GLM-4.7-FlashX vs GPT-6 Luna vs Step 3.5 Flash
GPT-6 Luna comes out ahead, 78 to 62 and 61 on our weighted score, though Step 3.5 Flash is 25% cheaper per token.
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
- Our pick
OpenAIGPT-6 Luna
78/100- ECI—
- Price$0.10 / $0.50
- Context1.05M
StepFunStep 3.5 Flash
62/100- ECI—
- Price$0.10 / $0.30
- Context256K
GPT-6 Luna is our pick
GPT-6 Luna is the better all-round choice, scoring 78/100 against Step 3.5 Flash (62) and GLM-4.7-FlashX (61). It leads on inputs & features and context window. 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 priceStep 3.5 FlashStep 3.5 Flash $0.15 · GLM-4.7-FlashX $0.152 · GPT-6 Luna $0.20 per 1M tokens (3:1 blend)
- Longest contextGPT-6 LunaGPT-6 Luna 1,050,000 · Step 3.5 Flash 256,000 · GLM-4.7-FlashX 200,000 tokens
- Widest inputsGPT-6 LunaGLM-4.7-FlashX: Text · GPT-6 Luna: Text, Images, PDFs · Step 3.5 Flash: Text
- Self-hostingGLM-4.7-FlashX and Step 3.5 FlashPublishes downloadable weights
| Measure | Weight | GLM-4.7-FlashX | GPT-6 Luna | Step 3.5 Flash |
|---|---|---|---|---|
| Price | 50% | 89 | 83 | 89 |
| Inputs & features | 30% | 35 | 80 | 35 |
| Context window | 20% | 32 | 61 | 36 |
| Overall | 100% | 61/100 | 78/100 | 62/100 |
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-4.7-FlashXZ.ai (Zhipu) | GPT-6 LunaOpenAI | Step 3.5 FlashStepFun |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 90.5% | — |
| FrontierMath Tiers 1–3Research-level mathematics | — | 79.0% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 98.9% | — |
| SimpleQA VerifiedShort factual questions | — | 41.4% | — |
| Price per million tokens | |||
| Input | $0.07 (best) | $0.10 | $0.10 |
| Output | $0.40 | $0.50 | $0.30 (best) |
| Cached input | $0.01 (best) | $0.01 (best) | $0.02 |
| Blended (3:1) | $0.152 | $0.20 | $0.15 (best) |
| Long-context rate | Same rate | Over 272K: $0.20 / $0.75 | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official StepFun (Global) API |
| Limits | |||
| Context window | 200,000 tokens | 1,050,000 tokens (best) | 256,000 tokens |
| Max output | 131,072 tokens | 128,000 tokens | 256,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yeslow · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.7-flashx | gpt-6-luna | step-3.5-flash |
| API providers | 8 | 24 (best) | 8 |
| Released | Jan 19, 2026 | Sep 22, 2026 | Jan 29, 2026 |
| Knowledge cutoff | Apr 2025 | May 18, 2026 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-4.7-FlashX$1.50
- GPT-6 Luna$2.00
- Step 3.5 Flash$1.60
Which should you choose?
Which is better: GLM-4.7-FlashX, GPT-6 Luna or Step 3.5 Flash?
GPT-6 Luna is the better all-round choice, scoring 78/100 against Step 3.5 Flash (62) and GLM-4.7-FlashX (61). It leads on inputs & features and context window. 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-4.7-FlashX, GPT-6 Luna or Step 3.5 Flash?
Step 3.5 Flash is cheaper at $0.10 input / $0.30 output per million tokens (official StepFun (Global) API price). GLM-4.7-FlashX costs $0.07 input / $0.40 output per million tokens (official Z.AI API price); GPT-6 Luna costs $0.10 input / $0.50 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $0.15 per million tokens for Step 3.5 Flash versus $0.152 for GLM-4.7-FlashX (1× as much) and $0.20 for GPT-6 Luna (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7-FlashX has not been scored yet, GPT-6 Luna has not been scored yet and Step 3.5 Flash has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7-FlashX, GPT-6 Luna and Step 3.5 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 has the largest context window at 1,050,000 tokens, against 256,000 for Step 3.5 Flash and 200,000 for GLM-4.7-FlashX. Maximum output per response: GLM-4.7-FlashX up to 131,072, GPT-6 Luna up to 128,000, Step 3.5 Flash up to 256,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-FlashX accepts text; GPT-6 Luna accepts text, images and PDFs; Step 3.5 Flash accepts text. GPT-6 Luna handles the widest range of inputs.
Are any of these open source?
GLM-4.7-FlashX and Step 3.5 Flash publishes its weights and can be self-hosted; GPT-6 Luna is proprietary.
Which is newer?
GPT-6 Luna is the newest, released Sep 22, 2026. Step 3.5 Flash came out Jan 29, 2026; GLM-4.7-FlashX came out Jan 19, 2026. Knowledge cutoff: GLM-4.7-FlashX Apr 2025, GPT-6 Luna May 18, 2026, Step 3.5 Flash Jan 2025.
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.
