{
  "id": 17237466,
  "title": "Kimi K3.1 Pro: A 2.8T Open-Weight Model That Nearly Matches Claude Opus 5.5 on Coding",
  "url": "https://urgent.news/2026/10/30/kimi-k3-1-pro-a-2-8t-open-weight-model-that-nearly-matches-claude",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T14:37:04.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/neuraitool/kimi-k31-pro-a-28t-open-weight-model-that-nearly-matches-claude-opus-55-on-coding-5coa"
  },
  "original_language": "en",
  "account": "Moonshot AI unveiled Kimi K3.1 Pro on October 23, a post-trained iteration of their Kimi K3 model, boasting 2.8 trillion total parameters and a 1 million token context window. This new model matches Claude Opus 5.5's performance on coding tasks at around half the API cost. However, independent verification of these claims is currently pending.\n\nKey improvements in Kimi K3.1 Pro stem from post-training enhancements rather than an increase in parameters. Moonshot AI claims the model was rebuilt with a more robust RL framework, sandboxing, and optimized rollout scheduling, leading to significant performance gains. For instance:\n- Long-horizon coding tasks showed a 61.2% success rate on Moonshot's Kimi Code Bench 3.0, compared to 55.95% for K3 and 58.2% for GPT-6 Astra.\n- Terminal-Bench 4.0 scored 65.7% for Kimi K3.1 Pro, slightly below Claude Opus 5.5's 66.4% but surpassing GPT-6 Astra's 58.2%.\n\nOther notable improvements include a substantial reduction in hallucinations (from 23.4% to 8.3%), and enhancements in finance and business operations tasks. Moonshot AI decided to remove cybersecurity data from the model through mid-training and reinforcement learning techniques, though this has resulted in a lower exploit benchmark score compared to the unfiltered checkpoint.\n\nPricing for Kimi K3.1 Pro is structured at $2 for input and $10 for output per 1 million tokens, with a discounted cached input rate of $0.20. The Highspeed variant is priced at $4 input and $20 output, claiming 2-3x faster serving compared to the standard model. Independent tests suggest Highspeed is roughly 1.6-1.75x faster, with a significant portion of completion tokens being reasoning tokens, making it particularly beneficial for interactive sessions rather than pure agent loops.\n\nDevelopers should be aware of a few caveats:\n- The reasoning effort setting is mandatory, and requests disabling it will be rejected.\n- Sampling is fixed, which could impact performance in some use cases.\n- Moonshot AI has not yet shipped the full model weights, promising delivery by November 4, 2026. The licensing details remain unspecified, so developers are advised to verify the model's availability on Hugging Face before integrating it into their infrastructure.\n\nThe implications of Kimi K3.1 Pro's release are noteworthy, as it represents a group of open-weight models closing the gap with proprietary systems in domains critical for agentic coding. With its competitive pricing, this model may currently offer the best value for high-end AI coding tasks.",
  "summary": "Kimi K3.1 Pro: A 2.8T Open-Weight Model That Nearly Matches Claude Opus 5.5 on Coding The most interesting model release this week didn't come from OpenAI or Anthropic. On October 23, Moonshot AI shipped Kimi K3.1 Pro — a post-trained version of Kimi K3 (2.8T total parameters, ~104B active per token, 1M context) that the company says lands within a point of Claude Opus 5.5 on a serious agentic…",
  "key_points": [
    "Kimi K3.1 Pro launched on October 23 with 2.8 trillion parameters and 1 million token context window",
    "Post-training enhancements, not parameter increase, boost performance on coding tasks",
    "61.2% success rate on Moonshot's Kimi Code Bench 3.0, outperforming GPT-6 Astra"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}