GPT-6 Luna vs Muse Spark 1.1 vs GLM-5.3-Flash
Too close to call on our weighted score (GLM-5.3-Flash 79, GPT-6 Luna 78, Muse Spark 1.1 57). The right pick depends on what you value most.
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
MetaMuse Spark 1.1
57/100- ECI154.3
- Price$1.25 / $4.25
- Context1.05M
Z.ai (Zhipu)GLM-5.3-Flash
79/100- ECI151.9
- Price$0.15 / $0.50
- Context1M
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, Muse Spark 1.1 57/100), so choose by what matters most for your work: GPT-6 Luna 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.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT-6 LunaGPT-6 Luna $0.20 · GLM-5.3-Flash $0.237 · Muse Spark 1.1 $2.00 per 1M tokens (3:1 blend)
- Longest contextGPT-6 Luna and Muse Spark 1.1GPT-6 Luna 1,050,000 · Muse Spark 1.1 1,048,576 · GLM-5.3-Flash 1,000,000 tokens
- Widest inputsMuse Spark 1.1 and GLM-5.3-FlashGPT-6 Luna: Text, Images, PDFs · Muse Spark 1.1: Text, Images, PDFs, Video · GLM-5.3-Flash: Text, Images, PDFs, Video
- Self-hostingGLM-5.3-FlashPublishes downloadable weights
| Measure | Weight | GPT-6 Luna | Muse Spark 1.1 | GLM-5.3-Flash |
|---|---|---|---|---|
| Price | 50% | 83 | 36 | 79 |
| Inputs & features | 30% | 80 | 90 | 90 |
| Context window | 20% | 61 | 61 | 60 |
| Overall | 100% | 78/100 | 57/100 | 79/100 |
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 | GPT-6 LunaOpenAI | Muse Spark 1.1Meta | GLM-5.3-FlashZ.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 154.3 (best) | 151.9 |
| ECI rank | — | #35 of 148 (best) | #42 of 148 |
| GPQA DiamondGraduate-level science questions | 90.5% (best) | — | 90.2% |
| FrontierMath Tiers 1–3Research-level mathematics | 79.0% (best) | — | 55.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | 98.9% (best) | — | 93.9% |
| SimpleQA VerifiedShort factual questions | 41.4% | 57.8% (best) | — |
| Price per million tokens | |||
| Input | $0.10 (best) | $1.25 | $0.15 |
| Output | $0.50 (best) | $4.25 | $0.50 (best) |
| Cached input | $0.01 (best) | $0.15 | $0.03 |
| Blended (3:1) | $0.20 (best) | $2.00 | $0.237 |
| Long-context rate | Over 272K: $0.20 / $0.75 | Same rate | Same rate |
| Price source | Official OpenAI API | Official Meta API | Official Z.AI API |
| Limits | |||
| Context window | 1,050,000 tokens (best) | 1,048,576 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | Yes | Yes | Yes |
| Audio | No | No | No |
| Video | No | Yes | Yes |
| Reasoning | Yeslow · medium · high · xhigh · max | Yesminimal · low · medium · high · xhigh | Yeslow · high · max |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-6-luna | muse-spark-1.1 | glm-5.3-flash |
| API providers | 24 | 13 | 65 (best) |
| Released | Sep 22, 2026 | Jul 9, 2026 | Aug 26, 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.
- GPT-6 Luna$2.00
- Muse Spark 1.1$21.00
- GLM-5.3-Flash$2.50
Which should you choose?
Which is better: GPT-6 Luna, Muse Spark 1.1 or GLM-5.3-Flash?
It is close. Our weighted score puts them within 1 points (GLM-5.3-Flash 79/100, GPT-6 Luna 78/100, Muse Spark 1.1 57/100), so choose by what matters most for your work: GPT-6 Luna 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.
Which is cheaper, GPT-6 Luna, Muse Spark 1.1 or GLM-5.3-Flash?
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); Muse Spark 1.1 costs $1.25 input / $4.25 output per million tokens (official Meta API price). 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 $2.00 for Muse Spark 1.1 (10× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-6 Luna has not been scored yet, Muse Spark 1.1 has an ECI of 154.3 and GLM-5.3-Flash has an ECI of 151.9.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-6 Luna, Muse Spark 1.1 and GLM-5.3-Flash 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 and Muse Spark 1.1 have the largest context windows (1,050,000 and 1,048,576 tokens), against 1,000,000 for GLM-5.3-Flash. Maximum output per response: GPT-6 Luna up to 128,000, Muse Spark 1.1 up to 131,072, GLM-5.3-Flash up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-6 Luna accepts text, images and PDFs; Muse Spark 1.1 accepts text, images, PDFs and video; GLM-5.3-Flash accepts text, images, PDFs and video. Muse Spark 1.1 handles the widest range of inputs.
Are any of these open source?
GLM-5.3-Flash publishes its weights and can be self-hosted; GPT-6 Luna and Muse Spark 1.1 is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. GLM-5.3-Flash came out Aug 26, 2026; Muse Spark 1.1 came out Jul 9, 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.
