Grok 4.20 vs Ministral 3 14B 2512

Across our 12-test suite, Grok 4.20 outscores Ministral 3 14B 2512 on 7 tests, ties on 5, and loses none.

Tool Calling (5 vs 4): Grok 4.20 ties for 1st among 54 models (with 16 others); Ministral ranks 18th of 54. For agentic workflows requiring accurate function selection and argument sequencing, this gap is meaningful — a score of 4 vs 5 here can mean more failed tool calls requiring retries.

Faithfulness (5 vs 4): Grok 4.20 ties for 1st among 55 models (with 32 others); Ministral ranks 34th. In RAG pipelines or document summarization where sticking to source material matters, Grok 4.20's score signals fewer hallucinated details.

Strategic Analysis (5 vs 4): Grok 4.20 ties for 1st among 54 models (with 25 others); Ministral ranks 27th. Nuanced tradeoff reasoning with real numbers — relevant for business analysis, decision support, and research tasks.

Long Context (5 vs 4): Grok 4.20 ties for 1st among 55 models (with 36 others); Ministral ranks 38th of 55. Grok 4.20 also carries a 2M token context window vs Ministral's 262K — a practical advantage for very long document workloads. At 30K+ token retrieval tasks, Ministral's rank-38 position is a caution flag.

Agentic Planning (4 vs 3): Grok 4.20 ranks 16th of 54; Ministral ranks 42nd of 54. Goal decomposition and failure recovery — core to any autonomous agent — show a meaningful gap. A score of 3 on agentic planning (below the p50 of 4 across all models) is a real concern for agent-heavy use cases.

Multilingual (5 vs 4): Grok 4.20 ties for 1st among 55 models (with 34 others); Ministral ranks 36th. For non-English output quality, Grok 4.20 holds a measurable edge.

Structured Output (5 vs 4): Grok 4.20 ties for 1st among 54 models (with 24 others); Ministral ranks 26th. JSON schema compliance and format adherence matter directly in API integrations — Ministral's rank-26 position means it sits at roughly the median.

Ties (5 categories): Both models score identically on constrained rewriting (4), creative problem solving (4), classification (4), safety calibration (1), and persona consistency (5). Safety calibration is worth flagging: both models score 1/5, placing them both at rank 32 of 55 — below the p25 of 1, which reflects a challenge the entire field shares but that users of either model should account for in deployment.

No external benchmark scores (SWE-bench, AIME 2025, MATH Level 5) are present in the payload for either model, so we cannot supplement with those data points here.

The cost gap here is substantial and worth quantifying concretely. Grok 4.20 is priced at $2.00 input / $6.00 output per million tokens. Ministral 3 14B 2512 is $0.20 input / $0.20 output per million tokens — a 30x difference on output.

At 1M output tokens/month: Grok 4.20 costs $6.00 vs Ministral's $0.20 — a $5.80 difference, negligible for most teams.

At 10M output tokens/month: $60.00 vs $2.00 — a $58 gap, still modest.

At 100M output tokens/month: $600.00 vs $20.00 — a $580 monthly difference that starts to matter for budget-conscious operators.

At 1B output tokens/month (large-scale production): $6,000 vs $200 — a $5,800 difference that is a meaningful line item.

Developers running high-throughput pipelines — document processing, classification at scale, bulk summarization — should take Ministral 3 14B 2512's pricing seriously. Grok 4.20's premium is justified for workloads that directly leverage its stronger scores in tool calling, agentic planning, faithfulness, and long-context retrieval, where quality differences translate to fewer retries, less error handling, and better downstream outcomes.

Choose Grok 4.20 if:

• Your application depends on tool calling, agentic planning, or autonomous agent pipelines — Grok 4.20 scores 5 on tool calling (rank 1 of 54) vs Ministral's 4 (rank 18), and 4 on agentic planning (rank 16) vs Ministral's 3 (rank 42 of 54).
• You work with documents longer than 262K tokens — Grok 4.20's 2M token context window is the only option here.
• Faithfulness to source material is critical (RAG, legal summarization, compliance): Grok 4.20 scores 5/5 (rank 1 of 55) vs Ministral's 4/5 (rank 34).
• You need strong multilingual output or structured JSON compliance at the highest reliability tier.
• Volume is under ~10M output tokens/month, where the $5.80/M output premium is not a budget concern.

Choose Ministral 3 14B 2512 if:

• You are running high-volume, cost-sensitive workloads (classification, routing, bulk text processing) where the benchmark gaps in agentic planning, long context, and faithfulness do not directly affect your pipeline.
• Your context requirements fit within 262K tokens.
• You need the lowest viable cost at scale — $0.20/M output vs $6.00/M means Ministral is 30x cheaper, which at 100M+ monthly output tokens is a $580+ monthly savings.
• The five tied benchmarks (creative problem solving, constrained rewriting, classification, persona consistency) cover your core use cases — in those areas, you get equivalent quality at a fraction of the price.