GPT-5.4 Nano vs Ministral 3 14B 2512

Summary of the 12-test comparison in our suite (scores use our 1–5 scale unless noted). Wins, losses and ties are from our testing. Detailed walk-through:

• Structured output: GPT-5.4 Nano 5 vs Ministral 4 — GPT-5.4 Nano wins. GPT-5.4 Nano is tied for 1st in structured output ("tied for 1st with 24 other models out of 54 tested"), meaning it consistently follows JSON/schema constraints in production use.

• Strategic analysis: GPT-5.4 Nano 5 vs Ministral 4 — GPT-5.4 Nano wins. Nano is tied for 1st ("tied for 1st with 25 other models"), which translates to stronger nuanced tradeoff reasoning and numeric decision work in our tests.

• Long context: GPT-5.4 Nano 5 vs Ministral 4 — GPT-5.4 Nano wins. Nano is tied for 1st with 36 others on long context, indicating superior retrieval accuracy past 30K tokens in our scenarios.

• Safety calibration: GPT-5.4 Nano 3 vs Ministral 1 — GPT-5.4 Nano wins. Nano ranks 10 of 55 (two models share this score), so it better balances refusing harmful requests while permitting legitimate ones in our testing.

• Agentic planning: GPT-5.4 Nano 4 vs Ministral 3 — GPT-5.4 Nano wins. Nano ranks 16 of 54, showing stronger goal decomposition and failure recovery across our agentic tasks.

• Multilingual: GPT-5.4 Nano 5 vs Ministral 4 — GPT-5.4 Nano wins. Nano is tied for 1st with 34 others (out of 55), so non-English outputs are higher quality in our benchmarks.

• Classification: GPT-5.4 Nano 3 vs Ministral 4 — Ministral 3 14B 2512 wins. Ministral is tied for 1st in classification ("tied for 1st with 29 other models out of 53 tested"), so it is the better low-cost choice for routing and categorization tasks in our tests.

• Ties (no clear winner in our suite): constrained rewriting (both 4), creative problem solving (both 4), tool calling (both 4), faithfulness (both 4), persona consistency (both 5). For example, both models scored 4 on tool calling and are tied at rank 18 of 54, meaning they perform similarly on function selection and argument accuracy in our scenarios.

• External math benchmark: Beyond our internal 1–5 tests, GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), which supports its strength on harder quantitative tasks relative to Ministral in our data (Ministral has no AIME score in the payload).

Practical interpretation: GPT-5.4 Nano gives measurable advantages for tasks needing long context, strict structured outputs, multilingual fidelity, strategic reasoning and safer refusals. Ministral 3 14B 2512 is cheaper and wins classification in our tests, making it a pragmatic choice where per-token cost or classification accuracy under tight budgets matters.

Payload prices: GPT-5.4 Nano input $0.20/mtok and output $1.25/mtok; Ministral 3 14B 2512 input $0.20/mtok and output $0.20/mtok. Price ratio is 6.25× on output (payload's priceRatio). Assuming the common convention that 'mtok' = 1,000 tokens, round-trip (input+output) costs per million tokens are: GPT-5.4 Nano = ($0.20+$1.25)*1000 = $1,450/M tokens; Ministral = ($0.20+$0.20)*1000 = $400/M tokens. Output-only costs per million: GPT-5.4 Nano $1,250/M; Ministral $200/M. At scale that matters: 1M tokens/month → $1,450 vs $400 (difference $1,050); 10M → $14,500 vs $4,000 (difference $10,500); 100M → $145,000 vs $40,000 (difference $105,000). Who should care: startups and high-volume API customers with heavy output tokens (batch generation, user-facing chat at scale) will feel the gap; research or feature-flag experiments at low volume may prefer GPT-5.4 Nano's performance despite the cost.

Choose GPT-5.4 Nano if you need best-in-class long-context retrieval, strict schema/JSON outputs, multilingual support, stronger strategic reasoning, or higher AIME math performance in our testing — and you can absorb ~6.25× higher output costs. Choose Ministral 3 14B 2512 if you need a much lower-cost model for high-volume output, classification/routing at scale, or budget-constrained production where its 4/5 classification score and $0.20 output rate (payload) materially reduce monthly bills.