Devstral Small 1.1 vs Llama 3.3 70B Instruct

Llama 3.3 70B Instruct wins 5 of 12 benchmarks in our testing; Devstral Small 1.1 wins none. The two models tie on 7 tests.

Where Llama 3.3 70B wins:

• Long context (5 vs 4): Llama scores 5/5, tied for 1st among 55 models. Devstral scores 4/5 (rank 38 of 55). For retrieval tasks at 30K+ tokens, Llama is meaningfully more reliable.
• Agentic planning (3 vs 2): Llama scores 3/5 (rank 42 of 54); Devstral scores 2/5 (rank 53 of 54, near the bottom). This is a significant gap for goal decomposition and failure recovery — ironic given Devstral's agent-focused positioning.
• Strategic analysis (3 vs 2): Llama scores 3/5 (rank 36 of 54); Devstral scores 2/5 (rank 44 of 54). Nuanced tradeoff reasoning clearly favors Llama.
• Creative problem solving (3 vs 2): Llama scores 3/5 (rank 30 of 54); Devstral scores 2/5 (rank 47 of 54).
• Persona consistency (3 vs 2): Llama scores 3/5 (rank 45 of 53); Devstral scores 2/5 (rank 51 of 53). Devstral is near the bottom of all tested models here.

Where they tie (7 benchmarks):

• Classification (4/4): Both tied for 1st among 53 models — a shared strength.
• Tool calling (4/4): Both rank 18 of 54, tied with 29 models. Solid but not elite.
• Structured output (4/4): Both rank 26 of 54.
• Faithfulness (4/4): Both rank 34 of 55.
• Constrained rewriting (3/3): Both rank 31 of 53.
• Safety calibration (2/2): Both rank 12 of 55 — a tie, though both score below the field median on this dimension.
• Multilingual (4/4): Both rank 36 of 55.

External benchmarks (Epoch AI): Llama 3.3 70B Instruct has external benchmark scores included in the payload. On MATH Level 5, it scores 41.6% — ranking last (14th of 14) among models we have data for, which sits well below the field median of 94.15% in our dataset. On AIME 2025, it scores 5.1% — last of 23 models with data, against a median of 83.9%. These third-party results confirm that Llama 3.3 70B Instruct is not a math or competition reasoning model. No external benchmark data is available in the payload for Devstral Small 1.1.

These two models are effectively at pricing parity. Devstral Small 1.1 costs $0.10 per million input tokens and $0.30 per million output tokens. Llama 3.3 70B Instruct costs $0.10 input and $0.32 output — a difference of just $0.02 per million output tokens. At 1M output tokens/month, Llama costs $0.02 more. At 10M tokens/month, that gap is $0.20. At 100M tokens/month, it reaches $2.00 — negligible for any production workload. The price ratio is 0.9375, meaning Devstral is technically 6.25% cheaper on output, but this is immaterial in practice. Cost should play no role in choosing between these two models; pick based on benchmark performance and use-case fit.

Choose Llama 3.3 70B Instruct if you need a capable general-purpose AI for tasks involving long documents, strategic reasoning, agentic workflows, creative ideation, or consistent persona maintenance. It outscores Devstral on 5 of 12 benchmarks in our testing and costs essentially the same — $0.02 more per million output tokens is not a real cost consideration. It is also the better choice if you need the extended parameter set (logprobs, top_k, repetition_penalty, min_p) for fine-grained output control, and it has an explicit 16,384 max output token limit documented in the payload.

Choose Devstral Small 1.1 if you are building a software engineering agent pipeline and the model's specialized fine-tuning for that domain (noted in its description as purpose-built for software engineering agents, developed with All Hands AI) is the primary requirement. Be aware that on our broader 12-test suite it wins no benchmarks outright, including scoring near the bottom on agentic planning — so validate performance in your specific coding-agent context before committing. It also supports structured outputs and tool calling at the same level as Llama, so it is viable for function-calling workflows.