Ministral 8B Instruct vs Phi-4-mini vs Llama-3.1-8B-Instruct
Too close to call on our weighted score (Phi-4-mini 58, Ministral 8B Instruct 57, Llama-3.1-8B-Instruct 56). The right pick depends on what you value most.
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
MicrosoftPhi-4-mini
58/100- ECI—
- Price$0.075 / $0.30
- Context128K
MetaLlama-3.1-8B-Instruct
56/100- ECI116.6
- Price$0.152 / $0.167
- Context128K
Too close to call
It is close. Our weighted score puts them within 1 points (Phi-4-mini 58/100, Ministral 8B Instruct 57/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Phi-4-mini on price and Ministral 8B Instruct 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 pricePhi-4-miniPhi-4-mini $0.131 · Ministral 8B Instruct $0.15 · Llama-3.1-8B-Instruct $0.156 per 1M tokens (3:1 blend)
- Longest contextMinistral 8B InstructMinistral 8B Instruct 131,072 · Phi-4-mini 128,000 · Llama-3.1-8B-Instruct 128,000 tokens
- Widest inputsSame inputsMinistral 8B Instruct: Text · Phi-4-mini: Text · Llama-3.1-8B-Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Ministral 8B Instruct | Phi-4-mini | Llama-3.1-8B-Instruct |
|---|---|---|---|---|
| Price | 50% | 89 | 92 | 88 |
| Inputs & features | 30% | 25 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 57/100 | 58/100 | 56/100 |
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 8B InstructMistral AI | Phi-4-miniMicrosoft | Llama-3.1-8B-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 116.6 |
| ECI rank | — | — | #145 of 148 |
| GPQA DiamondGraduate-level science questions | 27.2% (best) | — | 27.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 1.7% |
| Price per million tokens | |||
| Input | $0.15 | $0.075 (best) | $0.152 |
| Output | $0.15 (best) | $0.30 | $0.167 |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | $0.131 (best) | $0.156 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Azure API | Median of 9 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 128,000 tokens |
| Max output | 8,192 tokens (best) | 4,096 tokens | 4,096 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenMistral Research License | Open | Open |
| API model ID | — | phi-4-mini | — |
| API providers | 1 | 1 | 9 (best) |
| Released | Oct 16, 2024 | Dec 11, 2024 | Jul 23, 2024 |
| Knowledge cutoff | — | Oct 2023 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ministral 8B Instruct$1.80
- Phi-4-mini$1.35
- Llama-3.1-8B-Instruct$1.85
Which should you choose?
Which is better: Ministral 8B Instruct, Phi-4-mini or Llama-3.1-8B-Instruct?
It is close. Our weighted score puts them within 1 points (Phi-4-mini 58/100, Ministral 8B Instruct 57/100, Llama-3.1-8B-Instruct 56/100), so choose by what matters most for your work: Phi-4-mini on price and Ministral 8B Instruct 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 8B Instruct, Phi-4-mini or Llama-3.1-8B-Instruct?
Phi-4-mini is cheaper at $0.075 input / $0.30 output per million tokens (official Azure API price). Ministral 8B Instruct costs $0.15 input / $0.15 output per million tokens (median across 1 API provider); Llama-3.1-8B-Instruct costs $0.152 input / $0.167 output per million tokens (median across 9 API providers). At a typical mix of three input tokens to one output token, that is $0.131 per million tokens for Phi-4-mini versus $0.15 for Ministral 8B Instruct (1.1× as much) and $0.156 for Llama-3.1-8B-Instruct (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ministral 8B Instruct has not been scored yet, Phi-4-mini has not been scored yet and Llama-3.1-8B-Instruct has an ECI of 116.6.
Which is better for coding?
There are no published SWE-bench Verified results for Ministral 8B Instruct, Phi-4-mini and Llama-3.1-8B-Instruct 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 8B Instruct has the largest context window at 131,072 tokens, against 128,000 for Phi-4-mini and 128,000 for Llama-3.1-8B-Instruct. Maximum output per response: Ministral 8B Instruct up to 8,192, Phi-4-mini up to 4,096, Llama-3.1-8B-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Ministral 8B Instruct accepts text; Phi-4-mini accepts text; Llama-3.1-8B-Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Mistral Research License), so you can self-host them.
Which is newer?
Phi-4-mini is the newest, released Dec 11, 2024. Ministral 8B Instruct came out Oct 16, 2024; Llama-3.1-8B-Instruct came out Jul 23, 2024. Knowledge cutoff: Phi-4-mini Oct 2023, Llama-3.1-8B-Instruct Dec 2023.
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.
