Grok 4.20 vs Mistral Small 3.2 24B

Across our 12-test suite Grok 4.20 wins 9 tests, Mistral Small 3.2 24B wins 0, and they tie on 3 (constrained rewriting, safety calibration, agentic planning). Detailed walk-through (score format: Grok vs Mistral, with ranking where available):

• structured output: 5 vs 4 — Grok tied for 1st ("tied for 1st with 24 other models out of 54 tested"). This matters for JSON/schema tasks: Grok is more reliable at format adherence. Mistral sits mid-pack (rank 26/54).

• strategic analysis: 5 vs 2 — Grok tied for 1st ("tied for 1st with 25 other models out of 54 tested"); Mistral ranks 44/54. For nuanced tradeoff reasoning with numbers, Grok is markedly stronger in our tests.

• creative problem solving: 4 vs 2 — Grok ranks 9/54; Mistral ranks 47/54. For generating feasible, non-obvious ideas Grok is substantially better.

• tool calling: 5 vs 4 — Grok tied for 1st ("tied for 1st with 16 other models out of 54"); Mistral ranks 18/54. For function selection, arguments, and sequencing (agentic tool workflows) Grok is superior in our testing.

• faithfulness: 5 vs 4 — Grok tied for 1st ("tied for 1st with 32 other models out of 55 tested"); Mistral ranks 34/55. Grok sticks to source material more reliably in our benchmarks.

• classification: 4 vs 3 — Grok tied for 1st ("tied for 1st with 29 other models out of 53 tested"); Mistral is mid-ranked (31/53). For routing and categorization Grok scored higher.

• long context: 5 vs 4 — Grok tied for 1st ("tied for 1st with 36 other models out of 55 tested") and has a 2,000,000-token context window vs Mistral’s 128,000. For retrieval or multi-document workflows, Grok’s long context advantage is material.

• persona consistency: 5 vs 3 — Grok tied for 1st ("tied for 1st with 36 other models out of 53 tested"); Mistral ranks 45/53. Grok better maintains character and resists injection in our evaluation.

• multilingual: 5 vs 4 — Grok tied for 1st ("tied for 1st with 34 other models out of 55 tested"); Mistral ranks 36/55. Non-English parity favors Grok in our tests.

Ties (no winner): constrained rewriting 4 vs 4 (both rank 6/53); safety calibration 1 vs 1 (both rank 32/55) — both models performed similarly on refusal/permission balance in our tests; agentic planning 4 vs 4 (both rank 16/54) — both match on goal decomposition and recovery.

Practical meaning: Grok delivers stronger structured outputs, tool-based agenting, long-context retrieval, faithfulness, and multilingual quality in our benchmarks. Mistral’s strengths are not winners in these tests but it remains functionally capable for many instruction-following tasks—at a fraction of the cost.

Per the payload, Grok 4.20 charges $2.00 per 1K input tokens and $6.00 per 1K output tokens (i.e., $8.00 per 1M combined input+output when assuming equal input/output). Mistral Small 3.2 24B charges $0.075 per 1K input and $0.20 per 1K output (≈ $0.275 per 1M combined). At equal in/out token volume: 1M tokens/month costs $8.00 (Grok) vs $0.275 (Mistral); 10M costs $80 vs $2.75; 100M costs $800 vs $27.50. The payload lists a priceRatio of ~30, so Grok is ~30× more expensive per token. Who should care: enterprises or apps with sustained high-volume throughput (10M–100M tokens/month) will see substantial monthly cost differences and should budget accordingly; hobbyists, small startups, or cost-sensitive inference tasks should prefer Mistral for economics unless Grok’s higher benchmark performance justifies the spend.

Choose Grok 4.20 if you need production-grade tool calling, long-context (up to 2,000,000 tokens), strict structured output, high faithfulness, or multilingual parity — particularly for agentic workflows where mistakes are costly. Expect to pay roughly 30× more per token. Choose Mistral Small 3.2 24B if monthly token spend is the dominant constraint (1M–100M token budgets), you need a competent instruction-following model for lower-risk tasks, or you’re prototyping and want the lowest possible inference cost while sacrificing top-tier structured-output and tool-call performance.