Grok 4 vs Mistral Small 3.1 24B

Our 12-test benchmark suite gives Grok 4 a clear edge: it wins 9 tests outright, ties 3, and loses none against Mistral Small 3.1 24B.

Where Grok 4 wins decisively:

• Tool calling: 4 vs 1 — the most dramatic gap in the comparison. Grok 4 ranks 18th of 54 models; Mistral Small 3.1 24B ranks 53rd of 54, second-to-last. The payload flags a no_tool calling quirk for Mistral Small 3.1 24B, which explains the score. Any agentic or function-calling workflow should not use Mistral Small 3.1 24B based on our tests.

• Persona consistency: 5 vs 2. Grok 4 ties for 1st among 53 models tested; Mistral Small 3.1 24B ranks 51st of 53. This matters for chatbot personas, roleplay, and instruction-following that requires staying in character under adversarial inputs.

• Strategic analysis: 5 vs 3. Grok 4 ties for 1st among 54 models; Mistral Small 3.1 24B ranks 36th. For nuanced tradeoff reasoning with real numbers — financial analysis, competitive assessments, policy evaluation — Grok 4 is substantially stronger in our tests.

• Faithfulness: 5 vs 4. Grok 4 ties for 1st among 55 models; Mistral Small 3.1 24B ranks 34th. Grok 4 is more reliable at staying grounded in source material without hallucinating — critical for summarization, RAG pipelines, and document QA.

• Multilingual: 5 vs 4. Grok 4 ties for 1st among 55 models; Mistral Small 3.1 24B ranks 36th. A meaningful difference for international deployments.

• Creative problem solving: 3 vs 2. Grok 4 ranks 30th of 54; Mistral Small 3.1 24B ranks 47th. Neither model excels here relative to the field — both sit below the median (p50 = 4) — but Grok 4 is less weak.

• Classification: 4 vs 3. Grok 4 ties for 1st among 53 models; Mistral Small 3.1 24B ranks 31st. For routing and categorization tasks, Grok 4 is more accurate in our tests.

• Constrained rewriting: 4 vs 3. Grok 4 ranks 6th of 53; Mistral Small 3.1 24B ranks 31st. Compression and hard-limit adherence favors Grok 4.

• Safety calibration: 2 vs 1. Neither model distinguishes itself here — Grok 4 ranks 12th of 55 (tied with 19 others), Mistral Small 3.1 24B ranks 32nd of 55. Both score below the median field performance (p50 = 2 for safety calibration), so this is a weak area across the board.

Where they tie:

• Structured output: Both score 4, both rank 26th of 54. JSON schema compliance is equivalent — neither has an edge.
• Long context: Both score 5, both tie for 1st among 55 models. At 30K+ token retrieval, they perform identically in our tests — though note Grok 4 has a 256K context window versus Mistral Small 3.1 24B's 128K window.
• Agentic planning: Both score 3, both rank 42nd of 54. Goal decomposition and failure recovery is a shared weakness — both sit below the field median (p50 = 4).

No wins for Mistral Small 3.1 24B across our 12 tests.

The pricing gap here is substantial. Grok 4 costs $3.00/M input tokens and $15.00/M output tokens. Mistral Small 3.1 24B costs $0.35/M input and $0.56/M output — making Grok 4 roughly 8.6x more expensive on input and 26.8x more expensive on output.

At real-world volumes, this compounds quickly:

• 1M output tokens/month: Grok 4 costs $15 vs Mistral Small's $0.56 — a $14.44 difference, barely noticeable.
• 10M output tokens/month: $150 vs $5.60 — a $144.40 gap, meaningful for small teams.
• 100M output tokens/month: $1,500 vs $56 — a $1,444 monthly difference that directly affects unit economics.

Developers building high-volume pipelines — document processing, classification at scale, chatbot infrastructure — should treat this gap as a core architectural decision. Grok 4's quality advantage is real, but at 100M tokens/month, you are paying $1,444 more per month for it. If your use case can tolerate Mistral Small 3.1 24B's scores (and on long context and structured output, they tie), the cost savings are significant. If you are running lower volumes or need Grok 4's tool calling, faithfulness, or persona consistency capabilities, the premium is justifiable.

Choose Grok 4 if:

• You are building agentic or tool-calling workflows — Mistral Small 3.1 24B scores 1/5 on tool calling (rank 53 of 54) and has a flagged no_tool calling quirk; Grok 4 scores 4/5 and supports parallel tool calling.
• You need reliable persona consistency for chatbots or instruction-following agents (5 vs 2 in our tests).
• Strategic analysis, financial reasoning, or policy work is central to your use case (5 vs 3).
• Faithfulness to source material matters — RAG pipelines, document summarization, legal or medical text (5 vs 4).
• You are processing images or files alongside text — Grok 4 supports text+image+file input; Mistral Small 3.1 24B supports text+image only.
• Your volume is low enough that the $14.44/M output token premium is absorbed by quality gains.

Choose Mistral Small 3.1 24B if:

• You are running high-volume, cost-sensitive workloads where the 26.8x output cost difference ($0.56 vs $15/M tokens) is a hard constraint.
• Your tasks are primarily long-context retrieval or structured output — both models tie at 5/5 and 4/5 respectively, and you should not pay Grok 4's premium for equivalent performance.
• Tool calling is not required in your pipeline.
• You are prototyping, experimenting, or have budget limits that make $15/M output tokens untenable.