Gemini 2.5 Pro vs Ministral 3 8B 2512

Head-to-head on our 12-test suite (scores from the payload):

• Gemini 2.5 Pro wins (per winLossTie): structured_output 5 vs 4, strategic_analysis 4 vs 3, creative_problem_solving 5 vs 3, tool_calling 5 vs 4, faithfulness 5 vs 4, long_context 5 vs 4, agentic_planning 4 vs 3, multilingual 5 vs 4. These wins include top-ranked placements: long_context (Gemini tied for 1st out of 55), structured_output (tied for 1st of 54), faithfulness (tied for 1st of 55), and tool_calling (tied for 1st of 54). Practically, that means Gemini is better at retrieval and accuracy over 30k+ contexts, producing strict JSON/schema outputs, following source material without hallucination, and selecting/sequencing functions.
• Ministral 3 8B 2512 wins constrained_rewriting 5 vs 3 (tied for 1st of 53). If your workload requires tight compression or strict character-limit rewriting, Ministral is measurably better there.
• Ties: classification (4/4), safety_calibration (1/1), persona_consistency (5/5). Both models match on classification and persona retention in our tests; both scored poorly on safety calibration (score 1).
• External benchmarks (supplementary): Gemini 2.5 Pro scores 57.6% on SWE-bench Verified and 84.2% on AIME 2025 (Epoch AI), which supports its relative strength on coding/math tasks in public third-party measures. Ministral 3 8B 2512 has no external scores in the payload. Overall, Gemini wins the majority of tests (8 of 12) and ranks substantially higher on core developer-facing capabilities like long-context retrieval and tool calling, while Ministral’s single clear win is constrained rewriting.

Raw per-1k-token pricing from the payload: Gemini 2.5 Pro charges $1.25 input / $10 output per 1k tokens; Ministral 3 8B 2512 charges $0.15 input / $0.15 output per 1k tokens. At 1M tokens/month (1,000 thousand-token units):

• If all tokens are output: Gemini = 1,000 * $10 = $10,000; Ministral = 1,000 * $0.15 = $150.
• If tokens split 50/50 input vs output: Gemini = (500*$1.25)+(500*$10) = $5,625; Ministral = (500*$0.15)+(500*$0.15) = $150. At 10M tokens/month (10,000 units): output-only Gemini = $100,000 vs Ministral = $1,500; 50/50 Gemini ≈ $56,250 vs Ministral = $1,500. At 100M tokens: output-only Gemini = $1,000,000 vs Ministral = $15,000; 50/50 Gemini ≈ $562,500 vs Ministral = $15,000. The payload’s priceRatio is 66.67 (Gemini output price is ~66.7× Ministral’s). Teams with millions of monthly tokens or tight budgets should prioritize Ministral 3 8B 2512; teams that need Gemini’s higher task scores should budget for substantially higher costs.

Choose Gemini 2.5 Pro if you need: high-fidelity, long-context workflows, strict structured outputs (JSON/schema), reliable faithfulness, tool-calling accuracy, or multilingual parity — our tests show Gemini wins 8/12 benchmarks and ranks tied for 1st in long_context, structured_output, faithfulness, and tool_calling. Budget accordingly: Gemini’s output price is $10 per 1k tokens. Choose Ministral 3 8B 2512 if you need: a dramatically lower-cost model for high-volume usage, or superior constrained-rewriting/compression (Ministral wins that test). Ministral is appropriate for large-scale chat, vision-to-text, or cost-sensitive inference at $0.15 per 1k tokens.