GPT-4o-mini vs Mistral Small 3.2 24B

Across our 12-test suite there is no single majority winner: GPT-4o-mini wins 3 tests, Mistral Small 3.2 24B wins 3, and the remaining 6 are ties. Detailed breakdown (scores are our 1–5 internal ratings unless noted):

• GPT-4o-mini wins classification (4 vs 3). GPT-4o-mini is tied for 1st in classification ranking (tied with 29 others out of 53); Mistral ranks 31/53. This matters for routing, label prediction, and intent classification in production assistants.
• GPT-4o-mini wins safety calibration (4 vs 1). It ranks 6/55 vs Mistral’s 32/55 — meaning GPT-4o-mini is much more likely to decline harmful requests and reliably permit legitimate ones in our tests.
• GPT-4o-mini wins persona consistency (4 vs 3). GPT-4o-mini ranks 38/53 vs Mistral 45/53, so GPT-4o-mini better preserves character and resists prompt injections in dialogue tasks.
• Mistral Small 3.2 24B wins constrained rewriting (4 vs 3). Mistral ranks 6/53 vs GPT-4o-mini’s 31/53 — critical for strict length-limited transformations and on-device summarizers.
• Mistral wins faithfulness (4 vs 3). Mistral ranks 34/55 vs GPT-4o-mini’s 52/55, indicating Mistral is less likely to hallucinate against source material in our tests.
• Mistral wins agentic planning (4 vs 3). Mistral ranks 16/54 vs GPT-4o-mini 42/54 — relevant for multi-step task decomposition and recovery logic.
• Ties (identical scores): structured output (4/4), strategic analysis (2/2), creative problem solving (2/2), tool calling (4/4), long context (4/4), multilingual (4/4). For example, both score 4 on tool calling and rank 18/54 (tied with many models), so function selection and argument accuracy are comparable.
• External math benchmarks: Beyond our internal tests, GPT-4o-mini reports 52.6% on MATH Level 5 and 6.9% on AIME 2025 (Epoch AI). Mistral Small 3.2 24B has no external math scores in the payload. These external figures suggest GPT-4o-mini has measurable but limited performance on high-difficulty math tasks in Epoch AI’s datasets. Implications: GPT-4o-mini is preferable where safety, robust classification, and persona hold are priorities; Mistral is preferable where faithful adherence to sources, strict rewriting constraints, and planning are required. For neutral tasks (structured output, tool calling, long-context retrieval, multilingual output) both models perform similarly in our suite.

GPT-4o-mini charges $0.15 per input mTok and $0.60 per output mTok; Mistral Small 3.2 24B charges $0.075 input / $0.20 output per mTok (price ratio = 3×). Assuming a 50/50 input/output token split: at 1M tokens/month GPT-4o-mini costs $375 vs Mistral $137.50 (difference $237.50). At 10M tokens: GPT-4o-mini $3,750 vs Mistral $1,375 (difference $2,375). At 100M tokens: GPT-4o-mini $37,500 vs Mistral $13,750 (difference $23,750). Output-heavy workloads amplify the gap because GPT-4o-mini’s $0.60 output rate is the dominant cost. Teams with high volume APIs, consumer-scale apps, or tight margins should strongly prefer Mistral to reduce monthly spend; teams that prioritize safety, classification, or persona consistency at lower scale may accept GPT-4o-mini’s premium.

Choose GPT-4o-mini if you need stronger safety calibration, classification, and persona consistency for assistants, and you can absorb higher token costs (input $0.15 / output $0.60 per mTok). Choose Mistral Small 3.2 24B if you need better faithfulness, constrained rewriting, or agentic planning at scale and want a much lower price ($0.075 / $0.20 per mTok) — especially for high-volume APIs or cost-sensitive production systems. If your workload is balanced across structured output, tool calling, long-context, or multilingual tasks, Mistral delivers near-equal capability at ~1/3 the cost.