GLM-5-Turbo vs Grok 4.20 (Non-Reasoning) vs Ministral 3 14B
Ministral 3 14B comes out ahead, 63 to 53 and 38 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5-Turbo
38/100- ECI—
- Price$1.20 / $4.00
- Context200K
xAIGrok 4.20 (Non-Reasoning)
53/100- ECI—
- Price$1.25 / $2.50
- Context1M
- Our pick
Mistral AIMinistral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
Ministral 3 14B is our pick
Ministral 3 14B is the better all-round choice, scoring 63/100 against Grok 4.20 (Non-Reasoning) (53) and GLM-5-Turbo (38). It leads on price. Grok 4.20 (Non-Reasoning) wins 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 priceMinistral 3 14BMinistral 3 14B $0.282 · Grok 4.20 (Non-Reasoning) $1.56 · GLM-5-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextGrok 4.20 (Non-Reasoning)Grok 4.20 (Non-Reasoning) 1,000,000 · Ministral 3 14B 262,144 · GLM-5-Turbo 200,000 tokens
- Widest inputsGrok 4.20 (Non-Reasoning)GLM-5-Turbo: Text · Grok 4.20 (Non-Reasoning): Text, Images, PDFs · Ministral 3 14B: Text, Images
- Self-hostingMinistral 3 14BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | GLM-5-Turbo | Grok 4.20 (Non-Reasoning) | Ministral 3 14B |
|---|---|---|---|---|
| Price | 50% | 37 | 41 | 76 |
| Inputs & features | 30% | 45 | 70 | 60 |
| Context window | 20% | 32 | 60 | 37 |
| Overall | 100% | 38/100 | 53/100 | 63/100 |
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-5-TurboZ.ai (Zhipu) | Grok 4.20 (Non-Reasoning)xAI | Ministral 3 14BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.20 | $1.25 | $0.268 (best) |
| Output | $4.00 | $2.50 | $0.325 (best) |
| Cached input | $0.24 | $0.20 (best) | — |
| Blended (3:1) | $1.90 | $1.56 | $0.282 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $5.00 | Same rate |
| Price source | Official Z.AI API | Official xAI API | Median of 2 providers |
| Limits | |||
| Context window | 200,000 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 30,000 tokens | 262,144 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | OpenApache 2.0 |
| API model ID | glm-5-turbo | grok-4.20-0309-non-reasoning | — |
| API providers | 17 (best) | 10 | 2 |
| Released | Mar 16, 2026 | Mar 9, 2026 | Dec 2, 2025 |
| Knowledge cutoff | — | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5-Turbo$20.00
- Grok 4.20 (Non-Reasoning)$17.50
- Ministral 3 14B$3.33
Which should you choose?
Which is better: GLM-5-Turbo, Grok 4.20 (Non-Reasoning) or Ministral 3 14B?
Ministral 3 14B is the better all-round choice, scoring 63/100 against Grok 4.20 (Non-Reasoning) (53) and GLM-5-Turbo (38). It leads on price. Grok 4.20 (Non-Reasoning) wins 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-5-Turbo, Grok 4.20 (Non-Reasoning) or Ministral 3 14B?
Ministral 3 14B is cheaper at $0.268 input / $0.325 output per million tokens (median across 2 API providers). Grok 4.20 (Non-Reasoning) costs $1.25 input / $2.50 output per million tokens (official xAI API price); GLM-5-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.282 per million tokens for Ministral 3 14B versus $1.56 for Grok 4.20 (Non-Reasoning) (5.5× as much) and $1.90 for GLM-5-Turbo (6.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5-Turbo has not been scored yet, Grok 4.20 (Non-Reasoning) has not been scored yet and Ministral 3 14B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo, Grok 4.20 (Non-Reasoning) and Ministral 3 14B 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?
Grok 4.20 (Non-Reasoning) has the largest context window at 1,000,000 tokens, against 262,144 for Ministral 3 14B and 200,000 for GLM-5-Turbo. Maximum output per response: GLM-5-Turbo up to 131,072, Grok 4.20 (Non-Reasoning) up to 30,000, Ministral 3 14B up to 262,144 tokens.
Which can read images, PDFs, audio or video?
GLM-5-Turbo accepts text; Grok 4.20 (Non-Reasoning) accepts text, images and PDFs; Ministral 3 14B accepts text and images. Grok 4.20 (Non-Reasoning) handles the widest range of inputs.
Are any of these open source?
Ministral 3 14B publishes its weights (Apache 2.0) and can be self-hosted; GLM-5-Turbo and Grok 4.20 (Non-Reasoning) is proprietary.
Which is newer?
GLM-5-Turbo is the newest, released Mar 16, 2026. Grok 4.20 (Non-Reasoning) came out Mar 9, 2026; Ministral 3 14B came out Dec 2, 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.
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