Grok 4.20 vs Mistral Small 4

Across our 12-test suite, Grok 4.20 wins 6 benchmarks outright, ties 5, and loses 1. Mistral Small 4 wins 1, ties 5, and loses 6. Here's the test-by-test breakdown:

Tool Calling (5 vs 4): Grok 4.20 scores 5/5, tied for 1st among 17 models out of 54 tested. Mistral Small 4 scores 4/5, ranked 18th of 54. For agentic workflows where function selection and argument accuracy matter — think multi-step automation or API orchestration — Grok 4.20 has a meaningful edge.

Faithfulness (5 vs 4): Grok 4.20 ties for 1st among 33 models out of 55; Mistral Small 4 ranks 34th of 55. Faithfulness measures how well a model sticks to source material without hallucinating. In RAG pipelines, summarization, or any grounded-generation task, Grok 4.20's advantage here is operationally significant.

Long Context (5 vs 4): Grok 4.20 ties for 1st among 37 models out of 55; Mistral Small 4 ranks 38th of 55. This test measures retrieval accuracy at 30K+ tokens. Combined with Grok 4.20's 2M-token context window (vs Mistral Small 4's 262K), the gap for long-document use cases is pronounced.

Strategic Analysis (5 vs 4): Grok 4.20 ties for 1st among 26 models out of 54; Mistral Small 4 ranks 27th of 54. For nuanced tradeoff reasoning and complex business or technical analysis, Grok 4.20 consistently outperforms.

Classification (4 vs 2): This is the starkest gap in the dataset. Grok 4.20 ties for 1st among 30 models out of 53 with a score of 4/5. Mistral Small 4 scores 2/5 and ranks 51st out of 53 — near the bottom of all tested models. If your application depends on accurate categorization or routing, Mistral Small 4 is a poor fit.

Constrained Rewriting (4 vs 3): Grok 4.20 ranks 6th of 53; Mistral Small 4 ranks 31st of 53. Grok 4.20 handles compression within hard character limits more reliably — relevant for content generation, summarization, and SEO-driven writing.

Safety Calibration (1 vs 2): Mistral Small 4 wins the only test it takes outright. Grok 4.20 scores 1/5, ranking 32nd of 55 (tied with 23 others); Mistral Small 4 scores 2/5, ranking 12th of 55. Both scores fall below the field median of 2 and below the 75th percentile of 2 — safety calibration is a weak point across the board, but Grok 4.20 is notably weaker here. For applications that require reliable refusal of harmful requests while permitting legitimate ones, neither model excels, but Mistral Small 4 is the lesser concern.

Ties (5 tests): Both models score identically on structured output (5/5), creative problem solving (4/5), persona consistency (5/5), agentic planning (4/4), and multilingual (5/5). On structured output and multilingual, both are tied for 1st in their respective cohorts — these are non-differentiating strengths.

The pricing gap here is one of the largest you'll encounter: Grok 4.20 charges $2.00/M input and $6.00/M output tokens; Mistral Small 4 charges $0.15/M input and $0.60/M output. That's a 13.3x difference on input and 10x on output.

In practice:

• At 1M output tokens/month: Grok 4.20 costs $6.00, Mistral Small 4 costs $0.60 — a $5.40 difference, barely noticeable.
• At 10M output tokens/month: $60.00 vs $6.00 — a $54 gap that starts to register for indie developers.
• At 100M output tokens/month: $600.00 vs $60.00 — a $540/month difference that is a real budget line item for any production system.

Who should care: Developers running customer-facing chat, document processing pipelines, or any workload with sustained token throughput should factor this gap into their build decision. If your use case can tolerate Mistral Small 4's lower scores on faithfulness, long context, and classification, the cost savings at scale are substantial. If you're running low-volume, high-stakes tasks — legal analysis, agentic pipelines, RAG over long documents — Grok 4.20's performance edge may justify the premium. Context window also differs: Grok 4.20 offers 2,000,000 tokens vs Mistral Small 4's 262,144, which matters for long-document workloads and further distinguishes the two at the architectural level.

Choose Grok 4.20 if:

• You need reliable classification or routing — Mistral Small 4 ranks 51st of 53 on this test; Grok 4.20 ties for 1st.
• Your application involves RAG, document grounding, or any task where hallucination is costly — Grok 4.20 scores 5/5 on faithfulness vs Mistral Small 4's 4/5 (ranked 34th of 55).
• You're working with long documents — Grok 4.20 offers a 2M-token context window and scores 5/5 on long-context retrieval; Mistral Small 4 is capped at 262K tokens and scores 4/5 (38th of 55).
• You're building agentic pipelines that depend on precise tool calling — Grok 4.20's 5/5 (tied 1st) vs Mistral Small 4's 4/5 (18th) makes a practical difference in multi-step automation.
• Volume is low to moderate (under ~10M output tokens/month) and performance is the priority.

Choose Mistral Small 4 if:

• You're running high-volume workloads where cost is a primary constraint — at 100M output tokens/month, Mistral Small 4 saves $540/month over Grok 4.20.
• Your use case centers on tasks where both models score equally well: structured output, creative problem solving, persona consistency, agentic planning, or multilingual generation.
• Safety calibration matters for your application — Mistral Small 4 scores 2/5 vs Grok 4.20's 1/5, ranking 12th vs 32nd of 55 models.
• You don't need more than 262K context tokens and can accept lower faithfulness and classification performance in exchange for dramatically lower API costs.
• You're prototyping or running batch workloads where per-token cost compounds quickly and the performance delta on faithfulness and long context is acceptable.