GPT-5.4 Nano vs Mistral Medium 3.1

Across our 12-test suite the models split results: GPT-5.4 Nano wins 3 tests (structured output 5 vs 4, creative problem solving 4 vs 3, safety calibration 3 vs 2), Mistral Medium 3.1 wins 3 tests (constrained rewriting 5 vs 4, classification 4 vs 3, agentic planning 5 vs 4), and 6 tests tie (strategic analysis, tool calling, faithfulness, long context, persona consistency, multilingual). Details and practical meaning: - Structured output (JSON/schema): GPT-5.4 Nano scores 5 and ranks tied for 1st (rank 1 of 54, tied with 24 others). That means Nano is more reliable at schema compliance for production APIs. - Constrained rewriting (tight character compression): Mistral scores 5 (rank tied for 1st) vs Nano 4 (rank 6), so Mistral is preferable when you must meet hard character limits. - Classification: Mistral 4 (tied for 1st) vs Nano 3 (rank 31 of 53), so Mistral is a better pick for routing/classification pipelines. - Agentic planning: Mistral 5 (tied for 1st) vs Nano 4 (rank 16), indicating better goal decomposition and failure recovery in our tests. - Creative problem solving: Nano 4 vs Mistral 3 — Nano produces more non-obvious, specific ideas in our suite. - Safety calibration: Nano 3 vs Mistral 2 — Nano is more likely to refuse harmful requests while permitting legitimate ones in our tests. - Ties important to many apps: both models score 5 on long context and tie for 1st (both rank tied for 1st with 36 others), but GPT-5.4 Nano has a larger context_window (400,000 tokens vs Mistral's 131,072), and Nano exposes max_output_tokens 128,000 — a real advantage for very long-document workflows. - External benchmark: Beyond our internal tests, GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), where it ranks 8th of 23 (sole holder), indicating strong structured math/problem performance in that external measure. Overall, Nano favors schema fidelity, creativity, safety calibration and extreme context; Mistral favors tight rewriting, classification, and agentic planning.

Costs in the payload are per mTok (per unit shown). GPT-5.4 Nano: input $0.20 / output $1.25 per mTok. Mistral Medium 3.1: input $0.40 / output $2.00 per mTok. Per 1,000 mToks = multiply by 1,000: per 1,000 mToks (1M tokens) that’s input $200 / output $1,250 for Nano and input $400 / output $2,000 for Mistral. Using a simple 50/50 input-output split as an example, cost per 1M tokens = Nano $725 vs Mistral $1,200. At 10M tokens (50/50) = Nano $7,250 vs Mistral $12,000. At 100M tokens = Nano $72,500 vs Mistral $120,000. The gap matters for high-throughput apps and startups: Nano is ~62.5% of Mistral’s price (priceRatio 0.625), so teams with heavy token volumes or tight budgets should favor GPT-5.4 Nano. Teams that process small volumes or require the specific wins Mistral shows may accept the higher spend.

Choose GPT-5.4 Nano if: - You run high-volume or latency-sensitive production (input $0.20/out $1.25 per mTok) and need reliable JSON/schema outputs (structured output 5). - You work with extremely long contexts (400k window) or require large max output tokens (128k). - You value lower monthly spend: Nano is ~62.5% of Mistral’s per-mTok price. Choose Mistral Medium 3.1 if: - You need the best constrained rewriting, classification, or agentic planning in our tests (each 5 vs Nano’s 4). - Your product pipeline depends on accurate routing or goal decomposition more than per-token cost. - Your use cases fit within a ~131k token window and you prioritize rewriting/agentic strengths over raw context size.