Thinking Machines has released Inkling, the new leading U.S. open weights model

Thinking Machines has released Inkling, the new leading U.S. open weights model, debuting at 41 on the Artificial Analysis Intelligence Index

Thinking Machines has previously released research previews of models and this is their first production language model release. The model is 975B total parameters, has 41B active parameters, and accepts text, image, and audio input modalities. The model is accessible via Thinking Machines’ Tinker platform API (256K context window) and weights are available on HuggingFace (1M context window).

Key results:

➤ Inkling debuts at 41 on the Artificial Analysis Intelligence Index, making it the leading open weights release from a U.S. lab. Inkling scores 3 points higher on the Intelligence Index (41) than the previous leading U.S. open weights model, Nemotron 3 Ultra (38), and also beats Gemma 4 31B (29) and gpt-oss-120b (24)

➤ Inkling stands out on agentic performance. It scores higher than both Kimi K2.6 and DeepSeek v4 Flash on both GDPval-AA v2 and 𝜏³-Banking: Inkling scores an Elo of 1238 on GDPval-AA v2, higher than Kimi K2.6 (1190) and DeepSeek v4 Flash max (1189) and scores 24% on 𝜏³-Banking, higher than Kimi K2.6 (21%) and just above DeepSeek v4 Flash max (23%)

➤ Inkling is token efficient compared to open weights leaders. Inkling averages 25K output tokens per Intelligence Index task compared to 43K, 38K and 37K by GLM-5.2 (max), Kimi K2.6 and DeepSeek v4 Pro (max) respectively

➤ Inkling natively supports image and audio multimodal inputs, a key differentiator among open weights models. Inkling accepts text, image, and audio input modalities. Images and videos are encoded via a hierarchical patch encoder and audio via discrete token encoding, with all modalities projected into a shared hidden space and processed jointly by the decoder

Additional model details:

➤ Size: 975B (41B active) parameters

➤ Input modalities: Text, image, and audio (text output)

➤ Context window: 256K tokens on Tinker, open weights model supports 1M

➤ Pricing per 1M tokens (64K context window): $1.87 input / $0.374 cached / $4.68 output

➤ Pricing per 1M tokens (256K context window): $3.74 input / $0.748 cached / $9.36 output

Inkling scores an Elo of 1238 on GDPval-AA v2, higher than Kimi K2.6 (1190) and DeepSeek v4 Flash max (1189)

Inkling is token efficient compared to open weights leaders, averaging 25K output tokens per Intelligence Index task compared to 43K, 38K and 37K by GLM-5.2 (max), Kimi K2.6 and DeepSeek v4 Pro (max) respectively

Inkling scores +2 on AA-Omniscience, below leading open weights models but above other U.S. open weights models, with the next best Nemotron 3 Ultra (-1). Inkling scores 40% on Accuracy but 63% on the Hallucination Rate

Full breakdown of Inkling's performance:

See Artificial Analysis for further details and benchmarks:

https://artificialanalysis.ai/models/inkling