Processing 100 lines of code (~980 tokens) with Mistral Large 3 costs $0.00049 for codebase ingestion and $0.000784 for an AI-powered code review and refactoring pass.
Feed 100 lines of code into the prompt context for repository search, Q&A, or architecture planning.
Ingest 100 lines of code and generate audit findings, unit test recommendations, and refactor diffs.
Mistral Large 3 writes 100 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 | $0.00049 | $0.000049 | $0.000784 | 1,000,000 |
| GPT-5.6 Sol | openai | $0.0051 | $0.00051 | $0.0112 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.0128 | $0.001275 | $0.0281 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.0051 | $0.00051 | $0.0112 | 512,000 |
| GPT-5.5 Pro | openai | $0.0306 | $0.00306 | $0.0673 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.0204 | $0.00204 | $0.0367 | 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. 100 lines of code produces approximately 980 tokens.
Sending 100 lines of code as context and generating a thorough code review with recommendations costs approximately $0.000784. Utilizing prompt caching on repeat turns or static repository definitions drops this to $0.000343.
Mistral Large 3 has a context window of 1,000,000 tokens. 100 lines of code consumes 0.10% of its total available context.