GLM-4.7-FlashX vs Ministral 3 14B vs Step 3.5 Flash
Too close to call on our weighted score (Ministral 3 14B 63, Step 3.5 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
Mistral AIMinistral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
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 1 points (Ministral 3 14B 63/100, Step 3.5 Flash 62/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: Step 3.5 Flash on price and Ministral 3 14B 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 priceStep 3.5 FlashStep 3.5 Flash $0.15 · GLM-4.7-FlashX $0.152 · Ministral 3 14B $0.282 per 1M tokens (3:1 blend)
- Longest contextMinistral 3 14BMinistral 3 14B 262,144 · Step 3.5 Flash 256,000 · GLM-4.7-FlashX 200,000 tokens
- Widest inputsMinistral 3 14BGLM-4.7-FlashX: Text · Ministral 3 14B: Text, Images · 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 | Ministral 3 14B | Step 3.5 Flash |
|---|---|---|---|---|
| Price | 50% | 89 | 76 | 89 |
| Inputs & features | 30% | 35 | 60 | 35 |
| Context window | 20% | 32 | 37 | 36 |
| Overall | 100% | 61/100 | 63/100 | 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) | Ministral 3 14BMistral AI | Step 3.5 FlashStepFun |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.07 (best) | $0.268 | $0.10 |
| Output | $0.40 | $0.325 | $0.30 (best) |
| Cached input | $0.01 (best) | — | $0.02 |
| Blended (3:1) | $0.152 | $0.282 | $0.15 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 2 providers | Official StepFun (Global) API |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 256,000 tokens |
| Max output | 131,072 tokens | 262,144 tokens (best) | 256,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yeslow · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | OpenApache 2.0 | Open |
| API model ID | glm-4.7-flashx | — | step-3.5-flash |
| API providers | 8 (best) | 2 | 8 (best) |
| Released | Jan 19, 2026 | Dec 2, 2025 | Jan 29, 2026 |
| Knowledge cutoff | 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
- Ministral 3 14B$3.33
- Step 3.5 Flash$1.60
Which should you choose?
Which is better: GLM-4.7-FlashX, Ministral 3 14B or Step 3.5 Flash?
It is close. Our weighted score puts them within 1 points (Ministral 3 14B 63/100, Step 3.5 Flash 62/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: Step 3.5 Flash on price and Ministral 3 14B 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, Ministral 3 14B 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); Ministral 3 14B costs $0.268 input / $0.325 output per million tokens (median across 2 API providers). 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 $0.282 for Ministral 3 14B (1.9× 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, Ministral 3 14B 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, Ministral 3 14B 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?
Ministral 3 14B has the largest context window at 262,144 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, Ministral 3 14B up to 262,144, Step 3.5 Flash up to 256,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7-FlashX accepts text; Ministral 3 14B accepts text and images; Step 3.5 Flash accepts text. Ministral 3 14B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), 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; Ministral 3 14B came out Dec 2, 2025. Knowledge cutoff: GLM-4.7-FlashX 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.
