Grok 3 vs Ministral 3 14B 2512

Across our 12-test suite, Grok 3 wins 7 benchmarks, Ministral 3 14B 2512 wins 2, and they tie on 3. Here's the test-by-test breakdown:

Grok 3 wins:

• Strategic analysis (5 vs 4): Grok 3 ties for 1st among 54 models; Ministral 3 14B 2512 ranks 27th. For nuanced tradeoff reasoning with real numbers, this is a meaningful gap.
• Faithfulness (5 vs 4): Grok 3 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 34th. Grok 3 is substantially more reliable at staying grounded in source material — critical for RAG and summarization tasks.
• Long context (5 vs 4): Grok 3 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 38th. Despite Ministral 3 14B 2512 having the larger context window, Grok 3 scores higher on retrieval accuracy at 30K+ tokens in our testing.
• Agentic planning (5 vs 3): This is the widest gap in the comparison. Grok 3 ties for 1st among 54 models; Ministral 3 14B 2512 ranks 42nd. For autonomous, multi-step workflows — goal decomposition, failure recovery — Grok 3 is significantly more capable in our tests.
• Multilingual (5 vs 4): Grok 3 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 36th.
• Structured output (5 vs 4): Grok 3 ties for 1st among 54 models; Ministral 3 14B 2512 ranks 26th. JSON schema compliance and format adherence matter for API-driven pipelines.
• Safety calibration (2 vs 1): Neither model scores well here — both sit below the p75 of 2 in the broader field. Grok 3 ranks 12th of 55; Ministral 3 14B 2512 ranks 32nd. This is a weak area for both.

Ministral 3 14B 2512 wins:

• Creative problem solving (4 vs 3): Ministral 3 14B 2512 ranks 9th of 54; Grok 3 ranks 30th. For generating non-obvious, specific, feasible ideas, Ministral 3 14B 2512 outperforms in our testing.
• Constrained rewriting (4 vs 3): Ministral 3 14B 2512 ranks 6th of 53; Grok 3 ranks 31st. Compression tasks with hard character limits favor Ministral 3 14B 2512.

Ties (both score the same):

• Tool calling (4 vs 4): Both rank 18th of 54 — identical performance on function selection and argument accuracy.
• Classification (4 vs 4): Both tied for 1st among 53 models — strong and equivalent.
• Persona consistency (5 vs 5): Both tied for 1st among 53 models — no daylight between them here.

Neither model has external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) available in our data at this time.

The pricing gap between these two models is one of the widest we track. Grok 3 costs $3.00/M input and $15.00/M output tokens. Ministral 3 14B 2512 costs $0.20/M for both input and output.

At 1M output tokens/month: Grok 3 runs $15.00 vs Ministral 3 14B 2512's $0.20 — a $14.80 difference that's trivial for a serious project.

At 10M output tokens/month: $150 vs $2.00. The gap starts to matter for product teams watching margins.

At 100M output tokens/month: $1,500 vs $20.00. At this scale, Ministral 3 14B 2512 delivers $1,480/month in savings — material budget for most teams.

Who should care: High-volume production workloads (document processing pipelines, customer-facing chat, classification at scale) should weigh whether Grok 3's benchmark advantages justify the premium. For low-volume or exploratory use, the $14.80/month difference is a non-issue. Note also that Ministral 3 14B 2512 has a larger context window (262,144 tokens vs 131,072), which can reduce chunking overhead and associated costs in long-document workflows.

Choose Grok 3 if:

• You're building agentic or autonomous pipelines where goal decomposition and failure recovery matter (5 vs 3 on agentic planning in our tests)
• Your application relies heavily on RAG, summarization, or document grounding — Grok 3 scores 5 vs 4 on faithfulness and ranks 1st of 55 in our testing
• You process long documents and need high retrieval accuracy at 30K+ tokens
• Structured output (JSON schemas, API responses) is a core requirement — Grok 3 scores 5 vs 4 and ranks 1st of 54
• Multilingual quality is important at scale
• Volume is low enough that the $15.00/M output cost is acceptable

Choose Ministral 3 14B 2512 if:

• You need image input alongside text — Ministral 3 14B 2512 supports text+image input; Grok 3 does not per the data
• You're running high-volume workloads where $0.20/M output tokens vs $15.00/M is a material budget factor
• Creative ideation, brainstorming, or concept generation is your primary use case (ranks 9th vs 30th on creative problem solving)
• You write copy, headlines, or content under strict character constraints (ranks 6th vs 31st on constrained rewriting)
• You need a larger context window — 262,144 tokens vs 131,072
• You want to add repetition_penalty control to your prompting strategy (a parameter Ministral 3 14B 2512 supports that Grok 3 does not)