Benchmarking GPT-5.6 Sol and Fable on Full Library Rust Rewrites
Engineering benchmarks demonstrate that frontier agentic setups like Fable and GPT-5.6 Sol High can complete complex end-to-end Python-to-Rust rewrites in a single pass. While GPT-5.6 Sol reached performance parity after one follow-up prompt for $43 in token costs, lightweight models failed entirely.
Impact: High
Why it matters
You can now use high-reasoning agent workflows to port legacy or performance-critical Python libraries to Rust, dramatically reducing execution time and dependencies.
TL;DR
- 01Frontier models can autonomously port whole libraries across programming languages, but token usage can reach 11M+ tokens.
- 02GPT-5.6 Sol High requires prompt refinement to hit optimal runtime execution speeds, costing roughly $43 per full-run library port.
- 03Smaller or flash reasoning models currently lack the context preservation needed for complex, multi-file code syntax generation.
Key facts
- Token Volume
- 11 Million Tokens
- GPT-5.6 Sol Execution Cost
- $43
- Startup Speed Improvement
- 87ms to 2ms
- Rendering Speedup
- 9.6x
- Executable Size
- 3MB standalone binary
Python-to-Rust Rewrite Metrics
In a real-world benchmark, Fable undertook a complete port of the TerminalTextEffects Python library to Rust. The operation consumed 11M tokens in a single execution context, delivering an architecture with zero external dependencies compiled down to a 3MB single binary executable.
Performance and Runtime Benchmarks
Comparing the original Python code against the generated Rust binary revealed huge efficiency gains:
- Startup Time: Reduced from
87msin Python to2msin Rust. - Rendering Speed: Achieved a
9.6xperformance improvement. - Binary Footprint: Standalone
3MBbinary requiring no runtime environment or virtualenvs.
GPT-5.6 Sol vs DeepSeek V4 Flash
Testing model capabilities across the same architecture blueprint showed significant disparities:
- GPT-5.6 Sol High: Generated a working version. The initial iteration was
30%slower in rendering, but parity was achieved after1follow-up prompt. Total token expenditure was$43. - DeepSeek V4 Flash: Failed to generate working or compilable Rust code despite multiple iterative prompts.
✓ When to use
- Use high-tier models like GPT-5.6 Sol High or Fable when porting CLI utilities and zero-dependency standalone libraries.
- Use when budget permits spending $40+ per automated migration run to reduce technical debt.
✕ When NOT to use
- Do not use lightweight or flash reasoning models (e.g., DeepSeek V4 Flash) for multi-file syntax transformation tasks.
- Avoid running automated rewrites without explicit performance testing benchmarks on the initial generated output.
What to do today
- Evaluate legacy Python utilities in your monorepo for potential automated Rust porting using agentic workflows.
- Budget at least $40-$50 in API credits when running high-reasoning, multi-step code translation jobs.
Sources