New LLM Releases April 2026: Every Major Model Launch This Month

April 2026 is shaping up to be one of the most packed months for LLM releases in recent memory. OpenAI is shipping GPT-6, Anthropic previewed Claude Mythos to select partners, Google shipped four Gemma 4 variants under Apache 2.0, and Chinese labs dropped massive open-weight models that rival the best proprietary offerings. Here is everything that shipped, what is confirmed for the rest of the month, and what each release means for developers building AI-powered products.

Quick Reference: April 2026 LLM Releases

| Model | Company | Release Date | Parameters | Context Window | License | Pricing (per 1M tokens) | |---|---|---|---|---|---|---| | GPT-6 | OpenAI | Apr 14 | Undisclosed | 2M tokens | Proprietary | $2.50 in / $12 out | | Claude Mythos | Anthropic | Apr 7 (preview) | Undisclosed | TBA | Proprietary (gated) | $25 in / $125 out | | Gemma 4 31B | Google | Apr 2 | 31B dense | 256K tokens | Apache 2.0 | Free (open weights) | | Gemma 4 26B MoE | Google | Apr 2 | 26B MoE | 256K tokens | Apache 2.0 | Free (open weights) | | Gemma 4 E4B | Google | Apr 2 | ~4B effective | 256K tokens | Apache 2.0 | Free (open weights) | | Gemma 4 E2B | Google | Apr 2 | ~2B effective | 256K tokens | Apache 2.0 | Free (open weights) | | GLM-5.1 | Zhipu AI | Early Apr | 744B MoE (40B active) | 200K tokens | MIT | Free (open weights) | | Qwen 3.6-Plus | Alibaba | Early Apr | Undisclosed | 1M tokens | Open | Free (open weights) | | Llama 4 Scout | Meta | Apr (rolling) | Undisclosed | 10M tokens | Llama License | Free (open weights) | | Llama 4 Maverick | Meta | Apr (rolling) | 400B | 1M tokens | Llama License | Free (open weights) | | Arcee Trinity | Arcee AI | Early Apr | 400B | TBA | Apache 2.0 | Free (open weights) |

The Headline: GPT-6 Launches April 14

OpenAI confirmed on April 7 that GPT-6 (internally codenamed "Spud") will launch globally on April 14, 2026. Pre-training wrapped up on March 17 and post-training is complete.

The numbers represent a generational leap. GPT-6 outperforms GPT-5.4 by more than 40% across coding, reasoning, and agent tasks. HumanEval scores push past 95%, MATH reasoning hits around 85%, and agent task completion rates climb from 62% to roughly 87%.

What makes GPT-6 different

2M token context window. Double the size of GPT-5.4 and Claude Opus 4.6, roughly 1.5 million words of text in a single conversation.

Dual-tier reasoning. GPT-6 uses a two-tier inference framework: System-1 handles rapid responses and content generation (fast thinking), while System-2 performs internal logic verification and multi-step deduction (slow thinking). OpenAI claims this reduces hallucination rates to below 0.1%.

Super-app integration. GPT-6 serves as the engine that merges ChatGPT, Codex, and the Atlas browser into a single desktop application. One agent that can browse, code, and converse without breaking context.

Pricing stays flat. Input at $2.50 per million tokens, output at $12 per million tokens, basically the same as GPT-5.4.

Claude Mythos: Anthropic's Gated Preview

Anthropic announced Claude Mythos Preview on April 7, available exclusively through Project Glasswing to roughly 50 partner organizations. The focus is on cybersecurity vulnerability detection, reasoning, and coding.

Mythos is described as a "step change" above Claude Opus 4.6, which has been the top-performing model on many benchmarks since its February 2026 release. Preview pricing is steep at $25/$125 per million input/output tokens, reflecting the gated early-access nature of the program.

No public release date has been announced. For most developers, Claude Opus 4.6 and Sonnet 4.6 remain the current Anthropic options.

Google Gemma 4: Open-Source Gets Serious

Google released the Gemma 4 family on April 2 under Apache 2.0, delivering four models purpose-built for different deployment scenarios:

• Gemma 4 31B Dense - the flagship, with benchmark scores that outperform models 20 times its size
• Gemma 4 26B MoE - mixture-of-experts variant for efficient inference
• Gemma 4 E4B - consumer GPU and edge deployment
• Gemma 4 E2B - smartphones and Raspberry Pi devices

All four models support 256K context windows, native vision and audio processing, and fluency in over 140 languages. They are purpose-built for advanced reasoning and agentic workflows.

With over 400 million cumulative Gemma downloads, this release under Apache 2.0 (upgraded from earlier, more restrictive licenses) represents a strategic shift in Google's open model approach.

Zhipu GLM-5.1: China's MIT-Licensed Giant

Zhipu AI released GLM-5.1 under the MIT license, a 744-billion-parameter mixture-of-experts model with 40 billion parameters active per forward pass and a 200K context window.

The headline claim: on SWE-Bench Pro, GLM-5.1 reportedly beat both Claude Opus 4.6 and GPT-5.4. Alongside GLM-5.1, Zhipu also released GLM-5V-Turbo, a multimodal variant optimized for coding tasks.

The MIT license makes this one of the most permissive releases of a frontier-scale model to date. No usage restrictions, no registration required.

