https://huggingface.co/nex-agi/Nex-N2-mini

Hey everyone,

We're the team behind Krea, and today we're launching Krea 2, our new text-to-image model. Krea 2 is the most aesthetic open-source image model available. On quality, Krea 2 is the #1 text-to-image model from an independent lab on Artificial Analysis.

We are releasing Krea 2 as two variants:

Krea 2 Raw. CFG-guided, built for control and fidelity and training.

Krea 2 Turbo. Distilled and few-step, so it's fast, and it renders up to 2K.

A few things worth knowing:

It's tuned for natural language. Prompt it the way you'd describe an image to a person. Long, specific prompts give the best results, but short ones work fine too.

To render text in an image, wrap the words in quotes, like a sign that reads "open late".
There's a growing set of style LoRAs, and you can load any Krea 2 LoRA by its Hugging Face path.
Try it today:

Code and weights: krea.ai/krea-2-open-source
Technical report: https://www.krea.ai/blog/krea-2-technical-report
Code: github.com/krea-ai/krea-2
Try it on Krea: krea.ai
Try it on Hugging Face: https://huggingface.co/spaces/krea/Krea-2

AMA: We're doing an AMA right here today at 10 AM PT. Ask us anything: how we trained it, the LoRAs, prompting, limitations, what's next. The krea team will be in the comments.

Livestream: we are also doing a livestream with the ComfyUI team at 3PM PT: https://www.youtube.com/watch?v=31jiUhCEjJ4

Thanks for taking a look. We'd genuinely love your feedback, rough edges included.

- The Krea Team

As the title say.

EDIT: Looks like this was a mistake and the scores have been corrected since. Opus now scores 74.3 and is ranked #3, above GLM-5.2

Link to the index: https://artificialanalysis.ai/?intelligence=coding-index

Anyone tried it yet?

https://x.com/designarena/status/2066940737011560652

From Zai: https://x.com/zai_org/status/2066938937344495629

Edit:
Pricing:
input - 1.4$
output - 4.4$

(Damn) https://docs.z.ai/guides/overview/pricing

Ling-2.6-1T: A Trillion-Parameter Comprehensive Flagship Model for Complex Tasks

Today, we are thrilled to open-source Ling–2.6–1T from the Ling family.

Tailored for real–world, complex scenarios, this trillion–parameter model introduces targeted optimizations across inference efficiency, token overhead, and agentic capabilities, making it highly effective for coding and daily workflows.

Key upgrades in Ling–2.6–1T include:

• High Inference Efficiency: By adopting a hybrid architecture combining MLA and Linear Attention, we dramatically reduce latency and VRAM footprint for long contexts. It delivers superior throughput and lower per–token computational costs without sacrificing expressivity, ensuring real–time responsiveness for complex reasoning and tool calling.
• Lower Token Overhead via "Fast Thinking": We introduce a Contextual Process Redundancy Suppression reward strategy during post–training. This reduces reliance on verbose chains–of–thought (CoT), utilizing a "fast thinking" mechanism to reach answers directly and compress output costs while maintaining top–tier intelligence.
• Reliable Multi–Step Execution: With enhanced reasoning, agentic coding, and instruction following, Ling–2.6–1T achieves open–source SOTA on execution–heavy benchmarks, including AIME26, SWE–bench Verified, BFCL–V4, TAU2–Bench, and IFBench.
• Production–Ready for Agent Workflows: Designed for end–to–end engineering—from code generation to bug fixing—Ling–2.6–1T integrates seamlessly with mainstream agent frameworks like Claude Code, OpenClaw, OpenCode, and CodeBuddy, effortlessly handling multi–tool, multi–step constraints in enterprise environments.