Inkling Benchmark Results

Thinking Machines released Inkling, a 975B-parameter open weights model scoring 41 on the Artificial Analysis Intelligence Index, making it the leading U.S. open weights model. Inkling outperforms rivals on agentic benchmarks and supports text, image, and audio inputs, with weights available on HuggingFace.

All articles /articles July 15, 2026 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: Read the latest How GPT-5.6 Sol, Terra, Luna compare on intelligence vs cost GPT-5.6 Sol and Luna are ahead of Terra at every point on the Intelligence vs Cost per Task chart. GPT-5.6 Luna stands out as a particularly cost efficient model July 13, 2026 Muse Spark 1.1: Meta gains 8 Intelligence Index points in three months Meta's Muse Spark 1.1 scores 51 on the Artificial Analysis Intelligence Index and is cost and token efficient compared to its peers July 10, 2026 GPT-5.6 benchmarks across Intelligence, Speed and Cost GPT-5.6 Sol comes close second to Claude Fable 5 in the Artificial Analysis Intelligence Index at one third of the cost, and leads the Artificial Analysis Coding Agent Index in OpenAI’s Codex harness July 9, 2026