GPT-5.4 vs Ministral 3 14B 2512

Our 12-test internal benchmark suite shows GPT-5.4 winning 7 tests, Ministral 3 14B 2512 winning 1, and the two tying on 4.

Where GPT-5.4 wins clearly:

• Agentic planning (5 vs 3): GPT-5.4 ties for 1st among 54 models tested; Ministral 3 14B 2512 ranks 42nd. This is a meaningful gap for multi-step AI workflows and autonomous task execution.
• Strategic analysis (5 vs 4): GPT-5.4 ties for 1st among 54 models; Ministral 3 14B 2512 ranks 27th. Nuanced tradeoff reasoning with real numbers is a clear GPT-5.4 strength.
• Faithfulness (5 vs 4): GPT-5.4 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 34th. For summarization, RAG pipelines, and document-grounded tasks, GPT-5.4 hallucinates less frequently in our tests.
• Long context (5 vs 4): GPT-5.4 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 38th. This tracks with GPT-5.4's 1,050,000-token context window vs Ministral 3 14B 2512's 262,144 tokens.
• Safety calibration (5 vs 1): GPT-5.4 ties for 1st among 55 models (only 5 models reach this score); Ministral 3 14B 2512 ranks 32nd. This is the largest single-test gap and matters for any deployment with compliance or safety requirements.
• Structured output (5 vs 4): GPT-5.4 ties for 1st among 54 models; Ministral 3 14B 2512 ranks 26th. JSON schema compliance and format adherence are stronger with GPT-5.4.
• Multilingual (5 vs 4): GPT-5.4 ties for 1st among 55 models; Ministral 3 14B 2512 ranks 36th. Non-English output quality is noticeably higher with GPT-5.4 in our testing.

Where Ministral 3 14B 2512 wins:

• Classification (4 vs 3): Ministral 3 14B 2512 ties for 1st among 53 models (shared with 29 others); GPT-5.4 ranks 31st. For routing and categorization tasks, Ministral 3 14B 2512 actually outperforms GPT-5.4 in our tests — a surprising result worth noting.

Ties (both models equal):

• Tool calling (both 4): Both rank 18th of 54 in this score tier. Neither model dominates on function selection and argument accuracy.
• Constrained rewriting (both 4): Both rank 6th of 53 in their score tier. Compression tasks are a wash.
• Creative problem solving (both 4): Both rank 9th of 54. Non-obvious ideation is comparable.
• Persona consistency (both 5): Both tie for 1st among 53 models. Character maintenance is equally strong.

External benchmarks (Epoch AI):

GPT-5.4 scores 76.9% on SWE-bench Verified (real GitHub issue resolution), ranking 2nd of 12 models in our dataset on that measure. It also scores 95.3% on AIME 2025 (math olympiad), ranking 3rd of 23 models. These scores place GPT-5.4 above the field medians of 70.8% and 83.9% respectively. Ministral 3 14B 2512 does not have external benchmark scores in our dataset, so a direct external comparison cannot be made.

The pricing gap here is extreme. GPT-5.4 costs $2.50/MTok input and $15.00/MTok output. Ministral 3 14B 2512 costs $0.20/MTok for both input and output — a 12.5x gap on input and a 75x gap on output.

At real-world volumes, that math is stark:

• 1M output tokens/month: GPT-5.4 costs $15.00; Ministral 3 14B 2512 costs $0.20.
• 10M output tokens/month: GPT-5.4 costs $150.00; Ministral 3 14B 2512 costs $2.00.
• 100M output tokens/month: GPT-5.4 costs $1,500.00; Ministral 3 14B 2512 costs $20.00.

Developers running production workloads at scale will feel this immediately. A pipeline generating 100M output tokens monthly would save roughly $1,480 every month by choosing Ministral 3 14B 2512 — assuming the quality tradeoff is acceptable for the use case. Consumer or low-volume users won't feel the difference as acutely, but even at 1M tokens the 75x gap is hard to ignore. GPT-5.4's pricing is only justified when the benchmark advantages — particularly in agentic workflows, safety, and long-context tasks — are genuinely necessary.

Choose GPT-5.4 if:

• You're building agentic or multi-step AI workflows that require reliable goal decomposition and failure recovery (scored 5, ranked tied 1st of 54 in our tests).
• Safety calibration is non-negotiable — compliance-sensitive deployments, consumer-facing products, or anything requiring a model that refuses harmful requests while permitting legitimate ones (scored 5, tied 1st of 55).
• Your application relies on long-context retrieval at 30K+ tokens or uses a context window beyond 262K tokens (GPT-5.4 supports up to 1,050,000 tokens).
• You need the strongest multilingual output quality or high faithfulness in document-grounded tasks like RAG.
• Cost is secondary to raw benchmark performance and you're comfortable paying $15.00/MTok on output.

Choose Ministral 3 14B 2512 if:

• You're running high-volume classification, routing, or categorization pipelines — it ties for 1st of 53 models on our classification test and actually outperforms GPT-5.4 on that task.
• Budget is the primary constraint. At $0.20/MTok flat, it costs 75x less on output than GPT-5.4 and delivers competitive scores on tool calling, constrained rewriting, creative problem solving, and persona consistency.
• Your workload doesn't require frontier-level agentic planning or safety calibration.
• You need text and image input support at a price point that makes large-scale deployment viable.