Reasoning model releases

Anthropic's flagship Opus model, released July 24, 2026 — positioned as the go-to model for most knowledge work and automation, approaching the capability of Claude Fable 5 in many categories at roughly half the price. Built for demanding reasoning, autonomous coding, software development, and long-horizon agentic work. Introduces a five-level 'effort' dial exposed to developers on the Claude API and Platform, letting them trade compute and tokens for capability — at lower effort it preserves much of its performance while using fewer tokens and costing less to run. 1M-token context window (available at standard token pricing, not a separate long-context surcharge) with up to 128K output tokens; text, vision, and code. Standard API pricing $5/$25 per Mtok (the same as its predecessor Opus 4.8), plus a Fast mode at $10/$50. Anthropic describes it as its most aligned Opus model and the least susceptible to being tricked into misuse. Becomes the default model for Claude Max subscribers and is available across Anthropic's paid plans; scores 61 on the Artificial Analysis Intelligence Index. Closed weights; architecture, parameter count, and training compute undisclosed.

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.

Google DeepMind's fastest and most cost-effective 3.5-class model, released July 21, 2026 for low-latency and high-throughput agentic workloads like agentic search and document processing. Runs at ~350 output tokens/s (Artificial Analysis) with configurable thinking levels and built-in computer use, priced at $0.30 / $2.50 per 1M input/output tokens. Multimodal over a 1M-token context and a large step up on 3.1 Flash-Lite: Terminal-Bench 2.1 54% (vs 31%), GDM-MRCR v2 72.2% (vs 60.1%), GDPval-AA v2 1140 (vs 642); on several agentic and coding evals it even surpasses 3 Flash (SWE-Bench Pro 54.2% vs 49.6%, OSWorld-Verified 74.0% vs 65.1%). Available in the Gemini API (AI Studio, Android Studio), Gemini Enterprise, the Gemini app, and rolling out in Google Search.

Google DeepMind's July 2026 workhorse Flash model, built for scaling agentic workflows. Multimodal over a 1M-token context, it improves on Gemini 3.5 Flash in coding, knowledge work, and computer use while cutting output-token usage ~17% (up to 65% on some benchmarks like DeepSWE) and taking fewer reasoning steps and tool calls. Ships at a lower price than 3.5 Flash ($1.50 / $7.50 per 1M input/output tokens). Google-reported gains: DeepSWE 49% (vs 37%), MLE-Bench 63.9% (vs 49.7%), OSWorld-Verified 83.0% (vs 78.4%), GDPval-AA v2 1421 (vs 1349); knowledge cutoff advances to March 2026. Computer use is a built-in client-side tool. Available in the Gemini API (AI Studio, Android Studio, Antigravity), Gemini Enterprise, and the Gemini app.

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 efficient, ~32B variant of Kwaipilot's KAT-Coder V2.5, optimized through multi-stage training (supervised fine-tuning plus reinforcement learning). Shares KAT-Coder-Pro's 256K-token context and 80K max output, with function calling and tool use for agentic coding, at roughly a fifth of Pro's cost: $0.15 / $0.60 per million input/output tokens. Surfaced on release trackers on July 14 2026.

Kwaipilot's flagship agentic coding model, from the KAT (Kwaipilot Agentic Tuning) series at Kuaishou. A Mixture-of-Experts model with ~72B active parameters, trained through large-scale agentic reinforcement learning in reconstructed, verifiable repository environments. Supports function calling, tool use, structured JSON output, and prompt caching, with a 256K-token context and up to 80K output tokens. Served via API (StreamLake / Atlas Cloud / OpenRouter) at $0.74 / $2.96 per million input/output tokens. Surfaced on release trackers on July 14 2026.

OpenAI's GPT-5.6 series umbrella row. Officially previewed June 26, 2026 as three durable capability tiers — Sol (flagship), Terra (balanced, for everyday work), and Luna (fast and affordable) — introduced with a new `max` reasoning effort for deeper reasoning and an `ultra` mode that leverages subagents to accelerate complex work. In the GPT-5.6 naming system the number marks the generation while Sol/Terra/Luna are tiers that can advance on their own cadence. Initially a limited preview via the API and Codex for a small group of vetted partners (after U.S. government review), with general availability across ChatGPT, Codex, and the API planned in the following weeks.

Balanced, everyday-work tier of OpenAI's GPT-5.6 series, officially previewed June 26, 2026. OpenAI positions Terra as competitive with GPT-5.5 while being roughly 2x cheaper. Shares the series' new `max` reasoning effort and `ultra` subagent mode and OpenAI's GPT-5.6 safety stack. Begins as a limited preview via the API and Codex for vetted partners after U.S. government review, with broad availability planned in the following weeks. Priced at $2.50 / $15 per million input/output tokens.

