head-to-head

Mistral: Mistral Medium 3.5 vs OpenAI: GPT-5.4 Nano

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-11-07.

Mistral: Mistral Medium 3.5 OpenAI: GPT-5.4 Nano
Vendormistralaiopenai
Quality Score100100
Benchmark Score53.068.1
Input Price$1.50/M$0.20/M
Output Price$7.50/M$1.25/M
Context Window262,144400,000
Max Output209,715128,000
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index49.463.1
ai_index_agentic31.345.4
ai_index_coding77.492.5

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, GPT-5.4 Nano costs $0.51 and Mistral Medium 3.5 costs $3.38, so GPT-5.4 Nano is about 6.6 times cheaper for the same work.

GPT-5.4 Nano reads up to 400,000 tokens at once, roughly 600 pages of text, against about 393 pages for Mistral Medium 3.5.

On our blended benchmark score GPT-5.4 Nano leads 68.1 to 53.0.

Across the 20 tasks where they differ, GPT-5.4 Nano on 20 (SQL Generation, Code Review, Code Completion and others).

Mistral Medium 3.5 was released in April 2026 and GPT-5.4 Nano in March 2026.

Pick GPT-5.4 Nano if you need lower cost, long documents, benchmark performance or the stronger all-round task scores.

Costs use current list prices per million tokens; page counts assume about 0.75 words per token and 500 words per page.

Who wins by task?

TaskMistral: Mistral Medium 3.5OpenAI: GPT-5.4 Nano
SQL Generation 153 163
Code Review 147 158
Code Completion 116 133
Code Refactoring 144 157
Bug Fixing 155 168
Unit Test Generation 140 148
Code Documentation 131 140
Regex Writing 128 132
CI/CD Pipelines 132 139
Frontend Component Design 137 141
Data Analysis 151 159
CSV / Spreadsheet Cleanup 142 151
ETL Scripting 138 148
JSON Extraction 134 146
Bulk Data Labeling 123 134
OCR / Document Parsing 139 144
Table Extraction from PDFs 139 144
Long-Document Summarization 141 154
Short-Form Summarization 119 130
Blog Post Writing 129 135

Scores reflect capability match + benchmark data + pricing for each task. Methodology →

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