Release12d ago
Moonshot launches Kimi K3.1 Pro: 2.8T-total / 104B-active open-weight MoE with 1M context, 65.7% on Terminal-Bench 4.0, API at $2/$10, weights by Nov 4
On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.
Release9d ago
OpenAI launches GPT-6.1 Terra: fast, low-cost computer use at $1/$5 per million tokens, half the price of GPT-5.6 Terra and GPT-6.1 Sol
On Oct 26 OpenAI released GPT-6.1 Terra, an efficient model for visual reasoning, precise computer use and natural writing. It is rolling out today to Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and in the API as gpt-6.1-terra. Standard API pricing is $1 input, $0.05 cached input and $5 output per million tokens, which OpenAI says is 50% lower than GPT-5.6 Terra and GPT-6.1 Sol, with a 1,050,000-token context window. By OpenAI's figures it scores 15 points higher than GPT-5.6 Terra on Agents' Last Exam at medium effort for about a third of the cost, and solves 50.3% of Terminal-Bench 4.0 at max effort.
Coding20d ago
PreciseCoder: Augmenting Unit-Test RL with Localized Semantic Rewards Cuts Code Edits from 7–15 Lines to 3 and Boosts GPT-5.6 Sol Precision
While reinforcement learning from unit tests raises code pass rates, researchers discover it inadvertently teaches models to over-edit and hallucinate bugs in already-correct code. PreciseCoder augments unit-test rewards with a semantically verified localized edit signal. Across 4B, 9B, and 27B models, PreciseCoder slashes average edit lines from 7–15 down to 3 at equal pass rates, generalizes from single-line training to repository-level repair, and transfers 9B reasoning traces to boost GPT-5.6 Sol and Qwen3.8-Max edit precision by 4.6–12.4 points without retraining.
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