Flagship tier of OpenAI's GPT-5.6 series, officially previewed June 26, 2026 — OpenAI's strongest model to date. Adds a new `max` reasoning effort for the deepest reasoning and an `ultra` mode that uses subagents to accelerate complex work. Sets a new state of the art on Terminal-Bench 2.1 (command-line, agentic coding) and shows broad gains in long-horizon biology (GeneBench v1) and cybersecurity (ExploitBench, ExploitGym), paired with OpenAI's most robust safety stack and a phased release. Begins as a limited preview via the API and Codex for a small group of vetted partners after U.S. government review, with general availability planned in the following weeks; also launching on Cerebras at up to 750 tokens/sec in July. Priced at $5 / $30 per million input/output tokens.

Meta Superintelligence Labs' first paid model, released July 9, 2026 in US public preview on the Meta Model API. A natively multimodal reasoning model (text, image, video, PDF, and audio input; text output) with explicit chain-of-thought reasoning and a 1M-token context window that the model actively compacts. Positioned for agentic work and coding — tool use, multi-step workflow coordination, and long-horizon autonomous tasks — and pitched by Meta as roughly a quarter of the price of comparable Anthropic and OpenAI models at $1.25 in / $4.25 out per Mtok (with $20 in free credits per new API account). Marks the first time Meta has charged businesses for one of its models, a departure from the open-weight Llama strategy. Closed weights, undisclosed size. Vendor and third-party benchmarks place it around the Opus 4.8 / GPT-5.5 tier — strongest as an agent/workflow model and in tool-augmented reasoning, competitive but not dominant on coding and multimodal tasks.

SpaceXAI's "Opus-class" agentic flagship, released July 8, 2026 — the first Grok model trained jointly with the coding startup Cursor (Anysphere), on trillions of tokens of real Cursor usage data plus STEM tasks, research papers, and other knowledge work. Targets software engineering, agentic tasks, and knowledge work, with explicit strength in legal and finance use cases (SpaceXAI claims the top spot on the Harvey Legal Agent Benchmark). A Mixture-of-Experts model reported to be built on a ~1.5-trillion-parameter "V9" foundation. Vendor benchmarks are mixed against Anthropic's Opus 4.8 — ahead on DeepSWE 1.0 and Terminal-Bench 2.1, behind on DeepSWE 1.1 and SWE-bench Pro — but markedly more token-efficient (~15,900 output tokens on SWE-bench Pro tasks, ~4.2x fewer than Opus 4.8) and served at ~80 tokens/sec. Base pricing $2/$6 per Mtok; Cursor lists a faster variant at $4/$18. Available in Grok Build (default model), Cursor (all plans), and the SpaceXAI console; initially unavailable in the EU (expected mid-July). xAI was absorbed by SpaceX in Feb 2026 and rebranded SpaceXAI.

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.

Anthropic's most agentic Sonnet model yet, with performance approaching Claude Opus 4.8 at a lower price. Built for coding, tool use (browsers and terminals), and autonomous multi-step agentic work, with selectable effort levels up to xhigh. A substantial upgrade over its predecessor Sonnet 4.6 on reasoning, tool use, coding, and knowledge work, with gains shown on SWE-bench, OSWorld-Verified, BrowseComp, and Humanity's Last Exam. The default model on the Free and Pro plans and available to Max, Team, and Enterprise users; in Claude Code and via the Claude API as claude-sonnet-5. Uses an updated tokenizer (same approach as Opus 4.7). Ships with real-time cyber safeguards enabled by default. Introductory API pricing of $2/$10 per Mtok through Aug 31, 2026, then standard $3/$15.

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).

ByteDance's flagship next-generation agent model (served as Doubao-Seed-2.1-pro), built for the "coding and agent era." A deep-thinking model tuned for strong demand understanding, long-horizon planning, and continuous self-repair across complex coding, long-chain agents, and multi-step engineering delivery, with a 256K-token context. ByteDance reports its core coding, agent, and multimodal capabilities are comparable to GPT-5.5, with the highest score on GDPVal, top-tier results on the Agents' Last Exam, the highest score on MobileWorld, and SOTA results across several visual and video-understanding benchmarks (CharXiv-RQ, MeasureBench, TVBench, TOMATO). Proprietary; available via Doubao and Volcano Engine. List price ¥6 / ¥30 per million input/output tokens.

