Llama 3.3 70B Instruct vs Mistral Medium 3.1

In our testing across 12 benchmarks, Mistral Medium 3.1 wins 5 tests outright, Llama 3.3 70B Instruct wins zero, and the two tie on 7.

Where Mistral Medium 3.1 wins:

• Strategic analysis (5 vs 3): Mistral ties for 1st among 54 models; Llama ranks 36th. This is a meaningful gap for tasks requiring nuanced tradeoff reasoning with real data.
• Constrained rewriting (5 vs 3): Mistral ties for 1st among 53 models; Llama ranks 31st. Compression under hard character limits — critical for marketing copy, summaries, and UI text generation.
• Persona consistency (5 vs 3): Mistral ties for 1st among 53 models; Llama ranks 45th of 53. A bottom-tier result for Llama here — it struggles to maintain character under adversarial conditions, which matters for chatbots and roleplay applications.
• Agentic planning (5 vs 3): Mistral ties for 1st among 54 models; Llama ranks 42nd. For goal decomposition and failure recovery in multi-step AI workflows, Mistral has a clear edge.
• Multilingual (5 vs 4): Mistral ties for 1st among 55 models; Llama ranks 36th. Mistral performs at the ceiling on non-English output quality; Llama is solid but below the top tier.

Where both models tie:

• Tool calling (both 4/5): Both rank 18th of 54, sharing the score with 29 other models. Adequate for function-calling workflows, but neither leads the field.
• Classification (both 4/5): Both tie for 1st among 53 models — a genuine strength for both in routing and categorization tasks.
• Long context (both 5/5): Both tie for 1st among 55 models. At a 131,072-token context window each, both handle 30K+ token retrieval at the highest level in our tests.
• Structured output (both 4/5): Both rank 26th of 54. Solid JSON schema compliance from both.
• Faithfulness (both 4/5): Both rank 34th of 55. Neither hallucinates excessively when grounded in source material.
• Safety calibration (both 2/5): Both rank 12th of 55, tied with 20 models. Below-median performance — both models score below the p50 of 2 is actually at the median. Neither is a strong choice for safety-critical applications.
• Creative problem solving (both 3/5): Both rank 30th of 54. Mid-field performance for generating non-obvious, feasible ideas.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct's math performance is notably weak. In our testing using third-party benchmark data from Epoch AI, it scores 41.6% on MATH Level 5 (ranking last of 14 models tested) and 5.1% on AIME 2025 (ranking last of 23 models tested). Mistral Medium 3.1 has no external benchmark scores in the data payload. These results confirm Llama 3.3 70B Instruct is not suitable for competition-level mathematics or quantitative reasoning tasks.

The pricing gap here is substantial. Llama 3.3 70B Instruct costs $0.10/1M input tokens and $0.32/1M output tokens. Mistral Medium 3.1 costs $0.40/1M input and $2.00/1M output — 4x more expensive on input and 6.25x more on output.

At 1M output tokens/month, you pay $0.32 for Llama vs $2.00 for Mistral — a $1.68 difference that's negligible. At 10M output tokens/month, that gap becomes $16.80 ($3.20 vs $20.00). At 100M output tokens/month — typical for a production API serving thousands of users — the cost difference is $168 per month ($320 vs $2,000).

For developers running high-volume pipelines, the calculus is clear: Mistral Medium 3.1's benchmark advantages in agentic planning and strategic analysis need to materially improve outcomes to justify a 6x output cost premium. For applications where the two models tie — tool calling, classification, long context, structured output, faithfulness — defaulting to Llama 3.3 70B Instruct is the rational economic decision. Teams with strict per-query cost budgets, or those building consumer products at scale, should weigh this gap carefully.

Choose Llama 3.3 70B Instruct if:

• Your workload centers on classification, long-context retrieval, tool calling, structured output, or faithfulness — categories where it ties Mistral Medium 3.1 in our testing at one-sixth the output cost.
• You're running high-volume production pipelines (10M+ tokens/month) where the $1.68/1M output cost savings compound into significant budget differences.
• Math and advanced reasoning are not requirements — but be aware this model ranks last of all tested models on MATH Level 5 (41.6%) and AIME 2025 (5.1%) per Epoch AI data.
• You need broad parameter support: Llama 3.3 70B Instruct offers logprobs, top_k, min_p, logit_bias, and repetition_penalty — features absent from Mistral Medium 3.1's parameter list.

Choose Mistral Medium 3.1 if:

• Your application requires strong agentic planning or multi-step reasoning — it scores 5/5 (tied 1st of 54) versus Llama's 3/5 (ranked 42nd).
• You're building AI pipelines where strategic analysis quality matters — Mistral scores 5/5 (tied 1st of 54) versus Llama's 3/5 (ranked 36th).
• Persona consistency is critical (chatbots, roleplay, brand voice) — Mistral scores 5/5 (tied 1st) while Llama scores 3/5 (ranked 45th of 53).
• You need multimodal input: Mistral Medium 3.1 accepts image input (text+image->text); Llama 3.3 70B Instruct is text-only.
• You're serving multilingual users and need ceiling-level non-English performance — Mistral scores 5/5 (tied 1st) versus Llama's 4/5 (ranked 36th).