Llama-3.2-1B vs Ministral 8B Instruct vs Pixtral 12B

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.

Llama-3.2-1B is cheaper at $0.064 input / $0.15 output per million tokens (median across 2 API providers). Ministral 8B Instruct costs $0.15 input / $0.15 output per million tokens (median across 1 API provider); Pixtral 12B costs $0.15 input / $0.15 output per million tokens (official Mistral API price). 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 Ministral 8B Instruct (1.8× as much) and $0.15 for Pixtral 12B (1.8× as much).

There is no independent benchmark that covers all three models yet. Llama-3.2-1B has an ECI of 102.0, Ministral 8B Instruct has not been scored yet and Pixtral 12B has not been scored yet.

There are no published SWE-bench Verified results for Llama-3.2-1B, Ministral 8B Instruct and Pixtral 12B 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.

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, Ministral 8B Instruct up to 8,192, Pixtral 12B up to 128,000 tokens.

Llama-3.2-1B accepts text; Ministral 8B Instruct accepts text; Pixtral 12B accepts text and images. Pixtral 12B handles the widest range of inputs.

Yes, all three publish their weights (Llama 3.2 Community License and Mistral Research License), so you can self-host them.

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.

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.