Llama-3.2-1B vs Phi-4-mini vs Ministral 8B Instruct
Too close to call on our weighted score (Phi-4-mini 58, Ministral 8B Instruct 57, Llama-3.2-1B 55). The right pick depends on what you value most.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
MicrosoftPhi-4-mini
58/100- ECI—
- Price$0.075 / $0.30
- Context128K
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
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-3.2-1B 55/100), so choose by what matters most for your work: Llama-3.2-1B 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-3.2-1BLlama-3.2-1B $0.085 · Phi-4-mini $0.131 · Ministral 8B Instruct $0.15 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1B and Ministral 8B InstructLlama-3.2-1B 131,072 · Ministral 8B Instruct 131,072 · Phi-4-mini 128,000 tokens
- Widest inputsSame inputsLlama-3.2-1B: Text · Phi-4-mini: Text · Ministral 8B Instruct: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Llama-3.2-1B | Phi-4-mini | Ministral 8B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 92 | 89 |
| Inputs & features | 30% | 0 | 25 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 55/100 | 58/100 | 57/100 |
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 | Llama-3.2-1BMeta | Phi-4-miniMicrosoft | Ministral 8B InstructMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 102.0 | — | — |
| ECI rank | #147 of 148 | — | — |
| GPQA DiamondGraduate-level science questions | 23.9% | — | 27.2% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 0.6% | — | — |
| Price per million tokens | |||
| Input | $0.064 (best) | $0.075 | $0.15 |
| Output | $0.15 | $0.30 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.131 | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Azure API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens (best) | 4,096 tokens | 8,192 tokens (best) |
| 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 | No | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenLlama 3.2 Community License | Open | OpenMistral Research License |
| API model ID | — | phi-4-mini | — |
| API providers | 2 (best) | 1 | 1 |
| Released | Sep 25, 2024 | Dec 11, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Dec 2023 | Oct 2023 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Llama-3.2-1B$0.936
- Phi-4-mini$1.35
- Ministral 8B Instruct$1.80
Which should you choose?
Which is better: Llama-3.2-1B, Phi-4-mini or Ministral 8B Instruct?
It is close. Our weighted score puts them within 1 points (Phi-4-mini 58/100, Ministral 8B Instruct 57/100, Llama-3.2-1B 55/100), so choose by what matters most for your work: Llama-3.2-1B 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, Llama-3.2-1B, Phi-4-mini or Ministral 8B Instruct?
Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). Phi-4-mini costs $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). At a typical mix of three input tokens to one output token, that is $0.085 per million tokens for Llama-3.2-1B versus $0.131 for Phi-4-mini (1.5× as much) and $0.15 for Ministral 8B Instruct (1.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Phi-4-mini has not been scored yet and Ministral 8B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Llama-3.2-1B, Phi-4-mini and Ministral 8B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B and Ministral 8B Instruct have the largest context windows (131,072 and 131,072 tokens), against 128,000 for Phi-4-mini. Maximum output per response: Llama-3.2-1B up to 8,192, Phi-4-mini up to 4,096, Ministral 8B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Phi-4-mini accepts text; Ministral 8B Instruct accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Llama 3.2 Community License and 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-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Phi-4-mini Oct 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.
