Grok 3 vs Mistral Small 3.1 24B

Across our 12-test benchmark suite, Grok 3 wins 10 tests outright and ties 2 (constrained rewriting and long context). Mistral Small 3.1 24B wins zero tests.

Where Grok 3 dominates:

• Agentic planning (5 vs 3): Grok 3 ranks tied for 1st among 54 models (alongside 14 others); Mistral Small 3.1 24B ranks 42nd. This is the most consequential gap for developers building multi-step AI agents — goal decomposition and failure recovery require the kind of structured reasoning Mistral Small 3.1 24B struggles with here.

• Tool calling (4 vs 1): Grok 3 ranks 18th of 54; Mistral Small 3.1 24B ranks 53rd of 54 — near the bottom of all tested models. The payload confirms Mistral Small 3.1 24B has a no_tool calling quirk, which explains the floor score. Any application requiring function calling should not use Mistral Small 3.1 24B via this configuration.

• Persona consistency (5 vs 2): Grok 3 ties for 1st among 53 models; Mistral Small 3.1 24B ranks 51st. For chatbots, character-based apps, or any system prompt that must hold under adversarial input, this is a critical gap.

• Strategic analysis (5 vs 3): Grok 3 ties for 1st among 54 models; Mistral Small 3.1 24B ranks 36th. Nuanced tradeoff reasoning with real numbers is a meaningful differentiator for business intelligence and advisory use cases.

• Creative problem solving (3 vs 2): Grok 3 ranks 30th of 54; Mistral Small 3.1 24B ranks 47th. Both models are below the median on this test — neither excels at generating non-obvious, feasible ideas, but Grok 3 is less weak.

• Faithfulness (5 vs 4): Grok 3 ties for 1st among 55 models; Mistral Small 3.1 24B ranks 34th. For RAG pipelines and summarization tasks where sticking to source material matters, Grok 3 has a real edge.

• Structured output (5 vs 4): Both score well, but Grok 3 ties for 1st among 54 models vs Mistral Small 3.1 24B's 26th-place rank. JSON schema compliance is broadly competitive above score 4, but Grok 3 is more reliable at the ceiling.

• Classification (4 vs 3): Grok 3 ties for 1st among 53 models; Mistral Small 3.1 24B ranks 31st. For routing and categorization tasks, Grok 3 is the safer choice.

• Multilingual (5 vs 4): Grok 3 ties for 1st among 55 models; Mistral Small 3.1 24B ranks 36th. Non-English output quality is stronger with Grok 3.

• Safety calibration (2 vs 1): Neither model excels here. Grok 3 ranks 12th of 55; Mistral Small 3.1 24B ranks 32nd. Both score below the field median (p50 = 2), though Grok 3 at least reaches median performance.

Where they tie:

• Constrained rewriting (3 vs 3): Both rank 31st of 53 — identical performance on compressing text within hard character limits.

• Long context (5 vs 5): Both tie for 1st among 55 models alongside 36 other models. At 30K+ token retrieval, both perform at the ceiling, making context window size (131,072 for Grok 3 vs 128,000 for Mistral Small 3.1 24B) a negligible differentiator.

Safety calibration is a weak point for both models — scoring below the field average — which is worth factoring into deployment decisions for sensitive applications.

Grok 3 costs $3.00 per million input tokens and $15.00 per million output tokens. Mistral Small 3.1 24B costs $0.35 per million input tokens and $0.56 per million output tokens. The output cost ratio is 26.8x — meaning every dollar spent on Mistral Small 3.1 24B output buys you roughly 27x as many tokens as Grok 3.

At 1M output tokens/month: Grok 3 costs $15.00 vs Mistral Small 3.1 24B's $0.56. Negligible for most teams.

At 10M output tokens/month: Grok 3 costs $150.00 vs $5.60. The gap becomes meaningful for growing products.

At 100M output tokens/month: Grok 3 costs $1,500.00 vs $56.00 — a $1,444/month difference. At this scale, the choice of model has real budget implications.

Who should care about the cost gap: Developers building high-throughput APIs, consumer apps, or document processing pipelines where output volume scales with users. For low-volume enterprise workflows where output quality directly drives business value (legal analysis, code generation, agentic pipelines), the Grok 3 premium is easier to absorb. Also note that Mistral Small 3.1 24B supports image input (text+image->text modality) per the payload, while Grok 3 is text-only — if your use case involves vision, the pricing comparison shifts further in Mistral's favor since you'd need a different Grok model entirely.

Choose Grok 3 if:

• You're building agentic pipelines that require goal decomposition, tool calling, and failure recovery — Grok 3 scores 5 vs 3 on agentic planning and 4 vs 1 on tool calling in our tests.
• Your application relies on function/tool calling at all — Mistral Small 3.1 24B has a confirmed no_tool calling quirk and scores near last (53rd of 54) on that benchmark.
• You need reliable persona consistency for chatbots or system-prompt-driven applications (5 vs 2 in our testing).
• You're running strategic analysis, business intelligence, or advisory workflows where nuanced reasoning matters (5 vs 3).
• Faithfulness to source material in RAG or summarization is critical (5 vs 4, 1st vs 34th in our rankings).
• Budget is not the primary constraint and output quality drives direct business value.

Choose Mistral Small 3.1 24B if:

• You need multimodal (image + text) input — Mistral Small 3.1 24B supports text+image->text; Grok 3 is text-only.
• Your primary use case is long-context retrieval and both models score identically (5/5, tied for 1st) — paying $15.00 vs $0.56 per million output tokens for the same score makes no sense.
• You're running high-volume workloads where output token costs are the dominant cost driver and your tasks don't require tool calling or complex agentic behavior.
• Constrained rewriting is your main task — both score 3/5 and rank identically (31st of 53).
• You're prototyping or running a cost-sensitive operation where the 26.8x output price difference compounds quickly.