Sakana AI's frontier-class orchestration model: a single ~7B language model trained to coordinate a swappable pool of external frontier LLMs (model selection, delegation, verification, and synthesis happen internally), exposed behind one OpenAI-compatible API. The Ultra tier is tuned for maximum answer quality on hard, multi-step problems and coordinates a deeper, fixed pool of expert agents. Sakana reports it stands shoulder-to-shoulder with Anthropic's Fable 5 and Mythos Preview across coding, science, and reasoning benchmarks while routing around single-vendor/export-control risk. Current model ID fugu-ultra-20260615; vendor-reported scores include 95.5 GPQA-D, 73.7 SWE-Bench Pro, 93.2 LiveCodeBench, and 50.0 Humanity's Last Exam.

Sakana AI's orchestration model: a single language model that delivers a full multi-agent system behind one OpenAI-compatible API, dynamically routing tasks across a swappable pool of frontier LLMs (including recursive calls to itself). The base tier balances strong performance with low latency as an everyday default for coding, code review, and chat, and lets teams opt specific agents out of the pool for data/privacy/compliance needs. Released alongside Fugu Ultra on June 22, 2026; vendor-reported scores include 95.5 GPQA-D, 92.9 LiveCodeBench, and category-leading SciCode and long-context results.

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.

The public, guardrailed sibling of Claude Mythos 5 and Anthropic's most capable widely released model, built for long-horizon agentic work, coding, vision, and knowledge workflows. Launched June 9, 2026 across the Claude API, AWS, and Microsoft Foundry, then suspended three days later under a U.S. export-control directive. Anthropic says those controls were lifted June 30 and Fable 5 was restored globally on July 1 across Claude Platform, Claude.ai, Claude Code, and Claude Cowork, with cloud partner access being re-enabled. Its safeguards route flagged cybersecurity, biology/chemistry, and distillation requests to Claude Opus 4.8.

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.

Unisound's new-generation, general-purpose "native agentic" large model, built for task execution: it can autonomously decompose and advance complex real-world workflows of 100+ steps rather than single-turn Q&A. Unisound frames it around "high intelligence density x high token value" and reports ~25% lower thinking-token consumption. Available via the Unisound Token Hub.

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

Microsoft's first in-house frontier reasoning model, unveiled at Build 2026. A sparse MoE (~35B active, 256K context) trained entirely on commercially licensed data without third-party distillation. Microsoft reports 97.0% on AIME 2025 and coding parity with Claude Opus 4.6 on SWE-bench Pro.

An inference-efficient agentic coding model from Microsoft (~5B active parameters), trained from the ground up on clean, traceable, enterprise-grade data without third-party distillation. Rolling out in GitHub Copilot; Microsoft reports a +16-point SWE-bench Pro lead over Claude Haiku 4.5.

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.

Anthropic's most capable model, with strengthened agentic and long-running task performance.

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.

Alibaba's proprietary flagship in the Qwen3.7 "Agent Frontier" line — a text-only sparse-MoE model with a 1M-token context, tuned for long-horizon agentic, coding, and reasoning workloads. Parameter count is undisclosed; access is API-only via Alibaba Cloud Model Studio / DashScope (and aggregators such as OpenRouter).

Announced at Google I/O 2026; emphasizes deep multimodal reasoning over a 2M-token context. Recent reporting says the broad launch slipped from June toward July while testers continue using it in Google Antigravity and LMArena.

Google's fast, cost-efficient Gemini 3.5 tier, unveiled at I/O 2026. Multimodal over a 1M-token context and tuned for agentic and coding workflows; Google says it beats Gemini 3.1 Pro on coding and tool-use while running ~4x faster.

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

Baidu's flagship ERNIE 5.1, derived from ERNIE 5.0 by extracting an optimal sub-network from its elastic sub-model matrix — compressing total parameters to ~1/3 and active parameters to ~1/2 of ERNIE 5.0 while reaching leading performance at only ~6% of the pre-training compute of comparable models. A Mixture-of-Experts model trained with a disaggregated fully-asynchronous RL stack and a multi-teacher on-policy-distillation pipeline, tuned for agentic execution, reasoning, world knowledge, and creative writing. Vendor-reported results: 99.6 on AIME26 (with tools, second only to Gemini 3.1 Pro), GPQA and MMLU-Pro approaching leading closed models, surpassing DeepSeek-V4-Pro on τ³-bench and SpreadsheetBench-Verified, and ranking 1st among Chinese models / 4th globally (score 1223) on the LMArena Search Arena. Proprietary; served via ERNIE Bot, Baidu AI Studio, and the Qianfan platform.

OpenAI's May 2026 GPT-5.5 release: a stronger frontier workhorse positioned for deep reasoning, coding, multimodal analysis, and long-context agent workflows. OpenAI lists an 800K-token input context and 128K-token output limit, with API pricing at $3 / $20 per million input/output tokens.

xAI's agentic flagship with a 1M-token context and aggressive API pricing.

