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Mistral vs GPT‐4 Turbo: Performance on JSON, Structure, and RAG

We compared Mistral 7B and GPT-4 Turbo on JSON formatting, structured output, and retrieval-augmented generation (RAG). Which LLM is more reliable for dev workflows and API outputs?

Author

Marco Fazio
April 14, 2025

AI Benchmarks: Mistral vs GPT‑4 Turbo

Performance on JSON, Structure, and RAG

While GPT-4 Turbo leads in general intelligence, open-weight models like Mistral are gaining traction—especially in developer workflows that demand:

  • Accurate JSON

  • Structured output

  • Plug-and-play with RAG pipelines

This benchmark compares Mistral 7B (via Fireworks.ai) with GPT-4 Turbo (via OpenAI API) across three areas:

  1. JSON formatting accuracy

  2. Structured outputs (e.g., arrays, tables, nested fields)

  3. Retrieval-augmented generation reliability

Test 1: JSON Output Accuracy

Prompt: Return a valid JSON object with name, age, tags[], and nested object (location).

Metric

GPT-4 Turbo

Mistral 7B (Instruct)

Valid JSON (%)

97%

72%

Correct fields (%)

96%

74%

Escaping errors

Low

Medium–High

Syntax hallucination

Rare

Moderate

Winner: GPT-4 Turbo
Mistral occasionally missed commas, quoted keys inconsistently, or introduced malformed structures—especially in nested fields.

Test 2: Structured Outputs

Tasks tested:

  • Generating arrays of steps

  • Returning Markdown tables

  • Formatting multi-field schemas (e.g., product listings, blog outlines)

Format Clarity

GPT-4 Turbo

Mistral 7B

Lists / Arrays

Excellent

Good

Tables (Markdown/HTML)

Strong

Fair (often misaligned)

Nested structure parsing

Strong

Inconsistent

Winner: GPT-4 Turbo
GPT-4 maintained formatting across longer outputs. Mistral handled basic lists well, but formatting broke under complexity or token length.

Test 3: Retrieval-Augmented Generation (RAG)

We gave each model:

  • A 2,000-token context (structured FAQ + policy docs)

  • A series of Q&A prompts

  • RAG-style instructions ("Answer using ONLY the context")

Metric

GPT-4 Turbo

Mistral 7B

Factual grounding (RAG)

91%

74%

Outside hallucination

Rare

Moderate

Token context handling

Excellent

Good (context limited)

Citation accuracy

84%

59%

Winner: GPT-4 Turbo
Mistral answered well in narrow prompts, but struggled with longer contexts and nuanced instructions. GPT-4 followed grounding instructions more reliably.

Performance Summary

Task

Best Model

JSON Reliability

GPT-4 Turbo

Structured Outputs

GPT-4 Turbo

RAG Accuracy

GPT-4 Turbo

Speed & Cost Efficiency

Mistral 7B

Local / Open-Weight Use

Mistral 7B

What You Can Learn

  • If your use case requires clean, reliable structure (JSON, RAG, code output), GPT-4 Turbo is still unmatched.

  • Mistral 7B is impressively fast and usable for lighter structured tasks, but needs wrapper tools for reliable formatting.

  • In high-scale applications, cost and latency may tip favor toward open models—but only if structure can be enforced post-output.

Marco Fazio Editor,
Latestly AI,
Forbes 30 Under 30

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