Llama-3.2-1B vs Pixtral 12B vs Ministral 8B Instruct
Pixtral 12B comes out ahead, 64 to 57 and 55 on our weighted score, though Llama-3.2-1B is 43% cheaper per token.
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
- Our pick
Mistral AIPixtral 12B
64/100- ECI—
- Price$0.15 / $0.15
- Context128K
- Mistral AI
Ministral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- Context131K
Pixtral 12B is our pick
Pixtral 12B is the better all-round choice, scoring 64/100 against Ministral 8B Instruct (57) and Llama-3.2-1B (55). It leads on inputs & features. Llama-3.2-1B 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-3.2-1BLlama-3.2-1B $0.085 · Pixtral 12B $0.15 · 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 · Pixtral 12B 128,000 tokens
- Widest inputsPixtral 12BLlama-3.2-1B: Text · Pixtral 12B: Text, Images · 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 | Pixtral 12B | Ministral 8B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 89 | 89 |
| Inputs & features | 30% | 0 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 55/100 | 64/100 | 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 | Pixtral 12BMistral AI | 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) | $0.15 | $0.15 |
| Output | $0.15 | $0.15 (best) | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.085 (best) | $0.15 | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Mistral API | Median of 1 providers |
| Limits | |||
| Context window | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Max output | 8,192 tokens | 128,000 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | 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 | — | pixtral-12b | — |
| API providers | 2 | 4 (best) | 1 |
| Released | Sep 25, 2024 | Sep 1, 2024 | Oct 16, 2024 |
| Knowledge cutoff | Dec 2023 | Sep 2024 | — |
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
- Pixtral 12B$1.80
- Ministral 8B Instruct$1.80
Which should you choose?
Which is better: Llama-3.2-1B, Pixtral 12B or Ministral 8B Instruct?
Pixtral 12B is the better all-round choice, scoring 64/100 against Ministral 8B Instruct (57) and Llama-3.2-1B (55). It leads on inputs & features. Llama-3.2-1B 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, Llama-3.2-1B, Pixtral 12B 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). Pixtral 12B costs $0.15 input / $0.15 output per million tokens (official Mistral 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 $0.15 for Pixtral 12B (1.8× 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, Pixtral 12B 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, Pixtral 12B 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 Pixtral 12B. Maximum output per response: Llama-3.2-1B up to 8,192, Pixtral 12B up to 128,000, Ministral 8B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Llama-3.2-1B accepts text; Pixtral 12B accepts text and images; Ministral 8B Instruct accepts text. Pixtral 12B handles the widest range of inputs.
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?
Ministral 8B Instruct is the newest, released Oct 16, 2024. Llama-3.2-1B came out Sep 25, 2024; Pixtral 12B came out Sep 1, 2024. Knowledge cutoff: Llama-3.2-1B Dec 2023, Pixtral 12B Sep 2024.
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.
