GPT-5.4 Nano vs Mistral Small 4

Summary from our 12-test suite: GPT-5.4 Nano wins 5 tests (strategic analysis, constrained rewriting, classification, long context, safety calibration), ties 7 tests, and loses none. Specifics: - Long context: GPT-5.4 Nano scores 5 vs Mistral 4 and is tied for 1st with 36 others out of 55 in our rankings; Mistral ranks 38/55. This matters for RAG or retrieval tasks at 30K+ tokens — GPT-5.4 Nano is materially better. - Strategic analysis (nuanced tradeoffs): GPT-5.4 Nano 5 vs Mistral 4; Nano is tied for 1st of 54 models while Mistral ranks 27th — expect stronger numeric tradeoff reasoning on Nano. - Constrained rewriting (hard character limits): Nano 4 vs Mistral 3; Nano ranks 6/53 vs Mistral 31/53 — better for summarization into tight slots (SMS, meta tags). - Classification: Nano 3 vs Mistral 2; Nano ranks 31/53 vs Mistral 51/53 — routing and categorization will be more accurate on Nano in our tests. - Safety calibration: Nano 3 vs Mistral 2; Nano ranks 10/55 vs Mistral 12/55 — Nano refuses harmful content more reliably in our testing. Ties (no clear winner): structured output (both 5, tied for 1st), creative problem solving (both 4), tool calling (both 4, rank 18), faithfulness (both 4), persona consistency (both 5, tied for 1st), agentic planning (both 4), multilingual (both 5, tied for 1st). External benchmark: on AIME 2025 (Epoch AI) GPT-5.4 Nano scores 87.8% (rank 8 of 23, sole holder) — we include this as supplementary evidence of stronger math/olympiad performance; Mistral Small 4 has no AIME score in the payload. In practice these results mean GPT-5.4 Nano is the safer pick when you need long-context retrieval, nuanced numeric reasoning, tight-format rewriting, or stronger classification; Mistral matches GPT-5.4 Nano on structured formats, creativity, tool-calling, multilingual outputs, and persona preservation at a lower price.

Raw per-token rates from the payload: GPT-5.4 Nano input $0.20/mTok, output $1.25/mTok; Mistral Small 4 input $0.15/mTok, output $0.60/mTok. Per 1M tokens (1,000 mToks): GPT input $200, output $1,250; Mistral input $150, output $600. If you run a 1:1 input:output workload (1M in + 1M out): GPT-5.4 Nano costs ~$1,450/month vs Mistral ~$750/month — a $700 gap. At 10M in+out: GPT ~$14,500 vs Mistral ~$7,500 (gap $7,000). At 100M: GPT ~$145,000 vs Mistral ~$75,000 (gap $70,000). The models’ output-cost ratio is 2.08x (1.25/0.60) per mTok; for equal in/out usage the total spend is ~1.93x higher on GPT-5.4 Nano. Teams with millions of tokens per month (SaaS platforms, high-traffic assistants, large-scale batch jobs) should care; developers building low-volume prototypes will feel the quality gains more than the cost hit.

Choose GPT-5.4 Nano if you need: long-context retrieval (30K+ tokens), higher-ranked strategic analysis, constrained rewriting into tight length limits, better classification and stronger safety calibration — and you can absorb ~2x higher per-output-token costs. Choose Mistral Small 4 if you need: the same structured-output, creative problem-solving, tool-calling, multilingual and persona consistency quality at roughly half the output cost ($0.60 vs $1.25/mTok) — ideal for high-volume or budget-sensitive production.