Grok 4.20 vs Mistral Small 3.1 24B

Across our 12-test suite, Grok 4.20 outscores Mistral Small 3.1 24B on 10 benchmarks, ties on 2 (long context and safety calibration), and loses none.

Tool Calling (5 vs 1): This is the most consequential gap. Grok 4.20 scores 5/5 and is tied for 1st among 54 models tested. Mistral Small 3.1 24B scores 1/5 and ranks 53rd of 54 — confirmed by its no_tool calling quirk in the payload. This means the 3.1 24B cannot reliably select functions, pass arguments, or sequence calls, making it incompatible with agentic and API-integration workflows.

Strategic Analysis (5 vs 3): Grok 4.20 is tied for 1st of 54 models; Mistral Small 3.1 24B ranks 36th of 54. This test covers nuanced tradeoff reasoning with real numbers — Grok 4.20's advantage here is meaningful for business analysis, investment reasoning, and complex decision support.

Agentic Planning (4 vs 3): Grok 4.20 ranks 16th of 54; Mistral Small 3.1 24B ranks 42nd of 54. Goal decomposition and failure recovery are both weaker in the 3.1 24B — compounding the tool calling deficit for autonomous agent use cases.

Persona Consistency (5 vs 2): Grok 4.20 is tied for 1st of 53 models; Mistral Small 3.1 24B ranks 51st of 53 — near the bottom. For chatbot products, customer-facing AI, or roleplay applications, this is a sharp differentiator.

Creative Problem Solving (4 vs 2): Grok 4.20 ranks 9th of 54; Mistral Small 3.1 24B ranks 47th of 54. The 3.1 24B scores below the 25th percentile (p25=3) for this benchmark, while Grok 4.20 sits above the median.

Faithfulness (5 vs 4): Grok 4.20 is tied for 1st of 55 models; Mistral Small 3.1 24B ranks 34th. For RAG pipelines and summarization, Grok 4.20 is more reliable at sticking to source material.

Structured Output (5 vs 4): Grok 4.20 tied for 1st of 54; Mistral Small 3.1 24B ranks 26th of 54. JSON schema compliance is solid in the 3.1 24B but Grok 4.20 has a consistent edge.

Multilingual (5 vs 4): Grok 4.20 tied for 1st of 55; Mistral Small 3.1 24B ranks 36th of 55. Both score above the median (p50=5 for this test), but Grok 4.20 achieves the top tier.

Constrained Rewriting (4 vs 3): Grok 4.20 ranks 6th of 53; Mistral Small 3.1 24B ranks 31st of 53. Compressing content within hard character limits is notably better in Grok 4.20.

Classification (4 vs 3): Grok 4.20 tied for 1st of 53; Mistral Small 3.1 24B ranks 31st of 53.

Long Context (5 vs 5): Both models tie at the top of this benchmark — both tied for 1st of 55 models. However, Grok 4.20 offers a 2,000,000-token context window vs 128,000 for Mistral Small 3.1 24B. At the score level, they're equal, but Grok 4.20's window is 15x larger.

Safety Calibration (1 vs 1): Both score 1/5 and share rank 32 of 55. Neither model distinguishes itself here — this is an area where both fall in the bottom half of tested models.

Grok 4.20 costs $2.00/M input tokens and $6.00/M output tokens. Mistral Small 3.1 24B costs $0.35/M input and $0.56/M output — making the output cost ratio 10.7x in Mistral's favor. At 1M output tokens/month, that's $6.00 vs $0.56 — a $5.44 difference that's negligible for most teams. At 10M output tokens/month, the gap widens to $54.40 ($60 vs $5.60), still manageable for funded projects. At 100M output tokens/month, you're paying $600 vs $56 — a $544/month delta that becomes a real line item. Developers running high-volume text pipelines (summarization, classification, content generation at scale) where tool calling isn't required should take that gap seriously. Anyone building agentic systems, however, should note that Mistral Small 3.1 24B has a confirmed no_tool calling quirk — meaning it cannot participate in those workflows at all, making the price comparison moot for that use case.

Choose Grok 4.20 if: You need tool calling or agentic workflows (Mistral Small 3.1 24B literally cannot do this), you're building customer-facing AI that requires persona consistency, your tasks involve complex strategic reasoning or business analysis, you need context windows beyond 128K tokens, or you need the strongest multilingual output quality. At $6/M output tokens, it's priced at the higher end but delivers top-tier scores across 10 of 12 benchmarks.

Choose Mistral Small 3.1 24B if: Your workload is purely text-in/text-out with no function calling, you're running at very high volume (100M+ output tokens/month) where the $0.56/M output price translates to real savings, and your tasks are limited to summarization, basic classification, or long-document retrieval where its 5/5 long-context score is sufficient. Be aware that its persona consistency (rank 51 of 53), creative problem solving (rank 47 of 54), and tool calling (rank 53 of 54) scores make it a poor fit for anything requiring those capabilities.