Iris-mini

Kolibri 1

Aleph Alpha

Kolibri 1 is Aleph Alpha’s open-weight German-English reasoning and tool-use model, trained from scratch with a German-focused tokenizer and data mixture. Its hybrid-attention mixture-of-experts Transformer has 78B total parameters and approximately 3.46B active per token. The native 262,144-token context can extrapolate to 1,048,576 tokens, but the publisher recommends remaining within the native window for complex tasks and serving efficiency. Apache-licensed weights support private and sovereign deployments. Sparse activation reduces computation without eliminating the memory needed for the full weights; the published knowledge cutoff is June 18, 2026 for both English and German.

IndustryLLM

Alibaba

IndustryLLM is Alibaba’s open-weight language model for industrial procurement and engineering-standard reasoning. It adapts Qwen3.5-35B-A3B-Base through continued pre-training on industrial documents and transactions followed by task-focused supervised fine-tuning. The 35.95B-parameter hybrid mixture-of-experts network activates approximately 3B per token. Training targets informal buyer language, ambiguous product codes, contradictory specifications, and evidence-based constraint evaluation. Apache-licensed BF16 weights are published. Its 262K architectural context is distinct from 12,288-token packed fine-tuning sequences; this record uses the supported training length conservatively. The September 25 paper predates later discovery-index coverage.

Iris-pro

AllSpark Research

Iris-pro is AllSpark Research’s open-weight search-specialized language model, published in the September 3 Iris paper. It is post-trained from Qwen3.5-397B-A17B through alternating supervised fine-tuning and reinforcement learning, learning what to search, how to read evidence, and when enough information has been collected. Its mixture-of-experts backbone has 397B total and 17B active parameters, with a 256K context. Apache-licensed weights and an evaluation harness are available. The checkpoint generates reasoning, tool calls, and final language answers; search itself is performed by external tools. Published scores depend strongly on context resets and retry policies.