Gemini 2.5 Pro vs Ministral 3 14B 2512

Overview: In our 12-test suite Gemini 2.5 Pro wins 7 tests, Ministral 3 14B 2512 wins 1, and 4 tests tie. Below is a test-by-test readout with interpretation. Wins by Gemini (scores listed as Gemini → Ministral):

• structured_output 5 → 4 — Gemini is tied for 1st on structured_output ("JSON schema compliance and format adherence") in our rankings (tied for 1st with 24 others out of 54). Practically: Gemini is safer for strict schema outputs and data extraction tasks.
• creative_problem_solving 5 → 4 — Gemini tied for 1st (tied with 7 others). Expect more specific, feasible ideas in brainstorming and research prompts.
• tool_calling 5 → 4 — Gemini tied for 1st (tied with 16 others). In our tests Gemini better selected functions, arguments and sequencing, so it’s preferable for agentic/tool-driven workflows.
• faithfulness 5 → 4 — Gemini tied for 1st (tied with 32 others). For tasks needing to stick to source material (summaries, citations), Gemini is stronger in our evaluation.
• long_context 5 → 4 — Gemini is tied for 1st (tied with 36 others out of 55). For retrieval and memory at 30K+ tokens, Gemini retains accuracy better in our tests.
• agentic_planning 4 → 3 — Gemini ranks higher (rank 16 of 54) vs Ministral (rank 42 of 54). Expect better goal decomposition and failure-recovery planning from Gemini in our scenarios.
• multilingual 5 → 4 — Gemini tied for 1st (tied with 34 others). Our tests show higher parity in non-English outputs for Gemini. Win by Ministral:
• constrained_rewriting 3 → 4 — Ministral ranks much higher (rank 6 of 53, tied with many) vs Gemini (rank 31). For hard length-compression or tight character-limit rewrites, Ministral performs better in our testing. Ties (both models same score in our testing):
• strategic_analysis 4 = 4 (both rank 27 of 54); classification 4 = 4 (both tied for 1st with many models); safety_calibration 1 = 1 (both rank 32); persona_consistency 5 = 5 (both tied for 1st). Practical notes: classification and persona consistency are comparable between the two in our tests; both models struggle with safety calibration in our suite. External benchmarks (supplementary): In addition to our internal tests, Gemini 2.5 Pro scores 57.6% on SWE-bench Verified and 84.2% on AIME 2025 — both reported by Epoch AI. Per our rankings, that places Gemini at rank 10 of 12 on SWE-bench Verified and rank 11 of 23 on AIME 2025. Ministral 3 14B 2512 has no external SWE/AIME scores in the payload to compare. Overall implication: Gemini delivers stronger structured outputs, long-context handling, tool-calling, faithfulness and creative problem solving in our benchmarks; Ministral wins when tight-length rewriting and extreme cost efficiency matter.

Costs shown are per million tokens (mTok). Gemini 2.5 Pro: input $1.25/mTok, output $10.00/mTok. Ministral 3 14B 2512: input $0.20/mTok, output $0.20/mTok. Using a 50/50 input/output token split (explicit assumption): per 1M tokens Gemini = 0.5*$1.25 + 0.5*$10 = $5.625 (~$5.63); Ministral = 0.5*$0.20 + 0.5*$0.20 = $0.20. At scale: 1M tokens/month → Gemini $5.63 vs Ministral $0.20; 10M → Gemini $56.25 vs $2.00; 100M → Gemini $562.50 vs $20.00. If your workload is output-heavy (e.g., 10% input / 90% output), Gemini rises to ~$9.13 per 1M while Ministral remains $0.20. The ~50x priceRatio in the payload means cost-conscious teams (high-volume chat, consumer apps, or start-ups) should prefer Ministral 3 14B 2512; teams needing the higher fidelity in our benchmarks should budget for Gemini’s higher per-token expense.

Choose Gemini 2.5 Pro if you need: high-quality structured JSON outputs, reliable long-context retrieval (30K+ tokens), stronger tool calling/agent planning, better faithfulness and multilingual parity — examples: advanced code assistants that must call tools and return validated JSON, research workflows requiring sustained context, or enterprise automation where accuracy justifies higher cost. Choose Ministral 3 14B 2512 if you need: the lowest inference cost at scale (payload shows ~$0.20 vs ~$5.63 per 1M under a 50/50 split), better constrained_rewriting performance, or a budget-first production stack for high-volume consumer apps.