Moonshot Releases Kimi K3.1 Pro: 3-Trillion-Parameter Model Beats GPT-6 Astra on Terminal-Bench 4.0
With a 1-million-token context window and open weights scheduled for November 4, Kimi K3.1 Pro closes the gap between open-weight models and Claude Opus 5.5.
Competition among frontier foundation models intensified in late October 2026 with the release of Kimi K3.1 Pro from Moonshot AI. While open-weight models were historically perceived as trailing proprietary labs by a generation in autonomous software engineering and command-line execution, Kimi K3.1 Pro’s benchmark performance challenges that assumption directly.
Built on a 3-trillion-parameter class Mixture-of-Experts (MoE) architecture with a full 1-million-token context window, Kimi K3.1 Pro is engineered specifically for agentic coding, repository-scale reasoning, and long-horizon tool orchestration.
Terminal-Bench 4.0 Results: Outperforming GPT-6 Astra
Today’s most demanding evaluation for autonomous software engineering agents is Terminal-Bench 4.0, which measures how reliably a model can complete real Linux system administration, compilation debugging, database migrations, and multi-file bug fixes inside a live terminal environment without human intervention.
Here is the official Terminal-Bench 4.0 leaderboard comparison as of October 2026:
| AI Model | Developer | Access / License | Terminal-Bench 4.0 Score |
|---|---|---|---|
| Claude Opus 5.5 | Anthropic | Proprietary API | 66.4% |
| Kimi K3.1 Pro | Moonshot AI | API (Open Weights Nov 4, 2026) | 65.7% |
| GPT-6 Astra | OpenAI | Proprietary API | 58.2% |
Scoring 65.7% places Kimi K3.1 Pro within 0.7 percentage points of category leader Claude Opus 5.5 (66.4%), while outperforming OpenAI’s GPT-6 Astra (58.2%) by a decisive 7.5-point margin.
“Kimi K3.1 Pro breaking the 65% barrier on Terminal-Bench 4.0 demonstrates that reinforcement learning inside real shell execution environments matters far more for agentic reliability than raw active parameter count alone.”
Highspeed Variant (2–3x Faster) & November 4 Open-Weight Release
One of the biggest bottlenecks when running autonomous agents across dozens of multi-turn tool calls is time-to-first-token and token generation throughput.
To address agentic latency, Moonshot simultaneously launched Kimi K3.1 Pro Highspeed, delivering 2x to 3x faster inference throughput than the standard release—making it ideal for real-time IDE assistants and automated CI/CD repair agents.
Even more significant for the open-source research community and self-hosted enterprises, Moonshot confirmed that full open weights for Kimi K3.1 Pro are scheduled for public release on November 4, 2026.
Local Inference Update: llama.cpp b11870 Adds DeepSeek-V4.1-Flash
Alongside Moonshot’s announcement, the local and on-premise inference ecosystem also shipped a notable update this week. Prerelease llama.cpp build b11870 merged native architecture support for DeepSeek-V4.1-Flash.
With GGUF quantization support for DeepSeek-V4.1-Flash now available in llama.cpp, developers can run high-speed mid-sized reasoning models directly on Apple Silicon workstations and local GPU servers with substantially lower VRAM overhead.
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