GPT-5.4 Mini vs Mistral Small 4

Across our 12-test benchmark suite, GPT-5.4 Mini wins 5 tests, Mistral Small 4 wins 0, and they tie on 7.

Where GPT-5.4 Mini wins outright:

• Faithfulness (5 vs 4): GPT-5.4 Mini scores 5/5, tied for 1st among 55 tested models. Mistral Small 4 scores 4/5, ranking 34th of 55. In practice, this means GPT-5.4 Mini is more reliable at sticking to source material without hallucinating — critical for RAG systems, legal summaries, and any task where accuracy to a reference document matters.

• Classification (4 vs 2): This is the sharpest gap in the dataset. GPT-5.4 Mini scores 4/5, tied for 1st among 53 models. Mistral Small 4 scores 2/5, ranking 51st of 53 — near the bottom of all tested models. For routing, tagging, intent detection, or any classification-heavy pipeline, Mistral Small 4 is a poor choice based on our testing.

• Long Context (5 vs 4): GPT-5.4 Mini scores 5/5 (tied 1st of 55); Mistral Small 4 scores 4/5 (ranked 38th of 55). GPT-5.4 Mini also has a larger context window (400K vs 262K), compounding the advantage for long-document tasks.

• Strategic Analysis (5 vs 4): GPT-5.4 Mini scores 5/5 (tied 1st of 54); Mistral Small 4 scores 4/5 (ranked 27th of 54). For nuanced tradeoff reasoning with real numbers — business analysis, technical trade studies — GPT-5.4 Mini has a measurable edge.

• Constrained Rewriting (4 vs 3): GPT-5.4 Mini scores 4/5 (ranked 6th of 53); Mistral Small 4 scores 3/5 (ranked 31st of 53). For compression tasks with hard character limits — ad copy, UI strings, summarization under constraints — GPT-5.4 Mini is more reliable.

Where they tie (7 tests):

Both models score identically on structured output (5/5), creative problem solving (4/4), tool calling (4/4), safety calibration (2/2), persona consistency (5/5), agentic planning (4/4), and multilingual (5/5). Rankings are also identical on several of these — for example, both rank 18th of 54 on tool calling and 16th of 54 on agentic planning. For agentic workflows that don't lean heavily on classification or long-context retrieval, Mistral Small 4 is a cost-equivalent alternative.

Safety calibration is a notable shared weakness: both score 2/5, ranking 12th of 55 — below the field median of 2 but tied with 20 other models. Neither model stands out here.

Context window: GPT-5.4 Mini supports 400K tokens; Mistral Small 4 supports 262K. GPT-5.4 Mini also supports file inputs in addition to text and image, while Mistral Small 4 handles text and image only.

The pricing gap between these two models is substantial and should be a primary decision factor at scale. GPT-5.4 Mini is priced at $0.75 input / $4.50 output per million tokens. Mistral Small 4 comes in at $0.15 input / $0.60 output per million tokens — making output 7.5x cheaper.

At 1M output tokens/month: GPT-5.4 Mini costs $4.50 vs Mistral Small 4's $0.60 — a difference of $3.90. Barely noticeable.

At 10M output tokens/month: $45.00 vs $6.00 — a $39 gap. Still manageable for most teams.

At 100M output tokens/month: $450.00 vs $60.00 — a $390/month difference. At this volume, the performance wins of GPT-5.4 Mini need to directly translate into business value to justify the cost.

For applications where GPT-5.4 Mini's benchmark advantages in faithfulness, classification, and long-context handling are directly load-bearing — RAG pipelines, document triage, long-document summarization — the premium may be justified. For general chat, multilingual support, or agentic scaffolding where both models tied in our testing, Mistral Small 4 delivers equivalent results at a fraction of the cost. Context window is also a factor: GPT-5.4 Mini offers 400K tokens vs Mistral Small 4's 262K, which matters for long-document workloads even before factoring in the score difference.

Choose GPT-5.4 Mini if:

• Your application depends on classification accuracy (routing, tagging, intent detection) — Mistral Small 4 scored 2/5 and ranked 51st of 53 on this test in our suite.
• You're building RAG pipelines or document-grounded applications where faithfulness is critical — GPT-5.4 Mini scored 5/5 vs 4/5.
• Your workloads involve documents exceeding 262K tokens, or you need file input support.
• You need top-tier strategic analysis output and constrained rewriting for marketing or editorial workflows.
• Volume is under 10M output tokens/month and the $39 cost difference per 10M tokens is acceptable.

Choose Mistral Small 4 if:

• You're running high-volume workloads (10M+ output tokens/month) and classification is not a core function — the 7.5x output cost difference is real money at scale.
• Your use case is primarily multilingual support, persona-consistent chatbots, structured JSON output, or agentic tool-calling — all areas where both models tied in our testing.
• You need more sampling control — Mistral Small 4 exposes temperature, top_p, top_k, frequency_penalty, presence_penalty, and stop parameters, while GPT-5.4 Mini does not surface these in its supported parameters per the payload.
• You want an open API with a cost-efficient model for prototyping or production workloads where benchmark parity is sufficient.