Processing 250 lines of code (~2,450 tokens) with Mistral Large 3 costs $0.001225 for codebase ingestion and $0.00196 for an AI-powered code review and refactoring pass.
Feed 250 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 250 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
Mistral Large 3 writes 250 lines of code from scratch based on product specifications.
| Model | Provider | Code Ingestion | Cached Ingestion | Code Review Cost | Context Limit |
|---|---|---|---|---|---|
| Mistral Large 3 (Current) | mistral | $0.001225 | $0.000123 | $0.00196 | 1,000,000 |
| GPT-5.6 Sol | openai | $0.0128 | $0.001275 | $0.0281 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.0319 | $0.003188 | $0.0701 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.0128 | $0.001275 | $0.0281 | 512,000 |
| GPT-5.5 Pro | openai | $0.0765 | $0.00765 | $0.1683 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.051 | $0.0051 | $0.0918 | 1,000,000 |
On average, code yields approximately 9.8 tokens per line in Mistral Large 3 (mistral_bpe tokenizer). Indentation, brackets, camelCase variable names, and comments slightly increase token density compared to plain English text. 250 lines of code produces approximately 2,450 tokens.
Sending 250 lines of code as context and generating a thorough code review with recommendations costs approximately $0.00196. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.000858.
Mistral Large 3 has a context window of 1,000,000 tokens. 250 lines of code consumes 0.24% of its total available context.