Alibaba Qwen 3.6-Plus: 1M Context for Agents

Alibaba's Qwen 3.6-Plus targets agentic coding workflows with a 1 million token context window. The model is designed for tasks that require understanding and modifying large codebases in a single pass.

This positions Qwen 3.6-Plus as a direct competitor to Claude Opus 4.6 and GPT-5.4 for the growing market of AI-powered coding agents.

Meta Llama 4: The 10M Token Context Window

Meta's Llama 4 family includes two headline models:

• Llama 4 Scout with a 10 million token context window, the largest of any model released this month
• Llama 4 Maverick with 400 billion parameters, 1 million token context, and native multimodal capabilities

Both models use a mixture-of-experts architecture and are natively multimodal from training (not bolted-on vision after the fact). Meta is using controlled licensing agreements for Llama 4, distinguishing its approach from fully permissive open-source releases.

Arcee Trinity: 400B Under Apache 2.0

Arcee AI released Trinity, a 400 billion parameter model under Apache 2.0. Trinity is designed for enterprise use cases where teams need a large, capable model they can run and modify without licensing restrictions.

What This Means for Developers

The open-source gap is closing fast

Three months ago, proprietary models held a clear lead on reasoning and coding benchmarks. In April 2026, GLM-5.1 claims to beat the best proprietary models on SWE-Bench Pro, and Gemma 4's 31B dense model outperforms models 20x its size. The cost advantage of running open weights on your own infrastructure keeps growing.

Context windows are no longer a differentiator

When the smallest context window in this list is 200K tokens and the largest is 10M, context length alone is not a selling point. The question shifts to how well models actually use long contexts. Retrieval accuracy at 1M+ tokens matters more than the raw number.

Agent capabilities are the new battleground

Every release this month emphasizes agent workflows: GPT-6's super-app integration, Gemma 4's agentic design, Qwen 3.6-Plus's coding agent focus. If you are building AI products, agent reliability (tool calling accuracy, multi-step planning, error recovery) is now the primary differentiator between models.

The catch is that none of these models do anything useful sitting in a chat window. They need an agent loop, a tool layer, and a surface to actually act on. For anything that touches a real desktop (opening a browser tab, editing a Google Doc, filling a CRM, moving files between apps), that layer has to live on the machine. Browser-only agents and cloud sandboxes cover a small slice of what most small businesses actually do in a day.

Pricing compression continues

GPT-6 held pricing flat despite a 40%+ capability jump. Open-weight models are free to run. The cost of intelligence per token continues to fall, making previously expensive workflows (whole-codebase analysis, document processing at scale) economically viable for smaller teams.

Running Any Of These Models On Your Mac

If you are reading this list trying to pick which model to actually use for day-to-day work, the blocker is almost never the model. It is the harness. A 1M token context window and 95% HumanEval do not matter if the thing cannot open your browser, read your screen, and click the right button.

Fazm is the macOS-side answer to that harness problem. It is a local computer-use agent that drives your actual Mac through the accessibility APIs (not screenshots), and it is model-agnostic:

• Point it at any of the models in this post. Fazm supports custom API endpoints, so you can route through the GPT-6 API on April 14, Claude Opus 4.6, a local Gemma 4 31B, or a GLM-5.1 instance hosted behind a corporate proxy. Same agent, different backend.
• It works on the apps you already use. Browser, Google Docs, Sheets, Calendar, your CRM, your invoicing tool, Mail, the Finder. Not a headless Chromium in a data center.
• Voice-first. You describe what you want and it does it. No prompt engineering chat loop.
• Fully open source, runs locally. Source is at github.com/m13v/fazm. Your screen and mic never leave your machine unless you explicitly point it at a hosted model.

It runs on macOS 14 or newer. Native Swift/SwiftUI, no terminal required.

Why the harness matters more than the model

Every new benchmark in the tables above is measured in a sanitized sandbox. The moment you point a model at a messy real desktop, the failure modes change. Latency shifts to accessibility-tree queries and app focus transitions, not token generation. Errors cluster around stale UI state, modal dialogs, and half-loaded pages, not reasoning. A model that wins SWE-Bench Pro can still fail to close a Zoom notification blocking the "Send" button.

The practical bottleneck for small-business automation right now is whichever agent loop plus accessibility plumbing handles those failure modes most gracefully. That is what Fazm optimizes for. The model is a swappable component.

Related reading

• Accessibility APIs vs screenshots for computer control
• AI agents vs copilots: when each wins
• 1M token context windows in practice
• Does a 3-tool-call problem still matter in 2026?

Looking Ahead: What Is Still Coming

The month is not over. Several releases are rumored or confirmed for late April:

• GPT-6 public launch on April 14, the most anticipated release of the month
• Grok 5 from xAI is expected in Q2 2026, possibly as early as late April
• Claude Mythos public availability timeline remains unknown
• The 1M token context window beta for Claude Sonnet 4.5 and Claude Sonnet 4 retires on April 30

April 2026 is a turning point. The sheer volume of high-quality open-weight models, combined with GPT-6's generational leap and Anthropic's gated frontier preview, means developers have more options at lower cost than ever before. The best time to evaluate which model fits your use case is right now.

If you want to evaluate them actually doing things on your Mac (not in a chat window), grab Fazm for free. Plug in whichever of these models you are curious about and point it at a real workflow you would normally do by hand, invoicing, CRM updates, inbox triage, scheduling. That is the honest benchmark.