GPT-5.4 Nano vs Ministral 3 3B 2512
GPT-5.4 Nano is the stronger AI for most tasks, winning 8 of 12 benchmarks in our testing — including strategic analysis (5 vs 2), long context (5 vs 4), agentic planning (4 vs 3), and structured output (5 vs 4). Ministral 3 3B 2512 wins on constrained rewriting (5 vs 4), faithfulness (5 vs 4), and classification (4 vs 3), making it a legitimate choice for content editing and document-grounded tasks. However, GPT-5.4 Nano's output cost of $1.25/MTok versus Ministral 3 3B 2512's $0.10/MTok represents a 12.5x price gap that high-volume applications will feel sharply.
openai
GPT-5.4 Nano
Benchmark Scores
External Benchmarks
Pricing
Input
$0.200/MTok
Output
$1.25/MTok
modelpicker.net
mistral
Ministral 3 3B 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.100/MTok
modelpicker.net
Benchmark Analysis
GPT-5.4 Nano wins 8 of 12 benchmarks in our testing, ties 1, and loses 3. Here's what the scores mean task by task:
Strategic Analysis (5 vs 2): The largest gap in this comparison. GPT-5.4 Nano ties for 1st of 54 models on nuanced tradeoff reasoning; Ministral 3 3B 2512 ranks 44th of 54. This difference is decisive for business analysis, scenario planning, or any task requiring structured reasoning about competing factors.
Long Context (5 vs 4): GPT-5.4 Nano ties for 1st of 55 on retrieval accuracy at 30K+ tokens; Ministral 3 3B 2512 ranks 38th of 55. GPT-5.4 Nano also offers a 400K token context window versus Ministral 3 3B 2512's 128K — a practical advantage for large document ingestion.
Structured Output (5 vs 4): GPT-5.4 Nano ties for 1st of 54 on JSON schema compliance; Ministral 3 3B 2512 ranks 26th of 54. For API-integrated workflows requiring reliable schema adherence, GPT-5.4 Nano is the safer choice.
Persona Consistency (5 vs 4): GPT-5.4 Nano ties for 1st of 53; Ministral 3 3B 2512 ranks 38th. Relevant for chatbot deployments and role-based AI applications.
Multilingual (5 vs 4): GPT-5.4 Nano ties for 1st of 55; Ministral 3 3B 2512 ranks 36th. A meaningful edge for multilingual applications.
Agentic Planning (4 vs 3): GPT-5.4 Nano ranks 16th of 54; Ministral 3 3B 2512 ranks 42nd of 54. Goal decomposition and failure recovery favor GPT-5.4 Nano for agentic workflows.
Creative Problem Solving (4 vs 3): GPT-5.4 Nano ranks 9th of 54; Ministral 3 3B 2512 ranks 30th of 54.
Safety Calibration (3 vs 1): GPT-5.4 Nano ranks 10th of 55 — one of only 2 models at this score. Ministral 3 3B 2512 ranks 32nd of 55 with the lowest possible score of 1, indicating it struggles to correctly balance refusals and permissions in our testing.
Tool Calling (4 vs 4): A tie — both models rank 18th of 54, sharing the score with 28 other models. Neither model differentiates here.
Constrained Rewriting (4 vs 5): Ministral 3 3B 2512 ties for 1st of 53 with 4 other models; GPT-5.4 Nano ranks 6th of 53. Ministral 3 3B 2512 is better at compressing text within hard character limits.
Faithfulness (4 vs 5): Ministral 3 3B 2512 ties for 1st of 55 on sticking to source material without hallucinating; GPT-5.4 Nano ranks 34th of 55. For RAG pipelines and summarization where source fidelity is critical, Ministral 3 3B 2512 has a real advantage.
Classification (3 vs 4): Ministral 3 3B 2512 ties for 1st of 53; GPT-5.4 Nano ranks 31st of 53. Routing and categorization tasks favor Ministral 3 3B 2512.
On third-party benchmarks, GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), ranking 8th of 23 models tested — placing it solidly in the upper tier for competition-level math. No AIME 2025 or other external benchmark scores are available for Ministral 3 3B 2512 in the payload.
Pricing Analysis
GPT-5.4 Nano is priced at $0.20/MTok input and $1.25/MTok output. Ministral 3 3B 2512 runs $0.10/MTok for both input and output — making it half the price on input and 12.5x cheaper on output. At 1M output tokens per month, GPT-5.4 Nano costs $1.25 vs Ministral 3 3B 2512's $0.10 — a gap of $1.15. That's negligible. Scale to 10M output tokens and the gap becomes $115/month; at 100M tokens, it's $1,150/month. For high-throughput applications — bulk document processing, real-time classification pipelines, or customer-facing chatbots handling millions of messages — Ministral 3 3B 2512's flat $0.10/MTok pricing is a meaningful operational advantage. For lower-volume use cases where quality on strategic reasoning, long-context retrieval, or agentic tasks is the priority, GPT-5.4 Nano's premium is easier to justify.
Real-World Cost Comparison
Bottom Line
Choose GPT-5.4 Nano if: You need strong strategic reasoning (5/5, tied for 1st of 54), reliable structured output for API workflows (5/5, tied for 1st of 54), long-context retrieval across large documents (5/5 with a 400K context window), multilingual output quality, or agentic planning tasks. The 12.5x output cost premium is justified when task quality directly impacts outcomes. Its 87.8% AIME 2025 score (Epoch AI) also makes it a credible choice for math-heavy applications.
Choose Ministral 3 3B 2512 if: Your workload centers on high-volume classification and routing (4/5, tied for 1st of 53), source-faithful summarization or RAG (5/5, tied for 1st of 55), or constrained rewriting tasks (5/5, tied for 1st of 53). At $0.10/MTok flat, it is dramatically cheaper — at 100M output tokens per month you save roughly $1,150 versus GPT-5.4 Nano. Its safety calibration score of 1/5 (rank 32 of 55) is a concern for consumer-facing deployments where harmful request handling matters.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.