Devstral 2 2512 vs Llama 3.3 70B Instruct

Across our 12-test suite, Devstral 2 2512 wins 7 benchmarks, Llama 3.3 70B Instruct wins 2, and they tie on 3.

Where Devstral 2 2512 leads:

• Constrained rewriting (5 vs 3): Devstral 2 2512 ties for 1st with 4 other models out of 53 tested. Llama 3.3 70B Instruct ranks 31st of 53. This matters for any task requiring compression within hard character limits — marketing copy, summaries, or UI microcopy generation.
• Structured output (5 vs 4): Devstral 2 2512 ties for 1st out of 54 tested; Llama 3.3 70B Instruct ranks 26th. For JSON schema compliance and API integrations, Devstral 2 2512 is the more reliable choice.
• Multilingual (5 vs 4): Devstral 2 2512 ties for 1st out of 55 tested; Llama 3.3 70B Instruct ranks 36th. Non-English applications will see a meaningful quality difference.
• Agentic planning (4 vs 3): Devstral 2 2512 ranks 16th of 54; Llama 3.3 70B Instruct ranks 42nd. For goal decomposition and multi-step failure recovery — core to agentic coding workflows — this gap is significant.
• Creative problem solving (4 vs 3): Devstral 2 2512 ranks 9th of 54 vs Llama 3.3 70B Instruct's 30th. More capable at generating non-obvious, feasible solutions.
• Strategic analysis (4 vs 3): Devstral 2 2512 ranks 27th of 54; Llama 3.3 70B Instruct ranks 36th. Better nuanced tradeoff reasoning with real numbers.
• Persona consistency (4 vs 3): Devstral 2 2512 ranks 38th of 53 vs Llama 3.3 70B Instruct's 45th — both mid-to-lower tier, but Devstral 2 2512 maintains a one-point advantage.

Where Llama 3.3 70B Instruct leads:

• Classification (4 vs 3): Llama 3.3 70B Instruct ties for 1st out of 53 tested; Devstral 2 2512 ranks 31st. For routing and categorization workloads, Llama 3.3 70B Instruct is the clear winner.
• Safety calibration (2 vs 1): Llama 3.3 70B Instruct ranks 12th of 55; Devstral 2 2512 ranks 32nd. Both are below the median for this benchmark (p50 = 2), but Llama 3.3 70B Instruct handles harmful request refusals more reliably in our testing.

Ties (both models equal):

• Tool calling (4/4): Both rank 18th of 54 and share the same score — tied performance on function selection and argument accuracy.
• Faithfulness (4/4): Both rank 34th of 55. Neither model stands out for strict source adherence.
• Long context (5/5): Both tie for 1st out of 55 tested. At 30K+ token retrieval, both perform equally well — though Devstral 2 2512's 262K context window is double Llama 3.3 70B Instruct's 131K, which matters for longer document workflows.

External benchmarks: Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 and 5.1% on AIME 2025 (Epoch AI), ranking last among tested models on both (14th of 14 and 23rd of 23, respectively). These scores place it well below the median for math competition tasks — MATH Level 5 p50 is 94.15% and AIME 2025 p50 is 83.9% across tested models. Devstral 2 2512 has no external benchmark scores in our dataset. Neither model should be the first choice for advanced mathematical reasoning based on available data.

Devstral 2 2512 costs $0.40/M input tokens and $2.00/M output tokens. Llama 3.3 70B Instruct costs $0.10/M input and $0.32/M output — a 4x difference on input and 6.25x on output. At 1M output tokens/month, that's $2,000 vs $320 — a $1,680 gap. At 10M output tokens, you're looking at $20,000 vs $3,200, a $16,800 monthly difference. At 100M tokens, the gap hits $168,000. For developers running high-volume pipelines — content generation, summarization at scale, or chatbot backends with many short turns — Llama 3.3 70B Instruct's pricing is hard to ignore. Devstral 2 2512's premium is justifiable for agentic coding workflows, complex multi-step tasks, or applications where output quality directly affects business outcomes. If you're prototyping or running classification-heavy pipelines, the cost savings from Llama 3.3 70B Instruct alone may drive the decision.

Choose Devstral 2 2512 if: You're building agentic coding pipelines, multi-step automation, or tools that require structured JSON output, multilingual responses, or complex constrained writing. Its 262K context window also gives it an edge for long-document workflows. The $2.00/M output token cost is the tradeoff, but the quality advantage across 7 of 12 benchmarks justifies it for professional or production use cases where output quality directly affects outcomes.

Choose Llama 3.3 70B Instruct if: Your primary use case is classification, content routing, or any task where you need to categorize at scale — it ties for 1st on classification in our testing. It also scores better on safety calibration, making it a safer default for consumer-facing applications. At $0.32/M output tokens, it's the right call for high-volume, cost-sensitive workloads where the quality gap on tasks like agentic planning or multilingual output won't matter. Developers who need parameters like logprobs, top_k, or top_logprobs will also find more flexibility in Llama 3.3 70B Instruct's supported parameter set. Avoid both models if advanced math reasoning is a core requirement — Llama 3.3 70B Instruct scores at the bottom of tested models on AIME 2025 and MATH Level 5 (Epoch AI), and Devstral 2 2512 has no external math benchmark data available.