GPT-4o-mini vs Grok 3 Mini

Across our 12-test benchmark suite, Grok 3 Mini wins 7 tests, GPT-4o-mini wins 1, and they tie on 4.

Where Grok 3 Mini wins:

• Tool calling: Grok 3 Mini scores 5/5, tied for 1st among 54 models (with 16 others). GPT-4o-mini scores 4/5, tied at rank 18. For agentic workflows where function selection, argument accuracy, and sequencing matter, Grok 3 Mini has a meaningful edge.
• Faithfulness: Grok 3 Mini scores 5/5, tied for 1st among 55 models (with 32 others). GPT-4o-mini scores only 3/5, ranking 52nd of 55 — near the bottom of all tested models. This is a substantial gap. In RAG applications, summarization, or any task where sticking to source material matters, GPT-4o-mini carries real hallucination risk relative to Grok 3 Mini.
• Persona consistency: Grok 3 Mini scores 5/5, tied for 1st among 53 models. GPT-4o-mini scores 4/5 at rank 38. Relevant for chatbot and character-driven applications.
• Long context: Grok 3 Mini scores 5/5, tied for 1st among 55 models. GPT-4o-mini scores 4/5 at rank 38. Both have similar context windows (~128K tokens), but Grok 3 Mini retrieves more accurately at 30K+ token depths in our testing.
• Strategic analysis: Grok 3 Mini scores 3/5 vs GPT-4o-mini's 2/5. Both are below the field median of 4/5, but Grok 3 Mini is less weak here. GPT-4o-mini ranks 44th of 54 on nuanced tradeoff reasoning.
• Creative problem solving: Grok 3 Mini scores 3/5 (rank 30 of 54) vs GPT-4o-mini's 2/5 (rank 47 of 54). GPT-4o-mini is in the bottom tier for generating non-obvious, specific, feasible ideas.
• Constrained rewriting: Grok 3 Mini scores 4/5 (rank 6 of 53) vs GPT-4o-mini's 3/5 (rank 31 of 53). Compressing content within hard character limits is meaningfully better on Grok 3 Mini.

Where GPT-4o-mini wins:

• Safety calibration: GPT-4o-mini scores 4/5, ranking 6th of 55 models (4 models share this score). Grok 3 Mini scores 2/5, ranking 12th of 55. GPT-4o-mini's safety calibration — refusing harmful requests while permitting legitimate ones — is considerably more reliable in our testing. This matters for consumer-facing products and regulated environments.

Ties (4 tests):

• Structured output (both 4/5, rank 26 of 54): JSON schema compliance is equivalent.
• Classification (both 4/5, tied for 1st among 53 models): Routing and categorization tasks are effectively equal.
• Agentic planning (both 3/5, rank 42 of 54): Both are below the field median of 4/5 here — neither excels at goal decomposition and failure recovery.
• Multilingual (both 4/5, rank 36 of 55): Non-English output quality is equivalent.

External benchmarks: GPT-4o-mini has external benchmark scores from Epoch AI. On MATH Level 5 (competition math), it scores 52.6% — ranking 13th of 14 models tested, well below the median of 94.15% among benchmarked models. On AIME 2025 (math olympiad), it scores 6.9% — ranking 21st of 23, below the median of 83.9%. These scores confirm that GPT-4o-mini is not suited for advanced mathematics. Grok 3 Mini does not have external benchmark scores in our dataset.

GPT-4o-mini charges $0.15/M input tokens and $0.60/M output tokens. Grok 3 Mini charges $0.30/M input and $0.50/M output. The direction of the price gap depends on your token mix.

For output-heavy workloads (e.g., long-form generation, reasoning traces): at 1M output tokens/month, GPT-4o-mini costs $0.60 vs Grok 3 Mini's $0.50 — Grok 3 Mini is actually cheaper. At 10M output tokens, that's $6.00 vs $5.00; at 100M output tokens, $60 vs $50. Grok 3 Mini saves you money at scale if your output volume dominates.

For input-heavy workloads (e.g., large document processing, RAG pipelines): at 100M input tokens/month, GPT-4o-mini costs $15 vs Grok 3 Mini's $30 — GPT-4o-mini is $15 cheaper. The input cost gap is 2x, so applications that process far more tokens than they generate should stick with GPT-4o-mini on price alone.

Note that Grok 3 Mini uses reasoning tokens (flagged in the payload), which can increase output token counts depending on how reasoning is configured. Factor this into cost estimates for reasoning-intensive tasks. The overall price ratio between these two models is only 1.2x, so for most use cases the cost difference is not the deciding factor — capability differences are.

Choose GPT-4o-mini if:

• You need multimodal inputs — it accepts text, images, and files; Grok 3 Mini is text-only per our data.
• Safety calibration is a hard requirement (scored 4/5 vs Grok 3 Mini's 2/5). Consumer-facing products, healthcare, education, or any regulated context should weigh this heavily.
• Your workload is heavily input-token-dominated (document ingestion, large RAG pipelines) and cost is a priority — GPT-4o-mini's $0.15/M input rate is half of Grok 3 Mini's $0.30/M.
• You need logit_bias, top_logprobs, or web_search_options parameters, which are in GPT-4o-mini's supported parameter list but not Grok 3 Mini's.

Choose Grok 3 Mini if:

• Faithfulness is critical — its 5/5 score vs GPT-4o-mini's 3/5 makes it far more reliable for RAG, summarization, and citation-grounded tasks.
• You're building agentic or tool-calling workflows. Grok 3 Mini scores 5/5 on tool calling (tied for 1st) vs GPT-4o-mini's 4/5.
• Your output volume is high — at $0.50/M output tokens, Grok 3 Mini is cheaper per output token than GPT-4o-mini's $0.60/M.
• You want access to reasoning traces — Grok 3 Mini supports include_reasoning and exposes raw thinking traces, useful for debugging and transparency.
• You need strong long-context retrieval or persona consistency for chatbot/assistant applications.