Grok 4 vs Mistral Small 3.2 24B

Across our 12-test benchmark suite (scored 1–5), Grok 4 averages higher than Mistral Small 3.2 24B on nearly every dimension. Here's the test-by-test breakdown:

Strategic Analysis (5 vs 2): The widest gap in this comparison. Grok 4 ties for 1st with 25 other models out of 54 tested; Mistral Small ranks 44th of 54. This test evaluates nuanced tradeoff reasoning with real numbers — the kind of work that matters for financial modeling, policy analysis, and complex decision support. Mistral Small's score of 2 puts it near the bottom of the field here.

Creative Problem Solving (3 vs 2): Grok 4 holds a one-point advantage (rank 30 of 54 vs rank 47 of 54). Neither model excels here — both score below the field median of 4 — but Grok 4 is the better option for generating non-obvious, feasible ideas.

Faithfulness (5 vs 4): Grok 4 ties for 1st (with 32 others out of 55 tested) versus Mistral Small's rank 34 of 55. For RAG pipelines and document-grounded generation where hallucination is a real risk, Grok 4 has a meaningful edge.

Classification (4 vs 3): Grok 4 ties for 1st with 29 others out of 53 tested; Mistral Small ranks 31st. At scale, this score difference matters for routing, tagging, and categorization tasks.

Long Context (5 vs 4): Grok 4 ties for 1st with 36 others out of 55 tested; Mistral Small ranks 38th. Grok 4 also has a larger physical context window (256K vs 128K tokens), reinforcing its advantage on long-document retrieval tasks.

Persona Consistency (5 vs 3): Grok 4 ties for 1st with 36 others out of 53 tested; Mistral Small ranks 45th — near the bottom. For chatbot and assistant applications where maintaining character under injection attempts is critical, this gap is significant.

Multilingual (5 vs 4): Both score well, but Grok 4 ties for 1st with 34 others out of 55 tested versus Mistral Small's rank 36 of 55. Both are above the field median of 5 (wait — the field median IS 5), so Mistral Small at 4 actually falls slightly below the median for this test.

Safety Calibration (2 vs 1): Grok 4 ranks 12th of 55 (tied with 19 others); Mistral Small ranks 32nd of 55. Neither model is a standout here, but Grok 4 is notably better at refusing harmful requests while permitting legitimate ones.

Agentic Planning (3 vs 4) — Mistral Small wins: This is the one test where Mistral Small 3.2 24B outperforms Grok 4. Mistral Small ranks 16th of 54 (tied with 25 others) on goal decomposition and failure recovery, while Grok 4 ranks 42nd. For multi-step agent workflows, Mistral Small 3.2 24B is the better choice — and at $0.20/M output tokens, it's also dramatically cheaper for the high token volumes agentic workloads tend to generate.

Structured Output (4 vs 4), Constrained Rewriting (4 vs 4), Tool Calling (4 vs 4) — ties: All three tests end in a draw, with both models sharing identical ranks (rank 26 of 54, rank 6 of 53, and rank 18 of 54 respectively). For JSON generation, function calling, and text compression tasks, either model performs equivalently by our tests.

The pricing gap here is one of the largest you'll find: Grok 4 runs $3.00/M input tokens and $15.00/M output tokens, while Mistral Small 3.2 24B costs $0.075/M input and $0.20/M output. At 1M output tokens per month, Grok 4 costs $15,000 versus Mistral Small's $200 — a $14,800 difference. Scale to 10M tokens and you're looking at $150,000 vs $2,000. At 100M tokens, Grok 4 runs $1.5M versus $20,000. For consumer apps, content pipelines, or any high-volume classification or summarization workload, Mistral Small 3.2 24B is the economically rational choice. Grok 4's pricing is appropriate for low-volume, high-stakes tasks — strategic analysis, complex research synthesis, or applications where each output genuinely demands the highest quality the model can produce. Developers building prototypes or running evals should also note that Mistral Small 3.2 24B's context window is 128K tokens versus Grok 4's 256K, so long-document work at scale will cost even more with Grok 4.

Choose Grok 4 if: You need top-tier strategic analysis, faithfulness to source material, long-context retrieval (up to 256K tokens), or multilingual output quality and your volume is low enough that $15/M output tokens is acceptable. It's also the better fit for persona-driven assistant products where character consistency is critical. The 256K context window makes it uniquely useful for processing very long documents where Mistral Small 3.2 24B would need chunking.

Choose Mistral Small 3.2 24B if: You're building agentic pipelines (it outscores Grok 4 on agentic planning, 4 vs 3), running high-volume workloads where $0.20/M output tokens vs $15.00/M makes a material budget difference, or deploying tool-calling and structured output features where both models score identically anyway. At 10M+ tokens/month, Mistral Small 3.2 24B saves over $148,000 per 10M output tokens — savings that make it the rational default for most production deployments. It's also the right pick when you need Mistral-specific sampling parameters like min_p, top_k, frequency_penalty, and repetition_penalty that Grok 4 doesn't support.