GPT-4.1 vs Ministral 3 14B 2512

Head‑to‑head across our 12-test suite: GPT-4.1 wins 7 tests, Ministral 3 14B 2512 wins 1, and 4 are ties (winLossTie). Detailed callouts:

• Long-context: GPT-4.1 scores 5 vs Ministral 4. GPT-4.1 is tied for 1st in our rankings for long-context (tied with 36 others out of 55), which matters for retrieval and synthesis across 30K+ token inputs. Practically: use GPT-4.1 when you need accurate state/knowledge over million‑token windows (its context_window=1,047,576 vs Ministral's 262,144).
• Tool-calling: GPT-4.1 scores 5 vs Ministral 4 and is tied for 1st in tool calling ranking (tied with 16 others). That indicates better function selection, argument accuracy and sequencing in our tests — critical for agentic workflows and multi-step API usage.
• Faithfulness: GPT-4.1 5 vs Ministral 4; GPT-4.1 is tied for 1st with 32 others (out of 55). This shows GPT-4.1 sticks to source material more reliably in our testing.
• Strategic analysis & constrained rewriting: GPT-4.1 wins strategic analysis (5 vs 4) and constrained rewriting (5 vs 4). Rankings show GPT-4.1 tied for 1st on strategic analysis and constrained rewriting, useful for complex tradeoffs and tight character-limited outputs.
• Agentic planning: GPT-4.1 4 vs Ministral 3; GPT-4.1 ranks substantially higher (rank 16 of 54 vs 42 for Ministral) in decomposing goals and recovery plans.
• Multilingual and classification: GPT-4.1 wins multilingual (5 vs 4) and ties on classification (both score 4). GPT-4.1 is tied for 1st on multilingual in our tests. This favors multilingual applications.
• Creative problem solving: Ministral 3 wins (4 vs GPT-4.1's 3). Ministral ranks 9 of 54 on creative problem solving vs GPT-4.1 at 30 of 54 — a clear edge for idea generation and novel solutions in our suite.
• Ties: structured output, classification, safety calibration, and persona consistency are ties (both models matched on our 1–5 scale). Note safety calibration is low for both (score 1 each; rank 32 of 55).
• External benchmarks (supplementary): GPT-4.1 scores 48.5% on SWE-bench Verified, 83% on MATH Level 5, and 38.3% on AIME 2025 (these external figures are from Epoch AI). Ministral 3 has no external scores in the payload. On SWE-bench Verified GPT-4.1 ranks 11 of 12 in our recorded rankings, indicating it did not excel on that specific external coding test despite strong internal tool-calling and engineering capabilities. In short: GPT-4.1 demonstrates superior long-context, tool-calling, faithfulness, and planning in our tests; Ministral 3 is cheaper and better at creative problem solving.

Costs shown are per thousand tokens (mTok) in the payload. Assuming tokens are billed per 1,000: GPT-4.1 charges $2 input / $8 output per mTok; Ministral 3 14B 2512 charges $0.2 input / $0.2 output per mTok — a 40x priceRatio in the payload. At 1,000,000 tokens/month (1,000 mTok) with a 50/50 input-output split, GPT-4.1 would cost $2,000 (input) + $8,000 (output) = $10,000/month; Ministral 3 would cost $200 + $200 = $400/month. At 10M tokens/month GPT-4.1 ≈ $100,000 vs Ministral ≈ $4,000. At 100M tokens/month GPT-4.1 ≈ $1,000,000 vs Ministral ≈ $40,000. The cost gap matters for any business at scale (hundreds of millions of tokens)—Ministral radically reduces running costs. Teams that need the long‑context, tool integrations, or top-tier faithfulness should budget for GPT-4.1; cost‑sensitive products and high‑volume inference are where Ministral 3 provides strong ROI.

Choose GPT-4.1 if you need: high-fidelity long-context (GPT-4.1=5), robust tool-calling (5), top faithfulness (5), multilingual parity (5), or advanced agentic planning — and you can absorb higher per‑token costs ($2/$8 per mTok). Choose Ministral 3 14B 2512 if you need: a dramatic cost reduction ($0.2/$0.2 per mTok), strong creative problem solving (Ministral=4 vs GPT-4.1=3), and competent structured output/classification at large scale. Example use cases: pick GPT-4.1 for multi-step developer tooling, long-document analysis, and production agents; pick Ministral 3 for high-volume content generation, ideation, and budget-constrained deployments.