Kimi's open model K3 nears GPT-5.6 Sol and Fable 5 while signaling the end of super cheap Chinese AI

Kimi's open model K3 nears GPT-5.6 Sol and Fable 5 while signaling the end of super cheap Chinese AI

Key Points

- Kimi has released K3, a multimodal open-weight model built on a mixture-of-experts architecture with 896 experts, 2.8 trillion parameters, and a context window of one million tokens. Full weights are expected by the end of July.

- In Kimi's own benchmarks, K3 comes close to Claude Fable 5 and GPT 5.6 Sol but beats all other tested systems by a wide margin. Independent testing by Artificial Analysis largely confirms these results, though K3's hallucination rate increased compared to its predecessor.

- At $3 per million input tokens and $15 per million output tokens, K3 is much pricier than its predecessor but comparable to Western mid-range models like Sonnet 5. Per-task costs land around $0.94, similar to GPT-5.6 Sol and about half the price of Opus 4.8.

Kimi is launching K3, a multimodal model with 2.8 trillion parameters and a context window of one million tokens. In the company's own benchmarks, it performs on par with leading proprietary models.

According to Kimi, the new flagship model K3 has 2.8 trillion total parameters, processes images and video natively, and supports a context window of one million tokens. Kimi calls K3 the first open model in the roughly 3 trillion parameter range. Full model weights are scheduled for release by July 27. The model targets long-running programming tasks, knowledge work, and complex reasoning.

In Kimi's own benchmarks, K3 still trails the top proprietary models Claude Fable 5 and GPT 5.6 Sol but beats every other system tested, including the Claude Opus models and Chinese rival GLM-5.2. All results come from Kimi and were achieved at maximum or high thinking intensity, according to the company.

Across all 35 tests, K3 took first place about seven times and landed second or third in most of the rest. Fable 5 won the most individual tests. In nearly every benchmark, K3 beat Opus 4.8, GPT 5.5, and GLM 5.2 by a wide margin. Depending on the benchmark, one of three agent systems was used: KimiCode, Claude Code, or Codex. That means the results weren't all collected under identical conditions.

Artificial Analysis confirms K3's strong performance but flags higher hallucination rate

Independent testing lab Artificial Analysis has published its first evaluation of Kimi K3. The model scores 57 on the Artificial Analysis Intelligence Index, putting it on par with Opus 4.8 and GPT-5.5 but still behind Fable 5 and GPT-5.6 Sol. That largely lines up with Kimi's own claims.

On agentic tasks, K3 reaches an Elo rating of 1,668 on GDPval v2, a big jump from K2.6's 1,190. It beats GLM-5.2 (1,514), GPT-5.5 (1,494), and Claude Opus 4.8 (1,600), though it still falls short of Claude Fable 5 (1,760). K3 also takes the top spot on AutomationBench-AA, Artificial Analysis's version of Zapier's agentic SaaS workflow evaluation, with a score of 53 percent.

On AA-Briefcase, a private long-horizon knowledge work evaluation, K3 reaches an overall Elo of 1,547, up 732 points from K2.6. Only Claude Fable 5 scores higher. Artificial Analysis calls K3 well-rounded, with rubric scoring and analytical quality close to Fable 5's level. GPT-5.6 Sol still leads on presentation quality, though.

K3's accuracy rate improved from 33 percent to 46 percent on the AA-Omniscience Index, pushing the overall score from +6 to +18. But its hallucination rate climbed from 39 percent to 51 percent, meaning K3 fabricates more answers even as it gets more questions right.

K3 targets full development projects beyond code snippets

According to Kimi, the model's primary use case is long-running software development with minimal human oversight. K3 is built to analyze large codebases, coordinate terminal tools, and stay focused on a task across many work steps.

The model pairs programming with visual feedback: it examines screen captures, modifies code, then checks the visible output. Kimi calls this closed-loop system "Vision in the Loop" and positions it as a foundation for game development, UI design, and CAD.

As demos, Kimi shows off a procedurally generated 3D open-world game that K3 reportedly built entirely in the browser using Three.js, WebGPU, and GPU Compute, along with an interactive black hole visualization. For the open-world demo, K3 procedurally generated the environment and used an external tool to create the 3D rider and horse models. Other demos include a simulation of the Long March 10 rocket launch and return, plus a Game Boy Advance emulator.

K3 uses a mixture-of-experts architecture that activates only 16 of 896 experts at a time. It's paired with a new attention architecture called Kimi Delta Attention, which Kimi says enables up to 6.3x faster decoding for million-token contexts. Addtionally, "attention residuals" reportedly boost training efficiency by about 25 percent while adding less than 2 percent in extra compute overhead.

Chinese providers are raising prices for frontier models too

According to the Kimi API docs, one million input tokens cost $0.30 with a cache hit and $3.00 without. One million output tokens, including reasoning, cost $15.00. These prices apply regardless of context length. Caching happens automatically, which makes unmodified long prefixes especially useful for agents and large codebases.

That puts K3 well above the price level of its predecessor K2.6, which officially costs $0.16 per million tokens with a cache hit, $0.95 without, and $4.00 for output. Chinese providers aren't offering their frontier models at rock-bottom prices anymore either.

Still, K3 is much cheaper than the top Western models and sits more in the upper midrange. Anthropic's new Sonnet 5, for example, also costs $3 per million input tokens and $15 for output but delivers lower performance, at least according to Kimi's benchmarks.

According to Artificial Analysis, K3 averages $0.94 per task on the Intelligence Index, close to GPT-5.6 Sol at $1.04 and about half the price of Opus 4.8 at $1.80. It's well above open-weight peers like GLM-5.2 ($0.32) and DeepSeek V4 Pro ($0.04), though.

K3 also uses fewer tokens than its predecessor, needing about 132 million output tokens to complete all nine evaluations, down from roughly 166 million for K2.6, a 21 percent reduction while scoring 13 points higher. Independent benchmark results still only partly reflect real-world performance, but the per-task cost data gives a more concrete picture than token prices alone.

Availability

K3 is already available through Kimi.com, the mobile app for iOS, Android, and HarmonyOS, the Kimi Work desktop client (version 3.1.0 and later), and Kimi Code. On OpenRouter, the model is listed under the identifier "moonshotai/kimi-k3," though it's currently served there only through Moonshot itself.

For businesses, Kimi offers a separate version with member management and the ability to split personal and business accounts. A planned platform called Kimi Hosted Agent will provide isolated environments and runtimes for long-running tasks. Interested users can sign up for the waitlist now.

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