GLM-5.3-Flash vs GPT-6 Luna vs Qwen3.8 27B

It is close. Our weighted score puts them within 1 points (GLM-5.3-Flash 79/100, GPT-6 Luna 78/100, Qwen3.8 27B 57/100), so choose by what matters most for your work: GPT-6 Luna on price and GPT-6 Luna for long inputs. 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). GLM-5.3-Flash costs $0.15 input / $0.50 output per million tokens (official Z.AI API price); 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 $0.237 for GLM-5.3-Flash (1.2× as much) and $0.925 for Qwen3.8 27B (4.6× as much).

There is no independent benchmark that covers all three models yet. GLM-5.3-Flash has an ECI of 151.9, GPT-6 Luna has not been scored yet and Qwen3.8 27B has an ECI of 149.4.

There are no published SWE-bench Verified results for GLM-5.3-Flash, GPT-6 Luna 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 has the largest context window at 1,050,000 tokens, against 1,000,000 for GLM-5.3-Flash and 262,144 for Qwen3.8 27B. Maximum output per response: GLM-5.3-Flash up to 131,072, GPT-6 Luna up to 128,000, Qwen3.8 27B up to 32,768 tokens.

GLM-5.3-Flash accepts text, images, PDFs and video; GPT-6 Luna accepts text, images and PDFs; Qwen3.8 27B accepts text, images and video. GLM-5.3-Flash handles the widest range of inputs.

GLM-5.3-Flash and Qwen3.8 27B publishes its weights and can be self-hosted; GPT-6 Luna is proprietary.

GPT-6 Luna is the newest, released Sep 22, 2026. GLM-5.3-Flash came out Aug 26, 2026; Qwen3.8 27B came out Aug 14, 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.