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.

OpenAI's frontier reasoning model for life sciences, named after Rosalind Franklin and built to accelerate drug discovery, genomics, protein reasoning, and scientific research workflows. Optimized for multi-step, tool-heavy tasks (literature review, experimental design, sequence-to-function interpretation) with access to 50+ scientific databases via a Codex Life Sciences plugin. A June 3, 2026 update folded in GPT-5.5's agentic coding and tool use while using ~31% fewer tokens. Available as a research preview in ChatGPT, Codex, and the API through OpenAI's trusted-access program; not openly available.

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.

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.

Workhorse GPT-5 release with a dedicated Thinking mode; widely deployed across ChatGPT and the API.

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.

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

OpenAI's February 2026 Codex update, optimized for agentic software engineering in ChatGPT, Codex, and the API. GPT-5.3-Codex improves code quality, patch reliability, repository-scale reasoning, and long-running autonomous coding workflows while keeping the 400K-token input context and 128K-token output limit of the Codex line.

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.

OpenAI's December 2025 Codex model for agentic coding, released after GPT-5.2 with stronger repository understanding, code generation, and tool-use behavior for software engineering agents. The API model is listed with a 400K-token input context, 128K-token output limit, and $1.25 / $10 per million input/output tokens.

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.

OpenAI's December 2025 GPT-5.2 general model release, positioned as a stronger default for reasoning, coding, vision, instruction following, and long-context analysis. OpenAI lists a 400K-token input context, 128K-token output limit, and $2 / $12 per million input/output tokens.

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.

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

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

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

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.

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.

Anthropic's August 2025 Opus point release, focused on stronger coding, reasoning, and agentic reliability over Claude Opus 4. Proprietary API model with text, vision, and code capabilities.

Google's enhanced Gemini 2.5 reasoning mode for harder math, science, coding, and multimodal analysis. Previewed at Google I/O 2025 and later made available to Gemini app subscribers, Deep Think uses more deliberative reasoning for complex prompts.

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.

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 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.

Google's faster, lower-cost Gemini 2.5 model for high-throughput multimodal and agentic workloads. It brought Gemini 2.5's reasoning improvements to a production Flash tier with a 1M-token context and broad text, image, audio, video, and coding support.

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

Mistral's first dedicated reasoning model family, released in Small open-weight and Medium enterprise/API tiers.

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.

First Claude 4 Opus model, positioned for long-running agentic and coding work before the 4.x point releases.

ByteDance Seed reasoning model focused on long-horizon thinking and problem solving.

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.

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

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

Reasoning model released alongside o4-mini with tool use, image reasoning, and stronger agentic problem solving.

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

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

Reasoning-focused Gemini 2.5 model that made thinking a core part of Google's flagship model line.

Baidu's reasoning model released alongside ERNIE 4.5 before the open ERNIE 4.5 weights.

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

Anthropic's first hybrid-reasoning Sonnet. Shut down May 11, 2026 as the 4.x line matured.

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

xAI's third-generation model family, introduced with stronger reasoning, search, and coding modes.

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

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

Doubao 1.5 Pro update positioned for stronger multimodal, reasoning, and agentic work in Volcano Engine.

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

Moonshot's multimodal reinforcement-learning reasoning model, reported as matching OpenAI o1 on math, coding, and multimodal reasoning.

Second-generation StepFun foundation model line with larger-scale multimodal and reasoning ambitions.

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

First Gemini 2.0 release, built for native multimodal input/output, tool use, and agentic product integrations.

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

General release of OpenAI's o1 reasoning model with stronger deliberative reasoning and multimodal ChatGPT integration.

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

Reasoning-preview model exposed in DeepSeek Chat ahead of the open DeepSeek-R1 release.

01.AI's MoE API model that reached the global top-10 on Chatbot Arena, strong in Chinese, math, and coding.

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

OpenAI's first public reasoning-model preview, optimized to spend more inference time on hard math, coding, and science tasks.

Second-generation Grok release with Grok-2 and Grok-2 mini for chat, coding, reasoning, and image-enabled product experiences.

Major Sonnet upgrade that became Anthropic's default high-intelligence workhorse for coding, writing, and visual reasoning.

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

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

Grok update with stronger reasoning and a 128K context window.

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

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

Baidu's fourth-generation ERNIE flagship, announced with stronger understanding, generation, reasoning, and memory.

Google's improved multilingual, reasoning, and coding foundation model family introduced at I/O 2023.

The model that brought reliable multi-step reasoning to the mainstream; size never disclosed.