GPT-5.4 Mini vs Ministral 3 8B 2512

Across our 12-test suite, GPT-5.4 Mini outscores Ministral 3 8B 2512 on 8 benchmarks, ties on 3, and loses 1.

Where GPT-5.4 Mini leads:

• Structured output (5 vs 4): GPT-5.4 Mini ties for 1st of 54 models; Ministral 3 8B 2512 ranks 26th. For JSON schema compliance and API-facing output pipelines, this is a meaningful gap.
• Strategic analysis (5 vs 3): GPT-5.4 Mini ties for 1st of 54; Ministral 3 8B 2512 ranks 36th. A full two-point gap on nuanced tradeoff reasoning — relevant for business analysis, evaluation tasks, and decision-support applications.
• Faithfulness (5 vs 4): GPT-5.4 Mini ties for 1st of 55; Ministral 3 8B 2512 ranks 34th. Lower hallucination risk when staying close to source material.
• Long context (5 vs 4): GPT-5.4 Mini ties for 1st of 55; Ministral 3 8B 2512 ranks 38th. GPT-5.4 Mini also holds a significant context window advantage at 400K tokens vs 262K.
• Multilingual (5 vs 4): GPT-5.4 Mini ties for 1st of 55; Ministral 3 8B 2512 ranks 36th. One tier difference — relevant for non-English deployments.
• Agentic planning (4 vs 3): GPT-5.4 Mini ranks 16th of 54; Ministral 3 8B 2512 ranks 42nd. For goal decomposition and multi-step workflows, GPT-5.4 Mini is substantially more capable.
• Creative problem solving (4 vs 3): GPT-5.4 Mini ranks 9th of 54; Ministral 3 8B 2512 ranks 30th. A one-point gap on ideation quality.
• Safety calibration (2 vs 1): GPT-5.4 Mini ranks 12th of 55; Ministral 3 8B 2512 ranks 32nd. Both score below the field median (p50: 2), but Ministral 3 8B 2512's score of 1 places it in the bottom quartile. Neither model excels here.

Where they tie:

• Tool calling (4 vs 4): Both rank 18th of 54 — identical scores and rankings. Equivalent for function-calling workflows.
• Classification (4 vs 4): Both tie for 1st of 53. Strong routing and categorization from both.
• Persona consistency (5 vs 5): Both tie for 1st of 53. Character maintenance under injection attempts is equally strong.

Where Ministral 3 8B 2512 wins:

• Constrained rewriting (5 vs 4): Ministral 3 8B 2512 ties for 1st of 53 with only 4 other models — a genuinely elite score. GPT-5.4 Mini ranks 6th. For tasks requiring compression within hard character limits, Ministral 3 8B 2512 is the better tool.

GPT-5.4 Mini costs $0.75 per million input tokens and $4.50 per million output tokens. Ministral 3 8B 2512 costs $0.15 per million tokens for both input and output — a flat, symmetric pricing model. That's a 5x gap on input and a 30x gap on output. In practice: at 1M output tokens/month, GPT-5.4 Mini costs $4.50 vs $0.15 for Ministral 3 8B 2512 — a $4.35 difference that's barely meaningful. At 10M output tokens/month, the gap widens to $43.50 vs $1.50 — still manageable for most teams. At 100M output tokens/month, GPT-5.4 Mini runs $450 vs Ministral 3 8B 2512's $15 — a $435 monthly delta that matters. For consumer apps, batch summarization, classification pipelines, or any workload generating hundreds of millions of tokens, Ministral 3 8B 2512's flat $0.15 rate makes the cost arithmetic compelling. Teams running agent loops with long outputs should model their monthly token counts carefully before defaulting to GPT-5.4 Mini.

Choose GPT-5.4 Mini if: you need strong performance on strategic analysis, long-context retrieval (up to 400K tokens), faithfulness to source material, or multilingual output — and your token volumes don't push monthly output costs into the hundreds of dollars. It also supports image and file inputs, reasoning parameters, and structured outputs with a higher benchmark ceiling across most task types.

Choose Ministral 3 8B 2512 if: your primary use case is constrained rewriting or text compression, you're running high-volume pipelines where the 30x output cost difference becomes significant (100M+ tokens/month), or your tasks fall in areas where both models score equally — tool calling, classification, persona consistency — and you'd rather optimize for price. Its $0.15 flat rate makes it one of the most cost-efficient options in the market for straightforward tasks that don't require top-tier reasoning or long-context retrieval.