GPT-6 Luna vs Inkling Small vs Qwen3.8 27B

GPT-6 Luna is the better all-round choice, scoring 78/100 against Inkling Small (62) and Qwen3.8 27B (57). It leads on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

GPT-6 Luna is cheaper at $0.10 input / $0.50 output per million tokens (official OpenAI API price). Inkling Small costs $0.50 input / $1.20 output per million tokens (median across 11 API providers); Qwen3.8 27B costs $0.40 input / $2.50 output per million tokens (median across 39 API providers). At a typical mix of three input tokens to one output token, that is $0.20 per million tokens for GPT-6 Luna versus $0.675 for Inkling Small (3.4× as much) and $0.925 for Qwen3.8 27B (4.6× as much).

There is no independent benchmark that covers all three models yet. GPT-6 Luna has not been scored yet, Inkling Small has an ECI of 150.2 and Qwen3.8 27B has an ECI of 149.4.

There are no published SWE-bench Verified results for GPT-6 Luna, Inkling Small and Qwen3.8 27B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

GPT-6 Luna and Inkling Small have the largest context windows (1,050,000 and 1,048,576 tokens), against 262,144 for Qwen3.8 27B. Maximum output per response: GPT-6 Luna up to 128,000, Inkling Small up to 1,048,576, Qwen3.8 27B up to 32,768 tokens.

GPT-6 Luna accepts text, images and PDFs; Inkling Small accepts text, images and audio; Qwen3.8 27B accepts text, images and video. They handle the same number of input types.

Inkling Small and Qwen3.8 27B publishes its weights (Apache-2.0) and can be self-hosted; GPT-6 Luna is proprietary.

GPT-6 Luna is the newest, released Sep 22, 2026. Qwen3.8 27B came out Aug 14, 2026; Inkling Small came out Jul 30, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026.

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.