Grok 4 vs Mistral Small 4

Across our 12-test suite, Grok 4 wins 5 benchmarks, Mistral Small 4 wins 3, and they tie on 4. Neither model has a clean sweep.

Where Grok 4 wins:

• Strategic analysis (5 vs 4): Grok 4 ties for 1st among 54 models in our testing; Mistral Small 4 ranks 27th. This is the clearest qualitative gap between these two models. Strategic analysis measures nuanced tradeoff reasoning with real numbers — the kind of task that separates frontier models from capable small models in practice.
• Faithfulness (5 vs 4): Grok 4 ties for 1st among 55 models; Mistral Small 4 ranks 34th. Faithfulness measures how well a model sticks to source material without hallucinating — critical for RAG, summarization, and document QA pipelines.
• Long context (5 vs 4): Grok 4 ties for 1st among 55 models; Mistral Small 4 ranks 38th. Both have similar context windows (~256K tokens), but Grok 4 demonstrates meaningfully better retrieval accuracy at 30K+ tokens in our testing.
• Classification (4 vs 2): Grok 4 ties for 1st among 53 models; Mistral Small 4 ranks 51st out of 53. This is the largest rank differential in the dataset — Mistral Small 4 scores near the bottom for routing and categorization tasks, while Grok 4 scores at the top.
• Constrained rewriting (4 vs 3): Grok 4 ranks 6th of 53 models; Mistral Small 4 ranks 31st. Compressing content within hard character limits is a practical editorial task where Grok 4 has a consistent edge.

Where Mistral Small 4 wins:

• Structured output (5 vs 4): Mistral Small 4 ties for 1st among 54 models; Grok 4 shares a 4/5 score with 26 others and ranks 26th. For applications that depend on strict JSON schema compliance — API responses, form parsing, tool outputs — Mistral Small 4 is the safer default.
• Creative problem solving (4 vs 3): Mistral Small 4 ranks 9th of 54 models; Grok 4 ranks 30th. The gap is meaningful: Grok 4's score of 3/5 sits below the median for this test in our dataset (p50 = 4), while Mistral Small 4's 4/5 sits at the 75th percentile.
• Agentic planning (4 vs 3): Mistral Small 4 ranks 16th of 54 models; Grok 4 ranks 42nd. Grok 4's score of 3/5 is below the dataset median (p50 = 4) and below the 25th percentile threshold for this test (p25 = 4). For agentic and multi-step task applications, Mistral Small 4 is the stronger performer in our testing.

Where they tie:

• Tool calling (4/4): Both rank 18th of 54 models. Equal performance for function selection, argument accuracy, and sequencing.
• Safety calibration (2/2): Both rank 12th of 55 models. Neither model stands out here — both are near the 75th percentile for this score (p75 = 2), meaning most models score similarly low on refusing harmful requests while permitting legitimate ones.
• Persona consistency (5/5): Both tie for 1st among 53 models. Equal character maintenance and injection resistance.
• Multilingual (5/5): Both tie for 1st among 55 models. Neither has an advantage for non-English output quality.

The pricing gap here is not a rounding error — it is a strategic decision. Grok 4 costs $3.00 per million input tokens and $15.00 per million output tokens. Mistral Small 4 costs $0.15 per million input tokens and $0.60 per million output tokens. At 1M output tokens per month, you are paying $15 for Grok 4 versus $0.60 for Mistral Small 4 — a $14.40 difference that is trivial for an enterprise with a fixed use case. At 10M output tokens per month, that gap grows to $144. At 100M output tokens per month — realistic for a production chatbot, document processing pipeline, or customer support tool — Grok 4 costs $1,500,000 versus Mistral Small 4's $60,000, a difference of $1,440,000 annually. The 25x price ratio means the decision is not just about which model scores higher; it is about whether the benchmark advantages Grok 4 holds (strategic analysis, faithfulness, long-context retrieval) are worth $1.44M extra per 100M output tokens. For most high-volume applications, Mistral Small 4's scores on the benchmarks it wins — structured output, creative problem solving, agentic planning — are more than adequate at a fraction of the cost. The math only works for Grok 4 in low-volume, high-stakes deployments where accuracy per call outweighs cost per call.

Choose Grok 4 if:

• Your application depends on faithfully grounding responses in source documents (RAG, legal review, medical summarization) — it scores 5/5 vs Mistral Small 4's 4/5, and ranks 1st vs 34th in our testing.
• You need accurate classification or content routing — Grok 4 scores 4/5 and ranks 1st; Mistral Small 4 scores 2/5 and ranks 51st out of 53. This gap is real and operationally significant.
• Your workload involves long documents where retrieval precision matters — Grok 4 ranks 1st vs Mistral Small 4's 38th on long-context tasks.
• You are doing low-to-moderate volume strategic analysis (business intelligence, competitive research, scenario modeling) where the 5 vs 4 score difference on that dimension justifies the 25x cost premium.
• You can absorb $15/M output tokens and need the best available performance on faithfulness and analysis.

Choose Mistral Small 4 if:

• You are building structured output pipelines — it scores 5/5 and ranks 1st for JSON schema compliance, beating Grok 4's 4/5.
• Your use case involves agentic workflows or multi-step planning — Mistral Small 4 ranks 16th vs Grok 4's 42nd on agentic planning in our testing.
• You need creative ideation or non-obvious problem solving — Mistral Small 4 ranks 9th vs Grok 4's 30th on creative problem solving.
• You are running any significant volume (10M+ output tokens/month) where the $0.60 vs $15 per million output token gap translates to real budget pressure.
• You want a model that supports additional sampling parameters (frequency_penalty, presence_penalty, top_k, stop) not available in Grok 4's parameter set.