GLM-4.7-FlashX vs GLM-4.7-Flash vs Step 3.5 Flash
Too close to call on our weighted score (Step 3.5 Flash 62, GLM-4.7-Flash 62, GLM-4.7-FlashX 61). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
- Z.ai (Zhipu)
GLM-4.7-Flash
62/100- ECI—
- Price$0.06 / $0.40
- Context200K
StepFunStep 3.5 Flash
62/100- ECI—
- Price$0.10 / $0.30
- Context256K
Too close to call
It is close. Our weighted score puts them within a point (Step 3.5 Flash 62/100, GLM-4.7-Flash 62/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: GLM-4.7-Flash on price and Step 3.5 Flash 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 priceGLM-4.7-FlashGLM-4.7-Flash $0.145 · Step 3.5 Flash $0.15 · GLM-4.7-FlashX $0.152 per 1M tokens (3:1 blend)
- Longest contextStep 3.5 FlashStep 3.5 Flash 256,000 · GLM-4.7-FlashX 200,000 · GLM-4.7-Flash 200,000 tokens
- Widest inputsSame inputsGLM-4.7-FlashX: Text · GLM-4.7-Flash: Text · Step 3.5 Flash: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7-FlashX | GLM-4.7-Flash | Step 3.5 Flash |
|---|---|---|---|---|
| Price | 50% | 89 | 90 | 89 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 32 | 36 |
| Overall | 100% | 61/100 | 62/100 | 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) | GLM-4.7-FlashZ.ai (Zhipu) | Step 3.5 FlashStepFun |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 45.1% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 25.0% | — |
| Price per million tokens | |||
| Input | $0.07 | $0.06 (best) | $0.10 |
| Output | $0.40 | $0.40 | $0.30 (best) |
| Cached input | $0.01 (best) | — | $0.02 |
| Blended (3:1) | $0.152 | $0.145 (best) | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 13 providers | Official StepFun (Global) API |
| Limits | |||
| Context window | 200,000 tokens | 200,000 tokens | 256,000 tokens (best) |
| Max output | 131,072 tokens | 131,072 tokens | 256,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yeslow · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-4.7-flashx | glm-4.7-flash | step-3.5-flash |
| API providers | 8 | 19 (best) | 8 |
| Released | Jan 19, 2026 | Jan 19, 2026 | Jan 29, 2026 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | 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
- GLM-4.7-Flash$1.41
- Step 3.5 Flash$1.60
Which should you choose?
Which is better: GLM-4.7-FlashX, GLM-4.7-Flash or Step 3.5 Flash?
It is close. Our weighted score puts them within a point (Step 3.5 Flash 62/100, GLM-4.7-Flash 62/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: GLM-4.7-Flash on price and Step 3.5 Flash 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-4.7-FlashX, GLM-4.7-Flash or Step 3.5 Flash?
GLM-4.7-Flash is cheaper at $0.06 input / $0.40 output per million tokens (median across 13 API providers; free on Z.AI). Step 3.5 Flash costs $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). At a typical mix of three input tokens to one output token, that is $0.145 per million tokens for GLM-4.7-Flash versus $0.15 for Step 3.5 Flash (1× as much) and $0.152 for GLM-4.7-FlashX (1× 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, GLM-4.7-Flash 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, GLM-4.7-Flash 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?
Step 3.5 Flash has the largest context window at 256,000 tokens, against 200,000 for GLM-4.7-FlashX and 200,000 for GLM-4.7-Flash. Maximum output per response: GLM-4.7-FlashX up to 131,072, GLM-4.7-Flash up to 131,072, Step 3.5 Flash up to 256,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-FlashX accepts text; GLM-4.7-Flash accepts text; Step 3.5 Flash accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
Step 3.5 Flash is the newest, released Jan 29, 2026. GLM-4.7-FlashX came out Jan 19, 2026; GLM-4.7-Flash came out Jan 19, 2026. Knowledge cutoff: GLM-4.7-FlashX Apr 2025, GLM-4.7-Flash Apr 2025, 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.
