What this guide covers
Two coding agents released this week sit at opposite ends of a design spectrum. Google's Gemini 2.5 Flash scores 43.6% on the FrontierMath coding benchmark, up from 34.4% on its predecessor, and shows similar gains on DeepSWE, according to Google's release notes. Bullet, a Y Combinator S26 startup, launched explicitly optimized for throughput in agentic workflows, treating latency as the primary constraint rather than benchmark accuracy. Neither choice is universally correct. The right one depends on where your agent sits in the development lifecycle, what it does when it gets something wrong, and how you measure either outcome.
This guide walks through the metrics that distinguish speed-optimized from accuracy-optimized agents, shows how real deployments have landed on different points of that spectrum, and explains how to decide which matters more for your situation.
Why speed and accuracy pull in opposite directions
Accuracy-optimized agents typically use longer context windows, more reasoning steps, and larger models. Each of those adds latency and cost. Speed-optimized agents prune that reasoning budget: they sample fewer tokens, skip reranking passes, and may use a smaller model or a speculative decoding shortcut. The trade-off is not hypothetical. On standard coding benchmarks, the accuracy difference between a fast and a thorough model can exceed 10 percentage points, which, on a codebase with 200 agentic changes per week, translates to 20 additional defect candidates that humans need to catch.
That framing matters for your evaluation strategy. The question is never just "is this agent accurate?" but "how accurate does it need to be, and at what latency, for this workflow to deliver value?"
The metrics that actually differentiate them
First-pass acceptance rate
First-pass acceptance rate measures how often an agent's output passes review without revision. For a coding agent, that means how often a generated pull request is merged without changes. This is the clearest single-number proxy for accuracy in a coding context, and it is more useful than benchmark scores because it reflects your codebase, your review standards, and your test suite rather than a curated dataset.
Rakuten's deployment of Claude Managed Agents reported 99.9% accuracy across 7-hour autonomous coding sessions, with feature delivery time falling from 24 days to 5 days. That combination, high first-pass accuracy at high throughput, is what an accuracy-optimized agent deployed at scale looks like when the deployment is working. It is also the ceiling, not the baseline you should expect at day one.
Step-level error rate and cascade risk
Coding agents rarely fail in one atomic step. They plan, scaffold, write, test, and iterate. An error at step two can corrupt every step that follows. Step-level accuracy and cascade failure is the metric category that captures this: you instrument each tool call and reasoning step, then ask what fraction of errors at step N propagate to a failed final output.
Speed-optimized agents, because they compress reasoning budgets, tend to produce higher step-level error rates. Whether that matters depends on your pipeline. If your CI/CD catches the cascade before merge, the cost is wasted compute. If the cascade reaches a deployment, the cost is an incident.
Latency per accepted output, not raw latency
Raw latency is the wrong denominator. What you want is latency per accepted output: total wall-clock time divided by the number of outputs that actually pass review. A fast agent that requires three retries before producing an acceptable result is slower on this metric than a slower agent that succeeds on the first attempt.
Notion's integration of Claude Managed Agents achieved an 85% latency reduction while supporting 30 or more concurrent agent tasks, partly through prompt caching. That is an example of latency optimization that does not trade away accuracy: the architecture change, not the model choice, drove the improvement.
Token efficiency and cost per outcome
At scale, token efficiency compounds. According to a 2026 industry survey, 86% of organizations have moved beyond AI coding agent experimentation and are deploying for production code. At that volume, a 20% difference in tokens per accepted output is a material budget line. Measure cost per accepted output alongside latency per accepted output. A thorough model that succeeds once is often cheaper than a fast model that fails twice.
Prefactor records token counts at the span level, so you can compute cost per accepted output across model versions or agent configurations without aggregating from logs manually. This matters most when you are comparing model routing options across a mixed fleet.
Deployment stage shapes which trade-off wins
Early development: accuracy constraints dominate
When your agent is new, your test coverage is thin and your reviewers are calibrating their trust. A high step-level error rate during this phase produces noise that slows down the humans in the loop. Accuracy-optimized agents are easier to debug: their longer reasoning traces show where they went wrong. Detecting quality decay before it reaches production is easier when the agent's behavior is deliberate enough to be legible.
Scaled production: throughput becomes the binding constraint
Once your test suite is mature, your review process is tuned, and your first-pass acceptance rate is stable above a threshold you have set, latency and cost become the binding constraints. Developers using GitHub Copilot report 55% faster task completion, and the tool now generates 46% of code written by its users. That adoption pattern reflects a workforce that has calibrated its review process to a known accuracy level and is now optimizing for throughput.
Siemens runs GitHub Copilot across 30,000 developers. At that scale, even small per-task latency improvements aggregate into meaningful engineering capacity. The trade-off calculation is different from a five-person team running their first coding agent.
High-stakes contexts: neither extreme fits
Some workflows, security patches in regulated environments, changes to payment processing logic, schema migrations touching customer data, require correctness constraints that no speed optimization should override. For these, validating agent behavior against expected outcomes before execution is not optional. Prefactor's activity schema validation lets you define what sequences of tool calls are permissible for a given task type, and flags deviations before they reach a downstream system.
Building your evaluation framework
Choosing between a speed-optimized and accuracy-optimized agent is not a one-time decision. It is a parameter you tune as your deployment matures. The evaluation framework you need has three layers.
First, set baseline metrics before you switch anything: first-pass acceptance rate, step-level error rate, and cost per accepted output, measured on your actual workload, not a benchmark.
Second, instrument the agent in production. Benchmark scores like FrontierMath tell you what a model can do on curated tasks. The gap between benchmark performance and production behavior is consistently wider than teams expect, often by 10 or more percentage points. Your production trace data is the ground truth.
Third, set explicit thresholds for each metric that correspond to your deployment stage and use case. If first-pass acceptance rate drops below your threshold, that is a signal to move toward a more accuracy-optimized configuration, regardless of what the latency numbers say.
Your coding agent's behavior profile should inform which thresholds you set. An agent that writes and runs tests before proposing a diff operates differently from one that generates raw code, and the right accuracy threshold differs between them.
Where to start
Define your three baseline metrics on your current workload this week, then run one week of instrumented production traffic before changing your model or configuration. That sequence gives you a before-and-after comparison that is actually comparable.
Start evaluating your agents and read the docs to set up span-level instrumentation and scoring against your own acceptance criteria.