Thinking Machines Inkling: What Developers Need to Know

Inkling is the first open-weights model from Thinking Machines Lab, the AI company founded by Mira Murati. It is a 975 billion parameter MoE model that activates 41 billion parameters per request. Released under Apache 2.0 in July 2026, it competes with frontier proprietary models on reasoning benchmarks while remaining fully open for self-hosting and fine-tuning.

What Makes Inkling Different

Most open-weight models released in 2026 are optimized for a single benchmark category. Inkling takes a different approach. It aims for broad competence across text, code, multimodal, and audio tasks rather than narrow leaderboard dominance.

The architecture uses a Mixture-of-Experts design. Out of 975 billion total parameters, only 41 billion activate for any given input. This keeps inference costs manageable while maintaining the knowledge capacity of a much larger model.

Controllable Reasoning Effort

Inkling includes a feature that lets you specify how much "thinking" the model should do before answering. You can set this per request, trading response speed for reasoning depth. For simple lookups, use low effort. For complex math or multi-step analysis, use high effort. This is similar to the "thinking budget" concept seen in other reasoning models but available in an open-weights package.

Benchmark Performance

Inkling's benchmark results place it as the top-performing US-origin open-weight model as of July 2026:

On Reddit's r/LocalLLaMA community, Inkling is recognized as the leading US open-weight model, closing the gap with Chinese open-weight alternatives that dominated earlier in 2026.

What Open-Weights Means for Your AI Strategy

Open weights give you three options that closed-source models do not:

• Self-hosting: Run Inkling on your own hardware for complete data control. No API calls leave your infrastructure.
• Fine-tuning: Adapt the model to your specific domain (medical, legal, financial) without sending training data to a third party.
• Provider diversification: Access Inkling through multiple hosting providers, avoiding single-vendor dependency.

For teams already running a multi-provider AI (API) strategy, Inkling adds a new open-weight option that reduces reliance on any single proprietary model vendor. If a provider raises prices or changes terms, you can shift Inkling workloads to self-hosted infrastructure.

Accessing Inkling Through Eden AI

Eden AI's unified endpoint lets you access Inkling alongside proprietary models through the same API (Application Programming Interface, a way for programs to communicate). This means you can compare Inkling's output against Claude, GPT, or Gemini without changing your integration code.

Here is an example of calling Inkling through Eden AI:

import requests
import os

headers = {
    "Authorization": "Bearer " + os.environ["EDENAI_API_KEY"],
    "Content-Type": "application/json"
}

payload = {
    "model": "thinkingmachines/inkling",
    "messages": [
        {
            "role": "user",
            "content": "Explain the key differences between MoE and dense transformer architectures."
        }
    ],
    "max_tokens": 500
}

response = requests.post(
    "https://api.edenai.run/v3/chat/completions",
    headers=headers,
    json=payload
)

print(response.json()["choices"][0]["message"]["content"])

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You can add fallback providers to ensure reliability:

payload_with_fallbacks = {
    "model": "thinkingmachines/inkling",
    "fallbacks": ["anthropic/claude-sonnet-5", "google/gemini-2.5-flash"],
    "messages": [
        {
            "role": "user",
            "content": "Your task here"
        }
    ]
}

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Inkling vs Proprietary Models: When to Use Each

• Use Inkling when you need data sovereignty, want to fine-tune for a specific domain, or need to avoid per-token API costs at high volume.
• Use proprietary models when you need the absolute highest benchmark scores and are willing to pay per token. GPT-5.5 and Gemini 2.5 Pro still lead on some tasks.
• Use both when you want a fallback strategy. Route routine tasks to Inkling and escalate complex queries to proprietary models.

Hardware Requirements for Self-Hosting

Running Inkling's full 975B parameters requires significant GPU (Graphics Processing Unit, hardware specialized for parallel computation) resources. The model card indicates you need multiple high-end GPUs. However, because only 41B parameters activate per request, the actual inference compute is closer to what a 40B dense model requires.

For teams without the hardware to self-host, managed hosting providers and Eden AI's endpoint offer the same model without infrastructure investment.

Conclusion

Inkling from Thinking Machines Lab is a significant addition to the open-weight model category. With competitive benchmarks, Apache 2.0 licensing, and controllable reasoning effort, it gives developers a new option for multi-provider AI strategies. Eden AI provides access to Inkling alongside every major proprietary model through one unified endpoint.

FAQs - Thinking Machines' Inkling