Moonshot launches Kimi K3: the world's largest open-weight model at 2.8 trillion parameters — closing in on Claude Fable 5 and GPT-5.6 Sol

On July 16, 2026, Chinese AI lab Moonshot AI (backed by Alibaba) announced Kimi K3 — a 2.8-trillion-parameter model the company calls the world's largest open-weight model, with full weights to be publicly released on July 27, 2026. The headline: independent evaluations place K3 just behind today's two strongest closed models, Claude Fable 5 and GPT-5.6 Sol — at a fraction of their API prices.

What is Kimi K3?

Per Kimi's official blog, K3 is Moonshot's most capable model to date: 2.8 trillion parameters, built on two architectural updates — Kimi Delta Attention (KDA) and Attention Residuals (AttnRes) — with native vision (not a bolted-on module) and a 1-million-token context window. Moonshot calls it "the world's first open 3T-class model," aimed at long-horizon coding, knowledge work and deep reasoning.

Architecturally, K3 pushes Mixture-of-Experts sparsity further: each query activates just 16 of 896 experts. Combined with refined training recipes, Moonshot says K3 converts compute into intelligence about 2.5× more efficiently than Kimi K2. The model reasons by default — the API currently exposes a single reasoning_effort level, max, with lighter modes to follow.

Benchmarks: third in the world, ahead of Opus 4.8

The numbers drawing attention come from independent evaluations. Per Artificial Analysis figures cited by Moonshot in the K3 blog: on GDPval-AA v2 — a leaderboard testing models on real work tasks across 44 occupations in 9 industries — K3 scored 1,668, behind only Claude Fable 5 Max (1,760) and GPT-5.6 Sol Max (1,748), and ahead of Claude Opus 4.8 Max (1,600). On AA-Briefcase (Artificial Analysis' private benchmark for long-horizon agentic knowledge work), K3 scored 1,548 — second behind Fable 5 Max (1,583), ahead of GPT-5.6 Sol Max (1,495).

On front-end coding, Arena.ai's Code Arena leaderboard ranks K3 above Fable 5 — a 17-place jump over its predecessor Kimi K2.6. Arena CEO Anastasios Angelopoulos commented on X that it may be "the single biggest release of the year." For long information-seeking tasks, Moonshot self-reports a 91.2 BrowseComp score running as a single agent — a result that uses a context-compaction strategy triggered at 300K tokens (as applied in the Claude model cards); evaluated with the full 1M-token window and no context management, the score is 90.4. Both are company-reported figures awaiting independent verification.

Moonshot itself concedes in its blog that K3's overall performance still trails the two strongest proprietary models, Claude Fable 5 and GPT-5.6 Sol — while delivering frontier-level results and outperforming the other models it tested. The context matters: Fable 5 shipped only last month, and GPT-5.6 debuted a week before K3 — the open-vs-closed gap is now measured in weeks, not quarters.

Pricing: a third of the cost, full 1M-token window at flat rates

Kimi K3 is priced at $3 per 1 million input tokens ($0.30 on cache hits) and $15 per 1 million output tokens (reasoning tokens included) — and those rates apply across the entire 1,048,576-token window, with no long-context surcharge. For comparison, here are the official Anthropic and OpenAI price lists (standard rates, excluding batch discounts):

Quick math: K3's input price is 30% of Fable 5's and 60% of Opus 4.8 / GPT-5.6 Sol's; its output price is 30% of Fable 5's and 50% of GPT-5.6 Sol's. For reasoning-heavy workloads with long outputs, the cost gap compounds fast.

What can K3 do? Vision-in-the-loop and long-horizon coding

Moonshot positions K3 for extended engineering sessions: reading large codebases, orchestrating terminal tools, and correcting course on its own when an approach fails. The highlighted differentiator is "vision-in-the-loop" — the model screenshots its rendered output, looks at it, then goes back to fix the code, iterating until it works. Published demos include a browser-based 3D open-world game (Three.js + WebGPU), a simulation of the Long March 10 rocket's launch and return, and a Game Boy Advance emulator. In another case study, K3 ran autonomously for 48 hours to design and verify a chip for a nano model on the Nangate 45nm library using open-source EDA tools.

A second "DeepSeek moment"?

The last time a Chinese lab shipped a cheap model that matched closed U.S. models was DeepSeek R1 in January 2025 — an event that wiped roughly $1 trillion off leading U.S. tech stocks. Former White House AI policy adviser Sriram Krishnan called K3's debut a big moment with industry-wide implications. On the flip side, SiliconANGLE notes Anthropic previously accused Moonshot (along with DeepSeek and MiniMax) of violating its terms by distilling Claude's capabilities to train their own models — expect the debates around distillation and chip export controls to reignite.

As for Moonshot itself: per the Financial Times (via TechCrunch), the company is raising new capital at a $31.5 billion valuation — up sharply from the $2 billion round at a $20 billion valuation it closed in May 2026.

What it means for Vietnamese businesses

Kimi K3 reinforces the trend we analyzed in our DeepSeek V4 and AI sovereignty pieces: open models keep closing in on closed ones, at a fraction of the price. Three practical takeaways:

• Use the API today: for workloads without sensitive data, $3/$15 with a 1M-token context is highly competitive for long-document processing and large codebases.
• Wait for the July 27 weights to self-host: once the weights are open, businesses can run K3 on their own infrastructure — data never leaves the organization. Caveat: 2.8 trillion parameters is a very large model; self-hosting requires a serious GPU cluster, so it suits organizations with existing infrastructure or private cloud. Our recommended framework remains data classification — routine tasks via API, core data on mid-sized open models on-premise — as laid out in our internal AI roadmap.
• Read the license before commercial use: "open-weight" does not automatically mean anything-goes — the license terms will be clear when the weights ship on July 27, alongside the technical report.

Frequently asked questions

Note: This article aggregates public sources as of July 17, 2026; benchmark and pricing figures are as published by Moonshot, Anthropic, OpenAI or the cited sources at the time of writing and may change. For reference only — not technical or investment advice.