Quantization Degrades Quality Over Long Agent Loops

The benchmark says the model works.

Your 200-call agent loop says otherwise.

The gap between benchmarks and production

You're running an open-source model through a fast inference provider. The benchmarks look good. The latency is excellent. You spin up an agent task that needs to iterate: read files, propose changes, run tests, incorporate results, repeat.

Somewhere around tool call 150, the outputs start drifting. The model's suggestions get less coherent. It references things that aren't in the context anymore. You check the docs, check the prompt, check your settings. Nothing obvious is wrong.

Here's what the benchmarks didn't tell you: most published evals measure single-shot tasks or short agent loops. A code completion here, a unit test there. But your actual workflows run hundreds of tool calls deep. And at that depth, small quality losses compound.

What quantization actually costs

Fast inference providers achieve their speed through quantization: reducing the precision of model weights to run on cheaper hardware. This is a legitimate engineering tradeoff. Lower precision means lower memory footprint, which means higher throughput, which means lower latency and cost per token.

The problem is that the quality loss isn't zero. And on long-running tasks, it compounds.

"Quantization, no matter what anybody says, degrades the quality of the output of the model. And with long-running agent loops, it's a compounding effect."

Adam, Roo Cast S01E04

One tester ran the same model, same eval suite, through two different endpoints: a fast quantized provider and the original Alibaba endpoint for Qwen 3 Coder. The difference was measurable.

"If you use the Alibaba endpoint directly with Qwen 3 Coder, with my evals in particular, they score about 8% higher."

Adam, Roo Cast S01E04

8% sounds small. On a single prompt, you might not notice. Over a 200-call agent loop where each step builds on the last, that 8% compounds. A slightly wrong file reference in step 30 leads to a completely wrong approach by step 100.

Why standard benchmarks miss this

The benchmarks aren't lying. They're just measuring short tasks.

"A lot of the tests are running, if you dig into it, are very short-run agent loops or even a single like code test. But the things that I test are much longer running, you know, hundreds of tool calls that go out."

Adam, Roo Cast S01E04

If your use case is "generate a function, run a test, done," quantized inference is probably fine. The quality loss doesn't have time to compound. But if your use case involves multi-step reasoning, file exploration, iterative debugging, or any workflow where the model's output at step N becomes the context for step N+1, you're running a different experiment than the benchmark measured.

The tradeoff in practice

This isn't "never use quantized models." The tradeoff is real and sometimes worth it.

When quantized inference works well:

• Single-shot code generation
• Quick refactors with immediate feedback
• Tasks where you'll verify the output manually anyway
• High-volume, low-stakes automation

When you pay the compounding tax:

• Multi-file refactors that require consistent understanding
• Debugging sessions that iterate until tests pass
• Autonomous agent loops with minimal human checkpoints
• Any task where you're trusting step 150 based on what happened in step 30

The speed gain is real: often 10x or more. The question is whether your task is short enough that the quality loss stays negligible.

Quantized vs. full-precision inference for agent workflows

How Roo Code handles model selection for long loops

Roo Code uses BYOK (Bring Your Own Key) architecture, which means you connect directly to the model provider of your choice. This design lets you switch endpoints mid-workflow without changing your agent configuration.

When Roo Code closes the loop on a complex task, running tests, incorporating failures, and iterating automatically, each tool call builds on the previous context. For workflows exceeding 100 tool calls, routing to a full-precision endpoint preserves the coherence that quantized providers sacrifice for speed.

The practical pattern: Use fast quantized providers for exploratory tasks with human checkpoints. Switch to original model endpoints when Roo Code runs autonomous loops where step 150 depends on step 30.

What to try

If you're seeing unexplained degradation on long agent loops, run one test: take the same task, same prompt structure, and run it through the original model endpoint instead of the fast provider. If the output quality holds steady at depth, you've found the variable.

The benchmark didn't lie. It just measured a different workload than yours.