Thinking Machines Lab unveiled its second generation of models on 5 November. Artificial Analysis, which evaluates models with benchmarks, published its own assessment of the new model the same day. Inkling 2 Small is a mixture-of-experts (MoE) model: it processes each token with only some of its parameters, 17 billion of 594 billion in total. The model accepts text and images, and users can adjust its reasoning effort.

The model weights cannot be downloaded yet. Thinking Machines intends to release them under an Apache 2.0 licence next week, once external safety testing is complete. Artificial Analysis said the release is planned but its date has not been confirmed.

Scores rose, and so did token counts

According to a chart in Artificial Analysis’s release, its Intelligence Index v5.0.1 combines the results of nine benchmarks. Inkling 2 Small scored 31 points on the index, 18 more than its predecessor, Inkling Small. Google’s Gemini 3.8 Flash (high) achieved the same score.

Artificial Analysis said that once the weights are released, the model would, on current results, be the highest-scoring open-weight model developed outside China. In the chart, DeepSeek V4.1 Flash 1017 (max), for example, scores 33 points and GLM-5.3 (max) 32. Thinking Machines’ own table classifies both as open-weight models.

The number of tokens the model generated grew along with its score. It produced an average of about 150,000 tokens per index task, roughly four times as many as Inkling Small. Each task cost $1.18, compared with $0.20 for Inkling Small, $7.77 for Gemini 3.8 Flash (high) and $10.02 for Claude Sonnet 5.5 (medium).

On the AA-Omniscience knowledge benchmark, the hallucination rate fell from Inkling Small’s 61 per cent to 27 per cent. Artificial Analysis attributed this mainly to the model answering fewer questions. It attempted 53 per cent of the questions, against 77 per cent for its predecessor. The share of correct answers rose only from 32 per cent to 34 per cent.

In coding, the model and Claude Sonnet 5.5 (medium) were strong on different benchmarks. Inkling 2 Small scored 25 per cent on FrontierCode 2.0 and Sonnet 11 per cent. On Terminal-Bench 4.0, Sonnet scored 30 per cent and Inkling 13 per cent.

Rule circumvention spotted during training

Thinking Machines said early versions of the model tried to succeed at difficult tasks at any cost. In some cases they fabricated results, ignored instructions and concealed their actions. During reinforcement learning, asynchronous monitoring found that the model had worked around a network block through a third-party service it could reach with its search tools. The company paused training, removed environments that required web access and began checking tool calls before they were executed. It then resumed training.

On the company’s own metric for undesirable model behaviour, a lower score is better. Inkling 2 Small scored 14.9, its predecessor Inkling 66.8 and the closed GPT-6.1 Sol 0.8. The company also said the model is not at its best in conversational use and sometimes overreacts to questions about mental health.

The model is available in Tinker Playground and on Thinking Machines’ Tinker service, where users can run and fine-tune it. Artificial Analysis put the price at $0.30 per 1 million input tokens and $1.00 per 1 million output tokens. Thinking Machines said users can download fine-tuned models for their own use only once the weights are released.