Llama 3.3 70B Instruct vs Mistral Small 3.2 24B

Across our 12-test internal benchmark suite, Llama 3.3 70B Instruct wins 5 tests, Mistral Small 3.2 24B wins 2, and they tie on 5.

Where Llama 3.3 70B Instruct leads:

• Long context (5 vs 4): Llama scores a perfect 5, tied for 1st among 55 models in our suite. Mistral scores 4, ranked 38th of 55. This is a material difference for retrieval-heavy workflows — summarizing large documents, RAG over 30K+ token corpora, or legal/financial review at scale.
• Classification (4 vs 3): Llama ties for 1st among 53 models; Mistral ranks 31st. For routing, tagging, or categorization workloads, Llama's edge here is real and the ranking gap is large.
• Strategic analysis (3 vs 2): Llama ranks 36th of 54; Mistral ranks 44th. Neither model is a standout here (the median is 4 across our suite), but Llama is the less-bad option for nuanced tradeoff reasoning.
• Creative problem solving (3 vs 2): Llama ranks 30th of 54; Mistral ranks 47th — near the bottom. For generating non-obvious, feasible ideas, Mistral trails significantly.
• Safety calibration (2 vs 1): Llama ranks 12th of 55; Mistral ranks 32nd. Both score below the suite median of 2, but Llama refuses harmful requests and permits legitimate ones more reliably.

Where Mistral Small 3.2 24B leads:

• Agentic planning (4 vs 3): Mistral ranks 16th of 54; Llama ranks 42nd. For goal decomposition, multi-step task planning, and failure recovery — the backbone of agentic workflows — Mistral's 4 versus Llama's 3 is a meaningful advantage, especially since the suite median is 4.
• Constrained rewriting (4 vs 3): Mistral ranks 6th of 53; Llama ranks 31st. Compressing text within hard character limits is a common copywriting, UI, and SEO task — Mistral handles it substantially better here.

Where they tie (same score, same ranking tier):

• Tool calling (4/4): Both rank 18th of 54, sharing the score with 29 models. Adequate for function-calling use cases, but neither is a top-tier tool caller.
• Structured output (4/4): Both rank 26th of 54. JSON schema compliance is solid but not exceptional for either.
• Faithfulness (4/4): Both rank 34th of 55. Neither hallucinates excessively relative to source material.
• Multilingual (4/4): Both rank 36th of 55. Equivalent non-English quality.
• Persona consistency (3/3): Both rank 45th of 53. Neither excels at maintaining character under injection attacks.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has external scores on record: 41.6% on MATH Level 5 and 5.1% on AIME 2025. Both rank last among models with scores in our dataset (14th of 14 and 23rd of 23, respectively), placing it well below the suite medians of 94.15% and 83.9%. This signals Llama 3.3 70B Instruct is not suited for competition-grade math. Mistral Small 3.2 24B has no external benchmark scores in our dataset — we cannot make a comparison on this dimension. Neither model should be selected for hard math reasoning tasks based on this data.

Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output. Mistral Small 3.2 24B costs $0.075/M input and $0.20/M output. That's a 25% cheaper input and 37.5% cheaper output for Mistral. At real-world volumes, the gap compounds quickly on output-heavy tasks: at 1M output tokens/month, Llama costs $0.32 versus Mistral's $0.20 — a $0.12 difference barely worth optimizing around. At 10M output tokens, that gap becomes $1.20 versus $2.00 — still manageable for most teams. At 100M output tokens (high-volume production pipelines), Llama costs $32 versus Mistral's $20, a $12/month saving. For most applications the cost difference is modest, but developers running high-throughput inference at scale — chat bots, document processing pipelines, bulk classification — will find Mistral's lower rate meaningful. However, if your tasks favor Llama's benchmark strengths (long context, classification, strategic analysis), paying ~60% more on output may be well justified by quality gains.

Choose Llama 3.3 70B Instruct if:

• Your primary workload involves long documents (30K+ tokens) — it scores a perfect 5 on long-context retrieval, tied for 1st among 55 models.
• You need reliable classification or routing — Llama ties for 1st among 53 models, a major gap over Mistral's 31st.
• Safety calibration matters — Llama ranks 12th versus Mistral's 32nd, meaning fewer false refusals and better handling of edge cases.
• You need the stronger all-around performer and cost is not a primary concern.

Choose Mistral Small 3.2 24B if:

• You're building agentic pipelines that require multi-step planning and failure recovery — Mistral scores 4 vs Llama's 3, ranking 16th versus Llama's 42nd out of 54.
• Your use case involves constrained writing tasks (ad copy, UI labels, character-limited summaries) — Mistral ranks 6th of 53 on constrained rewriting versus Llama's 31st.
• You need image input support — Mistral's text+image modality is not available in Llama 3.3 70B Instruct per our data.
• You're running at high output volume and the $0.12/M output savings meaningfully affects your cost model.
• Math reasoning is not in scope — neither model performs well on competition math benchmarks, but Llama's external scores are explicitly at the bottom of our dataset.