Llama 3.3 70B Instruct vs Mistral Large 3 2512

Across our 12 internal benchmarks, Mistral Large 3 2512 wins 5 tests, Llama 3.3 70B Instruct wins 3, and they tie on 4.

Where Mistral Large 3 2512 wins:

• Structured output (5 vs 4): Mistral scores a top-tier 5/5, tied for 1st among 54 models, versus Llama's 4/5. This matters for any application relying on JSON schema compliance or consistent response formatting.
• Faithfulness (5 vs 4): Mistral scores 5/5, tied for 1st among 55 models in our testing. Llama scores 4/5 (rank 34 of 55). When accurate grounding to source material is critical — RAG pipelines, document Q&A — Mistral has a clear edge.
• Multilingual (5 vs 4): Mistral scores 5/5, tied for 1st among 55 models. Llama scores 4/5 (rank 36 of 55). For non-English deployments, this single-point gap is significant.
• Strategic analysis (4 vs 3): Mistral ranks 27 of 54; Llama ranks 36 of 54. Mistral handles nuanced tradeoff reasoning more reliably.
• Agentic planning (4 vs 3): Mistral ranks 16 of 54 with 4/5; Llama ranks 42 of 54 with 3/5. This gap matters for multi-step agent workflows requiring goal decomposition and failure recovery.

Where Llama 3.3 70B Instruct wins:

• Long context (5 vs 4): Llama scores 5/5, tied for 1st among 55 models. Mistral scores 4/5 (rank 38 of 55). At 30K+ token retrieval tasks, Llama is the stronger choice — notable given Mistral has a larger context window (262K vs 131K).
• Classification (4 vs 3): Llama scores 4/5, tied for 1st among 53 models. Mistral scores 3/5 (rank 31 of 53). For routing and categorization use cases, Llama is meaningfully better.
• Safety calibration (2 vs 1): Llama scores 2/5 (rank 12 of 55); Mistral scores 1/5 (rank 32 of 55). Neither model excels here — both score below the median of 2 — but Llama is relatively less problematic.

Ties (both score identically):

• Tool calling: both 4/5, tied rank 18 of 54.
• Creative problem solving: both 3/5.
• Constrained rewriting: both 3/5.
• Persona consistency: both 3/5.

External benchmarks: Llama 3.3 70B Instruct has scores from Epoch AI's math benchmarks: 41.6% on MATH Level 5 (ranking last — 14th of 14 models in our dataset with this score) and 5.1% on AIME 2025 (ranking last — 23rd of 23). These scores indicate weak quantitative reasoning capability. Mistral Large 3 2512 has no external benchmark scores in the payload, so direct comparison on these dimensions isn't possible.

Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output tokens. Mistral Large 3 2512 costs $0.50/M input and $1.50/M output tokens — 5x higher on input and 4.7x higher on output. At 1M output tokens/month, that's $320 vs $1,500: a $1,180 difference. At 10M output tokens/month the gap widens to $11,800 per month ($3,200 vs $15,000). At 100M output tokens the cost difference reaches $118,000 per month. For high-volume, cost-sensitive deployments — content pipelines, classification at scale, long-document summarization — the price gap is material. For low-volume enterprise use cases where faithfulness and agentic reliability are critical, the premium for Mistral Large 3 2512 may be justified. Developers building prototypes or running evaluation loops should strongly prefer the cheaper model unless the specific capability gaps (structured output, strategic analysis, multilingual) are blockers.

Choose Llama 3.3 70B Instruct if: you're running high-volume workloads where cost is a primary constraint; your use case centers on classification, routing, or long-context document retrieval (where it scores 4-5/5 and ties for 1st in our testing); you need a well-priced general model for text pipelines; or you're prototyping and want to minimize API spend without sacrificing core capabilities. Avoid it for math-heavy applications — its AIME 2025 score of 5.1% and MATH Level 5 score of 41.6% (Epoch AI) place it last in our dataset on those benchmarks.

Choose Mistral Large 3 2512 if: your application demands reliable structured output and JSON compliance (5/5 in our testing, tied for 1st); you're building multilingual products (5/5, tied for 1st); faithfulness to source material is non-negotiable for your RAG or document workflows (5/5, tied for 1st); or you're building agentic systems where planning and failure recovery matter (4/5 vs Llama's 3/5). The 5x output cost premium is easiest to justify for enterprise use cases with moderate token volumes and high accuracy requirements — not for bulk processing.