Thinking Machines Releases Open-Source Multimodal LLM Inkling, 975B Parameters with Open Weights

On July 15, 2026, Thinking Machines Lab, founded by former OpenAI CTO Mira Murati, officially released the open-weight multimodal large model Inkling. The model adopts a Mixture-of-Experts (MoE) Transformer architecture with 975B total parameters and 41B activated parameters, supporting a context window of up to 1 million tokens. Its pretraining data covers 45 trillion tokens of multimodal corpora including text, images, audio, and video. A lightweight version, Inkling-Small, with 276B total parameters and 12B activated parameters, is also released, using the same training methodology to reduce inference cost and latency.

Core Capabilities and Differentiating Advantages

Inkling is positioned as a balanced general-purpose foundation model, covering logical reasoning, code agents, visual understanding, audio processing, and factual prediction across all tracks, without specialized optimization for any single benchmark. Its key differentiating advantages include:

• Native multimodal reasoning: Supports cross-modal joint reasoning across text, images, and audio. Its audio capabilities rank among the top open-source models on benchmarks such as VoiceBench and MMAU.
• Controllable inference compute scheduling: Developers can adjust inference intensity to flexibly trade off between performance and token consumption. At equivalent performance, Inkling consumes only one-third the tokens of competitor Nemotron 3 Ultra.
• Factual calibration and safety protection: Trained via large-scale asynchronous reinforcement learning, with streamlined and efficient chain-of-thought and strong predictive calibration. On the FORTRESS benchmark, it leads open-source models in refusal rate for malicious requests.

Agent and Code Capabilities

Inkling excels in agentic programming and tool use:

• Ranks in the top tier of open-source models on the Design Arena web development blind test leaderboard, capable of generating complete web applications in a single pass.
• Supports long iterative optimization; for example, under GPT Codex review, it completed full-stack development of a multiplayer online Snake game after 40 rounds of feedback iteration.
• Competitive on code benchmarks such as SWEBench and Terminal Bench.

Deployment and Fine-Tuning Ecosystem

The full weights of Inkling are available on Hugging Face, with BF16 and NVFP4 quantized versions. The model is compatible with mainstream inference frameworks such as vLLM, SGLang, and llama.cpp, and is accessible via cloud provider APIs including TogetherAI and Databricks. The accompanying Tinker fine-tuning platform supports 64K and 256K context specifications, with a limited-time 50% discount. The official demonstration shows a closed-loop autonomous fine-tuning process: Inkling can independently write fine-tuning tasks, launch training, and perform self-checking of results using Tinker.

Limitations and Safety Risks

The official release notes that Inkling shares common limitations of large models, including factual hallucinations, training data biases, knowledge cutoff, and performance degradation in long conversations. Indirect prompting may bypass native safeguards. It is not recommended for direct use in high-risk scenarios such as healthcare, law, or industrial safety; multi-layered protections including content filtering and human review should be implemented.