Ministral 14B vs GLM-4.7-FlashX
Too close to call on our weighted score (Ministral 14B 64, GLM-4.7-FlashX 61). The right pick depends on what you value most.
Mistral AIMinistral 14B
64/100- ECI—
- Price$0.20 / $0.20
- Context262K
Z.ai (Zhipu)GLM-4.7-FlashX
61/100- ECI—
- Price$0.07 / $0.40
- Context200K
Add a model
Make it a three-way comparison.
Too close to call
It is close. Our weighted score puts them within 3 points (Ministral 14B 64/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: GLM-4.7-FlashX on price and Ministral 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 priceGLM-4.7-FlashXGLM-4.7-FlashX $0.152 · Ministral 14B $0.20 per 1M tokens (3:1 blend)
- Longest contextMinistral 14BMinistral 14B 262,144 · GLM-4.7-FlashX 200,000 tokens
- Widest inputsMinistral 14BMinistral 14B: Text, Images · GLM-4.7-FlashX: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ministral 14B | GLM-4.7-FlashX |
|---|---|---|---|
| Price | 50% | 83 | 89 |
| Inputs & features | 30% | 50 | 35 |
| Context window | 20% | 37 | 32 |
| Overall | 100% | 64/100 | 61/100 |
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 | Ministral 14BMistral AI | GLM-4.7-FlashXZ.ai (Zhipu) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $0.20 | $0.07 (best) |
| Output | $0.20 (best) | $0.40 |
| Cached input | — | $0.01 |
| Blended (3:1) | $0.20 | $0.152 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Z.AI API |
| Limits | ||
| Context window | 262,144 tokens (best) | 200,000 tokens |
| Max output | 262,144 tokens (best) | 131,072 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | No |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | OpenApache-2.0 | Open |
| API model ID | — | glm-4.7-flashx |
| API providers | 1 | 8 (best) |
| Released | Dec 2, 2025 | Jan 19, 2026 |
| Knowledge cutoff | — | Apr 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ministral 14B$2.40
- GLM-4.7-FlashX$1.50
Which should you choose?
Which is better: Ministral 14B or GLM-4.7-FlashX?
It is close. Our weighted score puts them within 3 points (Ministral 14B 64/100, GLM-4.7-FlashX 61/100), so choose by what matters most for your work: GLM-4.7-FlashX on price and Ministral 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, Ministral 14B or GLM-4.7-FlashX?
GLM-4.7-FlashX is cheaper at $0.07 input / $0.40 output per million tokens (official Z.AI API price). Ministral 14B costs $0.20 input / $0.20 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.152 per million tokens for GLM-4.7-FlashX versus $0.20 for Ministral 14B (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Ministral 14B has not been scored yet and GLM-4.7-FlashX has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 14B and GLM-4.7-FlashX yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Ministral 14B has the largest context window at 262,144 tokens, against 200,000 for GLM-4.7-FlashX. Maximum output per response: Ministral 14B up to 262,144, GLM-4.7-FlashX up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ministral 14B accepts text and images; GLM-4.7-FlashX accepts text. Ministral 14B handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
GLM-4.7-FlashX is the newest, released Jan 19, 2026. Ministral 14B came out Dec 2, 2025. Knowledge cutoff: GLM-4.7-FlashX Apr 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.
