Inkling Small vs. GPT-5.6 Luna (max): A Performance and Efficiency Analysis

What the benchmarks show

When evaluating the raw performance metrics, GPT-5.6 Luna (max) consistently outperforms Inkling Small across all measured categories. With an intelligence index of 51.2 compared to Inkling Small’s 40.2, and a coding index of 71.4 against 52.9, the OpenAI model demonstrates a higher ceiling for complex problem-solving and software development tasks. This trend is mirrored in the specific benchmarks: GPT-5.6 Luna (max) achieves a GPQA score of 0.911 and an HLE score of 0.372, while Inkling Small trails at 0.895 and 0.316, respectively. Both models show similar gaps in SciCode and LCR benchmarks, suggesting that for tasks requiring deep reasoning or high-level code generation, GPT-5.6 Luna (max) is objectively more capable.

Speed and cost tradeoffs

The most significant differentiator between these two models is their operational behavior. Inkling Small, released on July 30, 2026, is built for speed, offering a time to first token of just 1.713 seconds and an output speed of 91.82 tokens per second. This makes it highly responsive for interactive applications. Conversely, GPT-5.6 Luna (max), released on July 9, 2026, exhibits a significant bottleneck in initial response time, with a time to first token of 56.349 seconds. While its raw output speed is much higher at 186.687 tokens per second, the initial delay makes it unsuitable for real-time conversational interfaces.

From a cost perspective, the models are surprisingly close. Inkling Small carries a blended cost of $0.53 per million tokens, while GPT-5.6 Luna (max) is more economical at $0.45 per million tokens. Despite the higher intelligence metrics of the OpenAI model, it maintains a lower blended price point, largely due to a lower input cost of $0.20 per million tokens compared to Inkling Small’s $0.30.

Which model fits which workflow

Selecting the right model requires balancing the need for raw intelligence against the necessity of low latency. Inkling Small is optimized for workflows where the user cannot afford to wait nearly a minute for the first token to appear. Its performance profile is ideal for chat-based assistants, real-time coding suggestions, or any application where the user experience is predicated on immediate feedback. The slightly lower intelligence scores are a trade-off for the agility the model provides.

GPT-5.6 Luna (max) is better suited for asynchronous, batch-processed tasks. Because of its significant time-to-first-token delay, it is not appropriate for interactive sessions. However, for background jobs, large-scale code refactoring, or complex research tasks where the model can be left to run, the higher intelligence and coding indices provide a distinct advantage. The fact that it is also cheaper on a blended basis makes it a highly efficient "heavy lifter" for non-real-time workloads.

Decision takeaway

Ultimately, the decision rests on whether your application prioritizes responsiveness or depth. If you are building a tool that requires human-in-the-loop interaction, the 56-second delay of GPT-5.6 Luna (max) will likely be a dealbreaker, regardless of its superior benchmark scores. For those scenarios, Inkling Small is the clear winner. If your workflow involves processing large datasets or generating complex codebases where speed is secondary to accuracy, GPT-5.6 Luna (max) offers a more capable and cost-effective engine.