Grok 4.20 vs Ministral 3 3B 2512

We ran the two models across 12 internal tests and compared scores and rankings. Summary: Grok 4.20 wins 8 tests, Ministral 3 3B 2512 wins 1, and 3 tests tie. Detailed walk-through: - Tool calling: Grok 4.20 = 5 vs Ministral = 4. Grok ties for 1st in our suite (tied with 16 others), so it’s stronger at function selection, argument accuracy, and sequencing—important for agentic tool workflows. - Long_context: Grok 4.20 = 5 vs Ministral = 4. Grok ties for 1st with 36 others, so it’s better for retrieval and reasoning across 30K+ tokens. - Strategic_analysis: Grok 4.20 = 5 vs Ministral = 2. Grok ranks tied for 1st; Ministral ranks 44 of 54—Grok handles nuanced tradeoff reasoning and numeric analyses far better in our tests. - Structured_output: Grok 4.20 = 5 vs Ministral = 4. Grok ties for 1st, indicating stronger JSON/schema compliance and format adherence. - Persona_consistency: Grok 4.20 = 5 vs Ministral = 4. Grok ties for 1st, so it resists injection and keeps consistent character. - Creative_problem_solving: Grok 4.20 = 4 vs Ministral = 3. Grok ranks higher (rank 9 vs rank 30), producing more specific, feasible ideas in our tasks. - Agentic_planning: Grok 4.20 = 4 vs Ministral = 3. Grok’s planning and failure-recovery are superior in our tests (rank 16 vs 42). - Multilingual: Grok 4.20 = 5 vs Ministral = 4, and Grok ties for 1st (with 34 others), so non-English parity favors Grok. - Constrained_rewriting: Ministral 3 3B 2512 = 5 vs Grok 4.20 = 4. Ministral ties for 1st here (with 4 others), making it the better pick for tight character-limited compression tasks. - Faithfulness: tie at 5/5 — both models score top marks and tie for 1st in faithfulness in our testing. - Classification: tie at 4/4 — both tie for 1st in classification accuracy. - Safety_calibration: tie at 1/1 — both models rank similarly low by this metric in our suite (rank 32 of 55). Practical meaning: choose Grok where reliable tool use, long documents, multilingual output, and complex reasoning matter. Choose Ministral when you need maximal cost efficiency and best-in-class constrained rewriting.

Costs are radically different. Pricing in the payload is per 1,000 tokens (mTok). Using a 50/50 input/output token split as a practical example: for 1M tokens/month (1,000 mTok) that’s 500 mTok input + 500 mTok output. Grok 4.20: input $2·500 = $1,000; output $6·500 = $3,000; total ≈ $4,000/month. Ministral 3 3B 2512: input $0.1·500 = $50; output $0.1·500 = $50; total ≈ $100/month. At 10M tokens/month (5,000 mTok each): Grok ≈ $40,000/month; Ministral ≈ $1,000/month. At 100M tokens/month (50,000 mTok each): Grok ≈ $400,000/month; Ministral ≈ $10,000/month. Startups, high-throughput services, and any app with >10M tokens/month should care deeply about this gap; Grok’s quality can justify the cost for mission-critical agents, but Ministral enables orders-of-magnitude cheaper scale.

Choose Grok 4.20 if you need top-tier tool calling, long-context retrieval, strategic analysis, structured outputs, and strong persona consistency for mission-critical agents or enterprise workflows and can justify the cost ($6/mtok output). Choose Ministral 3 3B 2512 if your priority is operating cost: it delivers the best constrained rewriting results (5/5), reasonable structured output and vision-capable text->image handling, and runs at $0.1/mtok input and output—ideal for high-volume, budget-sensitive apps.