Devstral 2 2512 vs GPT-5.4 Nano

Across our 12-test suite, GPT-5.4 Nano wins 3 benchmarks outright, Devstral 2 2512 wins 1, and 8 are ties.

Where GPT-5.4 Nano wins:

• Strategic analysis: GPT-5.4 Nano scores 5/5 (tied for 1st of 54 models with 25 others) vs Devstral 2 2512's 4/5 (rank 27 of 54). For nuanced tradeoff reasoning with real numbers, GPT-5.4 Nano is meaningfully ahead.
• Safety calibration: GPT-5.4 Nano scores 3/5 (rank 10 of 55, shared with just 1 other model) vs Devstral 2 2512's 1/5 (rank 32 of 55, shared with 23 others). A score of 1 on safety calibration places Devstral 2 2512 at the 25th percentile or below in our distribution — a real concern for any customer-facing or regulated deployment.
• Persona consistency: GPT-5.4 Nano scores 5/5 (tied for 1st of 53 models) vs Devstral 2 2512's 4/5 (rank 38 of 53). This matters for chatbot, role-based assistant, and character-driven applications.

Where Devstral 2 2512 wins:

• Constrained rewriting: Devstral 2 2512 scores 5/5 (tied for 1st among 53 models with 4 others) vs GPT-5.4 Nano's 4/5 (rank 6 of 53). This is compression within hard character limits — useful for ad copy, notification text, or any task with strict length constraints.

The 8 ties (same score on both models):

• Structured output: both 5/5, tied for 1st of 54
• Tool calling: both 4/5, rank 18 of 54
• Faithfulness: both 4/5, rank 34 of 55
• Classification: both 3/5, rank 31 of 53
• Long context: both 5/5, tied for 1st of 55
• Agentic planning: both 4/5, rank 16 of 54
• Multilingual: both 5/5, tied for 1st of 55
• Creative problem solving: both 4/5, rank 9 of 54

The tied categories are largely mid-to-high tier results — both models handle structured output, long context, multilingual, tool calling, and agentic planning competently and at the same level in our testing.

External benchmark note: GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), ranking 8th of 23 models with that data — placing it above the median of 83.9% for that benchmark set. No AIME 2025 or other external benchmark data is available for Devstral 2 2512 in the payload, so a direct external comparison cannot be made.

Devstral 2 2512 costs $0.40/MTok input and $2.00/MTok output. GPT-5.4 Nano costs $0.20/MTok input and $1.25/MTok output — half the input price and 37.5% cheaper on output. At 1M output tokens/month, that gap is $750 vs $1,250 — a $500/month difference. At 10M tokens it becomes $5,000 vs $12,500. At 100M tokens the gap widens to $50,000 vs $125,000 annually — a significant infrastructure cost. GPT-5.4 Nano also supports image and file inputs, which Devstral 2 2512 does not per the payload, adding multimodal capability at no extra tier cost. Teams running high-volume text pipelines, classification jobs, or customer-facing chat will feel the 1.6× price differential acutely at scale. Devstral 2 2512's premium is harder to justify unless its specific benchmark advantages — primarily constrained rewriting — directly map to your use case.

Choose GPT-5.4 Nano if:

• Cost efficiency at scale matters — it's 37–50% cheaper per token and those savings compound fast past 10M tokens/month.
• You need strong safety calibration (score 3 vs 1) for regulated, enterprise, or customer-facing deployments.
• Your app relies on persona consistency or role-playing — GPT-5.4 Nano scores 5/5 vs 4/5.
• You need strategic analysis or nuanced reasoning tasks — it scores 5/5 vs 4/5.
• You want multimodal input support (text + image + file), which Devstral 2 2512 does not offer per the payload.
• You need a larger context window: GPT-5.4 Nano supports 400K tokens vs Devstral 2 2512's 262K.

Choose Devstral 2 2512 if:

• Constrained rewriting is a primary workload — it ties for 1st of 53 models on that specific task.
• You are building agentic coding pipelines and Devstral 2's specialization in that domain (as described in its model description) aligns with your architecture.
• Safety calibration is not a concern in your deployment context and you've accepted the tradeoff.