemma model for local code completion, generation, and instruction-following.

First Jamba hybrid Transformer-Mamba MoE model with open weights and a 256K context length.

Databricks' 132B-total / 36B-active open MoE model for code, math, RAG, and enterprise self-hosted workloads.

Next-generation BigCode code model trained on 4T+ tokens and 600+ programming languages, with 16K context.

First Gemma open-weight text model family, derived from the same research lineage as Gemini.

Largest Qwen1.5 model, released as the bridge from the original Qwen line to Qwen2.

Largest chat-tuned Qwen1.5 dense checkpoint, released with stronger human-preference alignment, multilingual support, and 32K context.

Ai2's first fully open language model release, including weights, training data, code, logs, and intermediate checkpoints.

Small multilingual Stable LM release built for low hardware barriers and local experimentation.

Early DeepSeek sparse MoE research model that foreshadowed the later V2/V3 architecture direction.

Nous instruction-tuned Mixtral model with strong open-chat and tool-use adoption.

Compact Mistral-based local chat model trained with C-RLFT, popular in early 2024 local leaderboards.

Compact Llama-style 1.1B chat model trained for local experimentation and low-memory deployments.

2.7B-parameter Phi model showing strong reasoning and language understanding at small scale.

Sarvam AI's first open Indic language model, adapted from Llama 2 for Hindi and Indian-language work.

The open sparse Mixture-of-Experts that brought MoE efficiency to the open ecosystem.

Alibaba's first major open Qwen model and the start of a prolific open-weight line.

First general DeepSeek language model family, with 7B and 67B base/chat checkpoints.

Chat-tuned Yi-34B checkpoint from 01.AI, released alongside quantized chat variants for bilingual open-weight assistants.

01.AI's strong bilingual open model, with a 200k-context variant.

xAI's first Grok model, later released as open weights with a 314B-parameter MoE checkpoint.

DeepSeek's first public code-model family, released before the general DeepSeek LLM line.

Open vision-language assistant and one of the most widely run early local multimodal models.

The 7B that punched far above its weight and put Mistral on the map.

Second open Qwen size, expanding the first-generation Qwen language-model lineup.

IBM's early Granite foundation model family for enterprise language and code tasks.

At launch the largest openly available model, from the UAE's TII.

Meta's first code-specialized Llama model family, released in base, Python, and instruction-tuned variants.

Alibaba's first open Qwen checkpoint and the start of the Qwen open-model line.

Early Nous Hermes instruction model on Llama 2, widely used in the open-model fine-tuning ecosystem.

The release that made capable open-weight models genuinely usable for production.

Second open ChatGLM generation, improving long context, inference efficiency, and bilingual chat quality.

Microsoft's first Phi small-language-model release, demonstrating strong code performance from textbook-quality synthetic data.

TII's breakout open Falcon model, released before Falcon 180B and trained on the RefinedWeb corpus.

MosaicML's permissively licensed 7B model, an early favorite for commercial local fine-tuning and long-context variants.

LMSYS instruction-tuned LLaMA model that became a landmark early local ChatGPT-style assistant.

Zhipu AI and Tsinghua KEG's first widely used open bilingual ChatGLM checkpoint.

Meta's first LLaMA, released to researchers; its leak catalyzed the open-weight movement.

A science-focused model whose public demo was withdrawn after just three days over confidently wrong outputs — an early, instructive retraction.

An open, multilingual 176B model (46 languages) from a global research collaboration.

Initially withheld over misuse fears, then fully released in Nov 2019 — an early 'limited release' debate.

The bidirectional encoder that reshaped NLP and seeded the transformer era.