GPT-5.4 Mini vs Grok 3 Mini

Across our 12-test suite, GPT-5.4 Mini outscores Grok 3 Mini on 5 benchmarks, ties on 6, and loses on 1.

Where GPT-5.4 Mini wins:

• Structured output (5 vs 4): GPT-5.4 Mini scores at the top tier for JSON schema compliance and format adherence, tied for 1st among 54 models. Grok 3 Mini ranks 26th of 54 with a score of 4 — still solid, but a meaningful gap for applications that depend on strict schema enforcement.
• Strategic analysis (5 vs 3): GPT-5.4 Mini is tied for 1st among 54 models; Grok 3 Mini ranks 36th. A two-point gap on nuanced tradeoff reasoning is significant — this matters for research summaries, business case analysis, and multi-variable decision support.
• Agentic planning (4 vs 3): GPT-5.4 Mini ranks 16th of 54; Grok 3 Mini drops to 42nd. For goal decomposition and failure recovery in autonomous workflows, GPT-5.4 Mini is the better choice.
• Creative problem solving (4 vs 3): GPT-5.4 Mini ranks 9th of 54; Grok 3 Mini ranks 30th. Generating non-obvious, feasible ideas is a clear GPT-5.4 Mini strength.
• Multilingual (5 vs 4): GPT-5.4 Mini is tied for 1st among 55 models; Grok 3 Mini ranks 36th. For non-English deployments, this gap is operationally relevant.

Where Grok 3 Mini wins:

• Tool calling (5 vs 4): Grok 3 Mini is tied for 1st among 54 models; GPT-5.4 Mini ranks 18th. For function selection, argument accuracy, and sequencing in agentic or API-integrated pipelines, Grok 3 Mini has a genuine edge here.

Where they tie (both score equally):

• Faithfulness (5/5 each): Both tied for 1st among 55 models — neither hallucinates on source-grounded tasks.
• Long context (5/5 each): Both tied for 1st among 55 models — retrieval accuracy at 30K+ tokens is equivalent.
• Persona consistency (5/5 each): Both tied for 1st among 53 models.
• Classification (4/4 each): Both tied for 1st among 53 models.
• Constrained rewriting (4/4 each): Both rank 6th of 53.
• Safety calibration (2/2 each): Both rank 12th of 55. Neither model excels here — both sit at the median or below on refusing harmful requests while permitting legitimate ones. This is a known limitation of both and worth factoring in for safety-critical deployments.

The pattern is clear: GPT-5.4 Mini is the broader, more capable model across analytical and generative tasks. Grok 3 Mini's one outright win — tool calling — is a high-value category for agentic developers, and its accessible pricing makes it competitive for that specific use case.

GPT-5.4 Mini costs $0.75/M input tokens and $4.50/M output tokens. Grok 3 Mini costs $0.30/M input and $0.50/M output — a 2.5x input gap and a 9x output gap. In practice: at 1M output tokens/month, GPT-5.4 Mini costs $4.50 vs Grok 3 Mini's $0.50 — a $4 difference that barely registers. At 10M output tokens/month, that gap widens to $40 vs $5, still manageable for most teams. At 100M output tokens/month, the math becomes material: $450 vs $50, a $400/month swing. Enterprise pipelines generating hundreds of millions of tokens — think high-frequency API calls, document processing at scale, or agent loops with long outputs — will find Grok 3 Mini's pricing significantly more sustainable. Developers running occasional or moderate workloads will likely find GPT-5.4 Mini's broader benchmark wins worth the cost. Note that Grok 3 Mini uses reasoning tokens (per its quirks data), which may affect effective output costs depending on how reasoning traces are billed in your setup.

Choose GPT-5.4 Mini if: you need strong performance across strategic analysis, structured output, agentic planning, multilingual tasks, or creative work — and your output volume is under ~50M tokens/month where the cost premium is manageable. It accepts text, image, and file inputs, supports structured outputs and tool calling, and offers a 400K context window. It's the better general-purpose choice for enterprise use cases with diverse task demands.

Choose Grok 3 Mini if: your pipeline is dominated by tool calling or function-calling workflows (where it scores 5/5 and ranks 1st of 54 in our testing), you're operating at high token volumes where $4.00/M output cost difference adds up, or you need access to raw reasoning traces (supported via its include_reasoning parameter). Its 131K context window covers most real-world use cases, and at $0.50/M output tokens it's among the most cost-efficient options in the market for logic-focused tasks. Also note: if your use case is purely text-in/text-out, Grok 3 Mini's modality limitation is not a constraint.