GLM-5.3-Flash vs Qwen3.8 Flash Next vs GPT-6 Luna
Too close to call on our weighted score (GLM-5.3-Flash 79, GPT-6 Luna 78, Qwen3.8 Flash Next 70). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5.3-Flash
79/100- ECI151.9
- Price$0.15 / $0.50
- Context1M
Alibaba (Qwen)Qwen3.8 Flash Next
70/100- ECI—
- Price$0.20 / $0.50
- Context262K
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-5.3-Flash 79/100, GPT-6 Luna 78/100, Qwen3.8 Flash Next 70/100), 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.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · GLM-5.3-Flash $0.237 · Qwen3.8 Flash Next $0.275 per 1M tokens (3:1 blend)
- Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · GLM-5.3-Flash 1,000,000 · Qwen3.8 Flash Next 262,144 tokens
- Widest inputsGLM-5.3-FlashGLM-5.3-Flash: Text, Images, PDFs, Video · Qwen3.8 Flash Next: Text, Images, Video · GPT-6 Luna: Text, Images, PDFs
- Self-hostingGLM-5.3-Flash and Qwen3.8 Flash NextPublishes downloadable weights (qwen-community-1.0)
| Measure | Weight | GLM-5.3-Flash | Qwen3.8 Flash Next | GPT-6 Luna |
|---|---|---|---|---|
| Price | 50% | 79 | 76 | 83 |
| Inputs & features | 30% | 90 | 80 | 80 |
| Context window | 20% | 60 | 37 | 61 |
| Overall | 100% | 79/100 | 70/100 | 78/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3-FlashZ.ai (Zhipu) | Qwen3.8 Flash NextAlibaba (Qwen) | GPT-6 LunaOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 151.9 | — | — |
| ECI rank | #42 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 90.2% | — | 90.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 55.8% | — | 79.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 93.9% | — | 98.9% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 41.4% |
| Price per million tokens | |||
| Input | $0.15 | $0.20 | $0.10 (best) |
| Output | $0.50 | $0.50 | $0.50 |
| Cached input | $0.03 | — | $0.01 (best) |
| Blended (3:1) | $0.237 | $0.275 | $0.20 (best) |
| Long-context rate | Same rate | Same rate | Over 272K: $0.20 / $0.75 |
| Price source | Official Z.AI API | Median of 5 providers | Official OpenAI API |
| Limits | |||
| Context window | 1,000,000 tokens | 262,144 tokens | 1,050,000 tokens (best) |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | No |
| Video | Yes | Yes | No |
| Reasoning | Yeslow · high · max | Yes | Yeslow · medium · high · xhigh · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Openqwen-community-1.0 | Proprietary |
| API model ID | glm-5.3-flash | — | gpt-6-luna |
| API providers | 65 (best) | 5 | 24 |
| Released | Aug 26, 2026 | Aug 27, 2026 | Sep 22, 2026 |
| Knowledge cutoff | — | — | May 18, 2026 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.3-Flash$2.50
- Qwen3.8 Flash Next$3.00
- GPT-6 Luna$2.00
Which should you choose?
Which is better: GLM-5.3-Flash, Qwen3.8 Flash Next or GPT-6 Luna?
It is close. Our weighted score puts them within 1 points (GLM-5.3-Flash 79/100, GPT-6 Luna 78/100, Qwen3.8 Flash Next 70/100), 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.
Which is cheaper, GLM-5.3-Flash, Qwen3.8 Flash Next or GPT-6 Luna?
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 Flash Next costs $0.20 input / $0.50 output per million tokens (median across 5 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 $0.275 for Qwen3.8 Flash Next (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5.3-Flash has an ECI of 151.9, Qwen3.8 Flash Next has not been scored yet and GPT-6 Luna has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3-Flash, Qwen3.8 Flash Next and GPT-6 Luna 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.
Which has the bigger context window?
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 Flash Next. Maximum output per response: GLM-5.3-Flash up to 131,072, Qwen3.8 Flash Next up to 131,072, GPT-6 Luna up to 128,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3-Flash accepts text, images, PDFs and video; Qwen3.8 Flash Next accepts text, images and video; GPT-6 Luna accepts text, images and PDFs. GLM-5.3-Flash handles the widest range of inputs.
Are any of these open source?
GLM-5.3-Flash and Qwen3.8 Flash Next publishes its weights (qwen-community-1.0) and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. Qwen3.8 Flash Next came out Aug 27, 2026; GLM-5.3-Flash came out Aug 26, 2026. Knowledge cutoff: GPT-6 Luna May 18, 2026.
How do you decide the winner?
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.
