Ministral 3 14B vs Llama 3.1 Nemotron Ultra 253B vs Grok 4.1 Fast
Grok 4.1 Fast comes out ahead, 71 to 65 and 63 on our weighted score.
Mistral AIMinistral 3 14B
63/100- ECI—
- Price$0.268 / $0.325
- Context262K
NVIDIALlama 3.1 Nemotron Ultra 253B
65/100- ECI—
- PriceFree / Free
- Context128K
- Our pick
xAIGrok 4.1 Fast
71/100- ECI—
- Price$0.20 / $0.50
- Context2M
Grok 4.1 Fast is our pick
Grok 4.1 Fast is the better all-round choice, scoring 71/100 against Llama 3.1 Nemotron Ultra 253B (65) and Ministral 3 14B (63). It leads on context window. Llama 3.1 Nemotron Ultra 253B wins on price. 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 priceLlama 3.1 Nemotron Ultra 253BLlama 3.1 Nemotron Ultra 253B Free · Grok 4.1 Fast $0.275 · Ministral 3 14B $0.282 per 1M tokens (3:1 blend)
- Longest contextGrok 4.1 FastGrok 4.1 Fast 2,000,000 · Ministral 3 14B 262,144 · Llama 3.1 Nemotron Ultra 253B 128,000 tokens
- Widest inputsMinistral 3 14B and Grok 4.1 FastMinistral 3 14B: Text, Images · Llama 3.1 Nemotron Ultra 253B: Text · Grok 4.1 Fast: Text, Images
- Self-hostingMinistral 3 14B and Llama 3.1 Nemotron Ultra 253BPublishes downloadable weights (Apache 2.0)
| Measure | Weight | Ministral 3 14B | Llama 3.1 Nemotron Ultra 253B | Grok 4.1 Fast |
|---|---|---|---|---|
| Price | 50% | 76 | 100 | 76 |
| Inputs & features | 30% | 60 | 35 | 60 |
| Context window | 20% | 37 | 24 | 72 |
| Overall | 100% | 63/100 | 65/100 | 71/100 |
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 3 14BMistral AI | Llama 3.1 Nemotron Ultra 253BNVIDIA | Grok 4.1 FastxAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.268 | Free (best) | $0.20 |
| Output | $0.325 | Free (best) | $0.50 |
| Cached input | — | — | — |
| Blended (3:1) | $0.282 | Free (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Nvidia API | Median of 2 providers |
| Limits | |||
| Context window | 262,144 tokens | 128,000 tokens | 2,000,000 tokens (best) |
| Max output | 262,144 tokens (best) | 8,192 tokens | 30,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Proprietary |
| API model ID | — | nvidia/llama-3.1-nemotron-ultra-253b-v1 | — |
| API providers | 2 (best) | 1 | 2 (best) |
| Released | Dec 2, 2025 | Apr 7, 2025 | Nov 19, 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.
- Ministral 3 14B$3.33
- Llama 3.1 Nemotron Ultra 253BFree
- Grok 4.1 Fast$3.00
Which should you choose?
Which is better: Ministral 3 14B, Llama 3.1 Nemotron Ultra 253B or Grok 4.1 Fast?
Grok 4.1 Fast is the better all-round choice, scoring 71/100 against Llama 3.1 Nemotron Ultra 253B (65) and Ministral 3 14B (63). It leads on context window. Llama 3.1 Nemotron Ultra 253B wins on price. 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 3 14B, Llama 3.1 Nemotron Ultra 253B or Grok 4.1 Fast?
Llama 3.1 Nemotron Ultra 253B is cheaper at Free input / Free output per million tokens (official Nvidia API price). Grok 4.1 Fast costs $0.20 input / $0.50 output per million tokens (median across 2 API providers); Ministral 3 14B costs $0.268 input / $0.325 output per million tokens (median across 2 API providers). Llama 3.1 Nemotron Ultra 253B is listed as free.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ministral 3 14B has not been scored yet, Llama 3.1 Nemotron Ultra 253B has not been scored yet and Grok 4.1 Fast has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 3 14B, Llama 3.1 Nemotron Ultra 253B and Grok 4.1 Fast 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 4.1 Fast has the largest context window at 2,000,000 tokens, against 262,144 for Ministral 3 14B and 128,000 for Llama 3.1 Nemotron Ultra 253B. Maximum output per response: Ministral 3 14B up to 262,144, Llama 3.1 Nemotron Ultra 253B up to 8,192, Grok 4.1 Fast up to 30,000 tokens.
Which can read images, PDFs, audio or video?
Ministral 3 14B accepts text and images; Llama 3.1 Nemotron Ultra 253B accepts text; Grok 4.1 Fast accepts text and images. Ministral 3 14B handles the widest range of inputs.
Are any of these open source?
Ministral 3 14B and Llama 3.1 Nemotron Ultra 253B publishes its weights (Apache 2.0) and can be self-hosted; Grok 4.1 Fast is proprietary.
Which is newer?
Ministral 3 14B is the newest, released Dec 2, 2025. Grok 4.1 Fast came out Nov 19, 2025; Llama 3.1 Nemotron Ultra 253B came out Apr 7, 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.
