Grok 3 Mini vs Mistral Small 3.2 24B

Across our 12-test benchmark suite, Grok 3 Mini wins 8 tests, Mistral Small 3.2 24B wins 1, and they tie on 3.

Where Grok 3 Mini leads:

• Tool calling (5 vs 4): Grok 3 Mini ties for 1st among 54 models tested; Mistral Small 3.2 24B ranks 18th. For agentic and API-driven workflows, this is a meaningful gap — function selection, argument accuracy, and sequencing all factor in.
• Faithfulness (5 vs 4): Grok 3 Mini ties for 1st among 55 models; Mistral Small 3.2 24B ranks 34th. If your application requires sticking closely to source material without hallucinating — RAG pipelines, document summarization — Grok 3 Mini has a clear edge.
• Long context (5 vs 4): Grok 3 Mini ties for 1st among 55 models; Mistral Small 3.2 24B ranks 38th. Both have large context windows (131K vs 128K tokens), but Grok 3 Mini's retrieval accuracy at 30K+ tokens outperforms in our testing.
• Persona consistency (5 vs 3): Grok 3 Mini ties for 1st among 53 models; Mistral Small 3.2 24B ranks 45th — a significant drop. For chatbot or assistant products requiring character stability and injection resistance, this gap matters.
• Classification (4 vs 3): Grok 3 Mini ties for 1st among 53 models; Mistral Small 3.2 24B ranks 31st. Routing and categorization tasks favor Grok 3 Mini.
• Safety calibration (2 vs 1): Grok 3 Mini ranks 12th of 55; Mistral Small 3.2 24B ranks 32nd. Neither model excels here — both fall below the median (p50 = 2) or at the floor — but Grok 3 Mini handles harmful request refusal and legitimate request permitting more reliably in our tests.
• Strategic analysis (3 vs 2): Grok 3 Mini ranks 36th of 54; Mistral Small 3.2 24B ranks 44th. Neither is strong at nuanced tradeoff reasoning with real numbers, but Grok 3 Mini is measurably better.
• Creative problem solving (3 vs 2): Grok 3 Mini ranks 30th of 54; Mistral Small 3.2 24B ranks 47th — near the bottom. Generating non-obvious, feasible ideas is a clear Grok 3 Mini advantage.

Where Mistral Small 3.2 24B leads:

• Agentic planning (4 vs 3): Mistral Small 3.2 24B ranks 16th of 54; Grok 3 Mini ranks 42nd. This is Mistral Small 3.2 24B's standout result — goal decomposition and failure recovery in multi-step tasks. If your workflow is heavily agentic, this is worth noting.

Ties:

• Structured output (4 vs 4): Both rank 26th of 54 — identical performance on JSON schema compliance.
• Constrained rewriting (4 vs 4): Both rank 6th of 53 — compression within hard character limits is equally strong.
• Multilingual (4 vs 4): Both rank 36th of 55 — equivalent non-English output quality.

Neither model has external benchmark scores (SWE-bench Verified, MATH Level 5, AIME 2025) included in this payload, so we cannot supplement with Epoch AI data here.

Grok 3 Mini costs $0.30/M input tokens and $0.50/M output tokens. Mistral Small 3.2 24B costs $0.075/M input and $0.20/M output — about 4x cheaper on input and 2.5x cheaper on output. In practice, output cost dominates most production workloads. At 1M output tokens/month, Grok 3 Mini runs $0.50 vs $0.20 for Mistral Small 3.2 24B — a $0.30 difference that barely registers. At 10M output tokens, that gap becomes $3.00 vs $2.00, still modest. At 100M output tokens/month — a serious production scale — you're looking at $50 vs $20, a $30/month difference per 100M tokens. For most developers and teams, this cost gap is easy to justify given Grok 3 Mini's benchmark advantage. However, for high-throughput pipelines running hundreds of millions of tokens monthly, or for cost-sensitive consumer products where margins are thin, Mistral Small 3.2 24B's lower price point becomes a genuine factor worth weighing against the performance gap. Mistral Small 3.2 24B also supports image input (text+image->text modality) per the payload, which could affect model selection for multimodal use cases regardless of price.

Choose Grok 3 Mini if: you need strong tool calling for agentic or API-integration workflows (scores 5, tied 1st of 54), high faithfulness for RAG or summarization pipelines (scores 5, tied 1st of 55), reliable persona consistency for assistant or chatbot products (scores 5, tied 1st of 53), or better performance on classification routing. Its reasoning token support and accessible thinking traces also make it useful when you need to inspect model reasoning. The 2.5x output cost premium is justifiable for most use cases given the benchmark gap.

Choose Mistral Small 3.2 24B if: your application is primarily agentic — multi-step goal decomposition, failure recovery, autonomous task execution — where it outperforms Grok 3 Mini (4 vs 3, ranking 16th vs 42nd of 54). It's also the better choice if you need image input processing, since its text+image->text modality handles multimodal inputs that Grok 3 Mini (text-only) cannot. High-volume production workloads running hundreds of millions of output tokens monthly will also find its lower price ($0.20/M vs $0.50/M output) meaningful at scale. For tasks where both models tie — structured output, constrained rewriting, multilingual — Mistral Small 3.2 24B gives equivalent quality at lower cost.