Llama 3.3 70B Instruct vs Ministral 3 8B 2512

Across our 12 internal benchmark tests, Llama 3.3 70B Instruct and Ministral 3 8B 2512 tie on 8 of them, with each model claiming 2 outright wins.

Where Llama 3.3 70B Instruct leads:

• Long context: 5/5 vs 4/5 — Llama ties for 1st among 55 tested models on retrieval accuracy at 30K+ tokens. Ministral ranks 38th of 55. For RAG pipelines, legal document review, or summarizing lengthy transcripts, this is a meaningful edge. Llama's 128K context window is also relevant here, though Ministral's 262K context window is substantially larger — if you need to load more tokens in, Ministral can hold more, but Llama retrieves from them more accurately in our tests.
• Safety calibration: 2/5 vs 1/5 — Llama ranks 12th of 55 (tied with 19 others); Ministral ranks 32nd of 55 (tied with 23 others). Neither model excels here relative to the field's median of 2/5, but Llama is more reliable at refusing harmful requests while permitting legitimate ones.

Where Ministral 3 8B 2512 leads:

• Constrained rewriting: 5/5 vs 3/5 — Ministral ties for 1st among 53 tested models (with 4 others); Llama ranks 31st. This is the clearest performance gap in the comparison. For tasks requiring compression within hard character limits — ad copy, headlines, UI labels, summaries — Ministral is the better tool.
• Persona consistency: 5/5 vs 3/5 — Ministral ties for 1st among 53 models (with 36 others); Llama ranks 45th of 53. For chatbot personas, roleplay, or customer-facing assistants that must stay in character, Ministral is substantially better in our testing.

Where they tie (8 benchmarks): Both models score identically on classification (4/5, tied for 1st with 29 others), tool calling (4/5, rank 18 of 54), structured output (4/5, rank 26 of 54), agentic planning (3/5, rank 42 of 54), strategic analysis (3/5, rank 36 of 54), creative problem solving (3/5, rank 30 of 54), faithfulness (4/5, rank 34 of 55), and multilingual (4/5, rank 36 of 55). These ties mean neither model has a meaningful edge for classification pipelines, JSON generation, tool-using agents, multi-step reasoning, or non-English tasks.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has scores on MATH Level 5 (41.6%) and AIME 2025 (5.1%), ranking last among models tested on both — 14th of 14 and 23rd of 23 respectively, according to Epoch AI data. These scores place it well below the median models in our suite (MATH Level 5 median: 94.15%; AIME 2025 median: 83.9%). Ministral 3 8B 2512 has no external benchmark scores in our data. For math-intensive workloads, neither model appears strong, and Llama's external scores confirm it should not be the choice for competition-level math tasks.

Modality note: Ministral 3 8B 2512 supports image input (text+image->text); Llama 3.3 70B Instruct is text-only. This is a hard differentiator if vision tasks are part of your workflow.

Llama 3.3 70B Instruct costs $0.10/M input tokens and $0.32/M output tokens. Ministral 3 8B 2512 costs $0.15/M input and $0.15/M output — making input slightly more expensive but output significantly cheaper. For output-heavy workloads, this gap compounds fast: at 1M output tokens/month, Llama costs $0.32 vs Ministral's $0.15 — a $0.17 difference. Scale to 10M output tokens and that's $3.20 vs $1.50, saving $1.70/month with Ministral. At 100M output tokens — realistic for high-volume API applications — Llama costs $32 vs Ministral's $15, a $17/month gap. The overall price ratio is 2.13x on output, meaning Ministral is meaningfully cheaper for generation-heavy use cases like content pipelines, chatbots, and agent loops. Developers with balanced read/write patterns should model their actual token mix: if input dominates, Llama's $0.10/M input gives it an edge.

Choose Llama 3.3 70B Instruct if: your workflow involves long documents (RAG, contract review, transcript analysis) where its 5/5 long-context score — tied for 1st of 55 models in our tests — matters more than cost; or if safety calibration is a meaningful product requirement, where it outscores Ministral 2/5 vs 1/5.

Choose Ministral 3 8B 2512 if: you need strong constrained rewriting (5/5, tied for 1st of 53 models vs Llama's 3/5), persona-consistent chatbots or assistants (5/5 vs 3/5), vision input processing, or you're running at scale and output costs are a budget constraint — Ministral's $0.15/M output is less than half of Llama's $0.32/M. For the 8 benchmarks where they tie, default to Ministral for cost efficiency.