Moonshot AI has just dropped Kimi K3, and it’s a monster. At 2.8 trillion parameters, it’s the world’s first open-source model to break into the 3-trillion-parameter class. Released July 16, 2026, Kimi K3 pushes the boundaries of what open-weight models can do in coding, knowledge work, and long-horizon agentic workflows. Full open-weight release follows by July 27.
The Scale: 2.8 Trillion Parameters
Let that number sink in. Kimi K3 has 2.8 trillion total parameters, making it the largest open-source model available. For context, the previous Kimi K2 series sat at around 1 trillion. Moonshot AI has been on a relentless scale push — in 9 of the past 12 months, Kimi models have held the frontier in open-source model scale.
But raw scale alone doesn’t tell the story. The architectural innovations are what make 2.8T parameters actually usable.
Architecture: KDA, Attention Residuals, and Stable LatentMoE
Kimi K3 introduces three key architectural innovations that together deliver roughly 2.5x the overall scaling efficiency of K2:
- Kimi Delta Attention (KDA) — A hybrid linear attention mechanism that helps information flow more smoothly through longer sequences and deeper models. This is the core efficiency enabler that makes the massive parameter count tractable.
- Attention Residuals (AttnRes) — A complementary technique that preserves information across deep network layers, reducing the degradation that typically plague very large models.
- Stable LatentMoE — An extreme sparsity framework where only 16 out of 896 experts are activated per token. This means that despite the 2.8T total parameter count, the active parameter footprint per forward pass is dramatically smaller, keeping inference costs reasonable.
Benchmarks: Top-Tier Across the Board
According to Artificial Analysis benchmarks aggregated on OpenRouter, Kimi K3 lands in the top percentiles across multiple categories:
| Benchmark | Score | Percentile |
|---|---|---|
| Intelligence Index | 57.1 | Top 3% of all models |
| Coding Index | 76.2 | Top 5% of all models |
| Agentic Index | 50.1 | Top 3% of all models |
| GPQA Diamond (graduate-level science) | 93.5% | — |
| HLE (Humanity’s Last Exam) | 44.3% | — |
| AA-LCR (long context reasoning) | 74.7% | — |
| GDPval-AA (economically valuable tasks) | 58.4% | — |
| SciCode (scientific Python) | 58.7% | — |
The 93.5% on GPQA Diamond is particularly notable — that’s graduate-level scientific reasoning, and it puts Kimi K3 in elite territory alongside closed models from OpenAI and Anthropic. The Coding Index of 76.2 confirms this is a serious coding model, not just a chatbot that happens to write code.
1M Token Context with Automatic Caching
Kimi K3 supports a 1,048,576-token context window — the full million. This is critical for the agentic workflows the model is designed for: navigating large code repositories, processing extensive documentation, and maintaining context across multi-step tool interactions.
Context caching is automatic. No cache IDs, no TTLs, no extra parameters. Just keep the long prefix unchanged across requests, and Moonshot’s infrastructure handles the rest. On OpenRouter, this is already delivering a 75.1% cache hit rate, bringing the effective input price down from $3.00 to $0.973 per million tokens.
Pricing and Performance
| Metric | Value |
|---|---|
| Input price | $3.00 / 1M tokens |
| Output price | $15.00 / 1M tokens |
| Cache read | $0.30 / 1M tokens |
| Effective input (with caching) | ~$0.97 / 1M tokens |
| Throughput | 18 tokens/sec |
| Latency (p50) | 9.50 seconds |
| Provider uptime (30d) | 99.79% |
At $3/$15 per million tokens, Kimi K3 is priced competitively for a frontier-class model. The aggressive caching discount makes it even more attractive for agentic use cases where the system prompt and tool definitions are repeated across calls.
Native Multimodal and Agentic Capabilities
Kimi K3 has native visual understanding. It accepts images via base64 encoding (public URLs are not supported for vision input) and can process video files. The model is particularly strong at tasks that combine software engineering with visual reasoning — using screenshots and visual feedback to improve workflows in game development, frontend engineering, and CAD.
The API is fully OpenAI-compatible. Here’s a basic call:
from openai import OpenAI
client = OpenAI(
api_key="your-moonshot-api-key",
base_url="https://api.moonshot.ai/v1",
)
completion = client.chat.completions.create(
model="kimi-k3",
messages=[{"role": "user", "content": "Explain quantum entanglement."}],
reasoning_effort="max",
)
print(completion.choices[0].message.content)Thinking Mode and Reasoning Effort
Kimi K3 always has thinking mode enabled. Unlike K2.x where thinking was optional, K3 reasons on every request. Currently, the only supported reasoning effort level is max, with more levels promised soon. The thinking process is exposed via a separate reasoning_content field in streaming responses, so you can observe the chain-of-thought in real time.
Important constraint: temperature, top_p, n, presence_penalty, and frequency_penalty are all fixed and should be omitted from requests. This is a deliberate design choice — the model is tuned for its default sampling parameters.
Developer Experience
Moonshot has clearly invested in developer ergonomics. The model supports:
- Tool calls with
tool_choicecontrol (use"required"to force tool use on the first turn) - Dynamic tool loading — inject tool definitions into system messages without pre-registering them
- Structured output with strict JSON Schema enforcement
- Partial mode — continue generation from a text prefix
- Official tools via the Formula integration layer (note: web search is being updated and not recommended yet)
- Streaming with separate
reasoning_contentand answer deltas
Integration guides are already available for popular coding agents including Claude Code, Codex, OpenCode, and Hermes Agent. The model is also accessible through OpenRouter at moonshotai/kimi-k3.
The Bigger Picture
Kimi K3 represents a significant milestone for open-source AI. For 9 of the past 12 months, Kimi has held the scale frontier among open models — from K2’s 1T to K2.5’s 1.1T multimodal to now K3’s 2.8T. The architectural innovations (KDA, AttnRes, Stable LatentMoE with 16/896 expert activation) show that scaling is not just about throwing more parameters at the problem — it’s about making those parameters efficient.
The 2.5x scaling efficiency improvement over K2 is the number to watch. If Moonshot can sustain that trajectory, we’re looking at a model family that converts compute into capability more effectively than any other open-source line. The full model weights arrive by July 27, 2026, along with a technical report detailing the architecture, training methodology, and evaluation.
For developers building agentic systems, coding assistants, or knowledge-work pipelines, Kimi K3 is now a serious contender. The combination of 1M context, native multimodal, always-on reasoning, and aggressive caching pricing makes it particularly well-suited for production workloads where context windows are large and repeated.
The open-source frontier just moved. Again.