GPT-5.4 vs Ministral 3 8B 2512

GPT-5.4 wins 8 of 12 internal benchmarks in our testing. Here's what each score gap actually means:

Agentic Planning (GPT-5.4: 5 vs Ministral 3 8B 2512: 3): GPT-5.4 is tied for 1st among 54 models; Ministral 3 8B 2512 ranks 42nd of 54. For multi-step workflows — decomposing goals, recovering from failures, orchestrating tools — this is a meaningful gap. If you're building autonomous agents, GPT-5.4 is substantially better in our tests.

Strategic Analysis (5 vs 3): GPT-5.4 ties for 1st of 54; Ministral 3 8B 2512 sits 36th. Nuanced tradeoff reasoning with real numbers is where GPT-5.4 separates from the smaller model — relevant for financial analysis, business decisions, and research synthesis.

Safety Calibration (5 vs 1): GPT-5.4 is among just 5 models that score 5/5 in our testing; Ministral 3 8B 2512 ranks 32nd of 55, scoring 1/5. This is the widest gap in the comparison. Safety calibration measures appropriate refusals of harmful requests while permitting legitimate ones — a critical differentiator for consumer-facing and regulated applications.

Faithfulness (5 vs 4): Both are solid, but GPT-5.4 ties for 1st of 55 while Ministral 3 8B 2512 ranks 34th. For RAG pipelines and summarization where hallucination is costly, GPT-5.4 has the edge.

Long Context (5 vs 4): GPT-5.4 ties for 1st of 55 and supports a 1,050,000-token context window; Ministral 3 8B 2512 ranks 38th of 55 with a 262,144-token window. Both can handle substantial context, but for retrieval accuracy deep into long documents, GPT-5.4 performs better in our tests.

Structured Output (5 vs 4): GPT-5.4 ties for 1st of 54; Ministral 3 8B 2512 ranks 26th of 54. JSON schema compliance favors GPT-5.4, though Ministral 3 8B 2512's score is still above the median.

Multilingual (5 vs 4): GPT-5.4 ties for 1st of 55; Ministral 3 8B 2512 ranks 36th. Non-English output quality is consistently better with GPT-5.4 in our testing.

Tool Calling (4 vs 4) — Tied: Both rank 18th of 54, sharing the score with 28 other models. Function selection and argument accuracy are equivalent between these two.

Persona Consistency (5 vs 5) — Tied: Both tie for 1st of 53 alongside 36 other models. No differentiation here.

Constrained Rewriting (4 vs 5) — Ministral 3 8B 2512 wins: Ministral 3 8B 2512 ties for 1st with 4 other models; GPT-5.4 ranks 6th. For tasks requiring compression within hard character limits — ad copy, metadata, short-form content — Ministral 3 8B 2512 is marginally better in our tests.

Classification (3 vs 4) — Ministral 3 8B 2512 wins: Ministral 3 8B 2512 ties for 1st of 53; GPT-5.4 ranks 31st. For routing, categorization, and labeling at scale, Ministral 3 8B 2512 is the better — and dramatically cheaper — choice.

Creative Problem Solving (4 vs 3): GPT-5.4 ranks 9th of 54; Ministral 3 8B 2512 ranks 30th. GPT-5.4 generates more non-obvious, feasible ideas in our testing.

External Benchmarks (Epoch AI): GPT-5.4 scores 76.9% on SWE-bench Verified (rank 2 of 12 models tested) and 95.3% on AIME 2025 (rank 3 of 23). These place it among the top coding and math models by those third-party measures. Ministral 3 8B 2512 has no external benchmark scores in the payload — not a weakness by itself, but the comparison can't be made directly.

The pricing gap here is stark. GPT-5.4 runs $2.50 input / $15.00 output per million tokens. Ministral 3 8B 2512 charges a flat $0.15 for both input and output — 100x cheaper on output.

At 1M output tokens/month: GPT-5.4 costs $15.00; Ministral 3 8B 2512 costs $0.15. The difference is barely noticeable.

At 10M output tokens/month: GPT-5.4 costs $150; Ministral 3 8B 2512 costs $1.50. GPT-5.4 is now a meaningful line item.

At 100M output tokens/month: GPT-5.4 costs $1,500; Ministral 3 8B 2512 costs $15. At this scale, the $1,485 monthly difference demands justification — you need GPT-5.4's capabilities to be mission-critical.

Developers running classification pipelines, summarization jobs, or any task where Ministral 3 8B 2512's scores are competitive should take the cost gap seriously. Consumer and enterprise users with complex reasoning, agentic, or multilingual workloads have the clearest case for absorbing GPT-5.4's premium.

Choose GPT-5.4 if:

• You're building agentic systems that require multi-step planning and failure recovery (scored 5 vs 3 in our tests)
• Safety calibration matters — consumer-facing apps, regulated industries, or brand-risk-sensitive deployments (5 vs 1)
• You need strong strategic reasoning or nuanced analysis (5 vs 3 on strategic analysis)
• Your workloads involve deep long-context retrieval (1M token window, 5/5 in our tests)
• Multilingual quality is important and degradation in non-English is unacceptable
• You need top-tier coding capability — GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI), ranking 2nd of 12 models tested
• Budget is not a primary constraint, or volume is low enough that the 100x price gap is immaterial

Choose Ministral 3 8B 2512 if:

• You're running high-volume classification or routing pipelines where it scores 4/5 (tied for 1st of 53) vs GPT-5.4's 3/5
• Your primary task is constrained rewriting — ad copy, metadata, headlines — where it scores 5/5 (tied for 1st) vs GPT-5.4's 4/5
• Cost is a primary constraint: at $0.15/$0.15 per million tokens vs $2.50/$15.00, the savings at 10M+ output tokens/month are substantial
• Tool calling is your core need and you don't need GPT-5.4's other capabilities — both score 4/5 in our tests
• You want vision-capable text generation at a fraction of frontier model pricing