Autonomous Coding Agent Cost: 2026 Latency & PR Benchmark

A coding agent run costs $1.20 on the invoice. Then it makes two failed repair attempts, burns through a long test log, and leaves an engineer 30 minutes of review. The “cheap” change has now become a $35 engineering task.

That’s the trap in most autonomous coding-agent cost estimates. The API bill is visible; the expensive parts—retries, tool calls, context growth, debugging, and review—hide inside the workflow.

The useful unit isn’t tokens or tokens per second. It’s secure, accepted software work per dollar and minute.

Key takeaways

• A low API price doesn’t guarantee a low cost per merged change.
• Reasoning effort should be a routing control, not a universal default.
• Agent benchmarks measure the model, harness, tools, retry policy, and evaluation setup together.
• Runtime choice depends on prefix reuse, context length, concurrency, and end-to-end task latency.
• An accepted-PR metric needs a quality gate that includes security, regression checks, and human approval.

Cost per Accepted Pull Request

An agent may inspect a repository, search dependencies, edit six files, run tests, read a stack trace, revise its patch, and open a pull request. The final response might contain 300 visible tokens. The task may have consumed 50,000 tokens across prompts, tool output, hidden reasoning, and retries.

A more useful formula separates cached and uncached input:

Cost per accepted PR = (uncached input × input rate) + (cached input × cache rate) + (output and reasoning × output rate) + tool costs + review labor, divided by accepted PRs

Cached input shouldn’t simply be added to ordinary input. It is billed at a different rate, and it may reduce prefill work without reducing decoding, test execution, or tool latency.

Here’s a worked example. Suppose one task uses:

• 18,000 uncached input tokens at $3 per million;
• 42,000 cached input tokens at $0.30 per million;
• 6,000 output and reasoning tokens at $15 per million;
• two tool calls costing $0.08 in total;
• one retry with the same token mix at 60 percent of the original usage;
• 24 minutes of review at a stated labor rate of $100 per hour.

The initial model and tool cost is:

• Uncached input: $0.054
• Cached input: $0.013
• Output and reasoning: $0.090
• Tools: $0.080

That totals $0.237. Adding the retry brings model and tool spend to roughly $0.38. Review adds $40, producing a true engineering cost of about $40.38. If the patch fails review and never merges, its cost per accepted PR is effectively infinite until another successful run replaces it.

The exact prices vary by provider, but the pattern is stable: review time can dominate the bill.

Track distributions rather than one flattering average:

• p50 and p90 cost per task;
• p95 cost per accepted PR;
• agent turns and retries;
• tool calls per accepted PR;
• human review minutes;
• regression, rollback, and security-findings rates;
• assignment-to-merge time.

Separate routine edits, dependency upgrades, debugging, migrations, and long-running terminal work. Blending them produces a number nobody can use for capacity planning.

Claude Opus 5.5 and GPT-6 Sol cost comparison

A direct Claude Opus 5.5 and GPT-6 Sol cost comparison is meaningful only when the models run under the same conditions. Compare the full workflow, not just the published token price:

“Accepted” also needs a gate. Passing the existing tests is not enough. A qualifying PR should clear regression checks, dependency and security scanning, scope review, and human approval. Otherwise, an agent can improve its accepted-PR rate by submitting risky patches that happen to satisfy a narrow test suite.

What Agent Benchmarks Can—and Can’t—Tell You

The common benchmark families test different slices of software work. Terminal-Bench, for example, emphasizes command-line navigation, package installation, debugging, and system configuration. SWE-bench evaluates issue resolution in real repositories. OSWorld covers broader computer-use tasks. DeepSWE and similar evaluations focus on longer-horizon software engineering.

Their official repositories and papers are the right starting points for definitions and task versions: Terminal-Bench, SWE-bench, and OSWorld. A vendor blog may report a useful result, but it should be read alongside the exact task split, model configuration, token budget, hardware, tool permissions, retry count, and whether hidden reasoning tokens were included.

That last point matters. A model allowed unlimited retries in a managed coding product is not directly comparable with one given a single attempt in a bare evaluation harness. Neither is a result measured with a 32K context window comparable with one using 128K. Throughput, reasoning effort, prompt caching, and test execution can all change the ranking.

Treat vendor percentages as claims about a configuration, not as universal model properties. Before routing production work, replay 100 to 300 representative private issues. Record not only test success, but review outcomes, security findings, unnecessary changes, and time to merge.

The benchmark result that matters most is the one produced by your repository, permissions, CI system, and definition of “done.”

Reasoning, Caching, and Task Time

For a chat assistant, time to first token is a useful experience metric. For a coding agent, it’s only one timestamp in a much longer chain:

Task time = model time + tool time + environment time + retry time

Measure time to first token, time to first visible response, inter-token latency, tool duration, and end-to-end task time separately. A fast decoder won’t rescue a workflow that spends two minutes waiting for a test environment or repeatedly reconstructing a failed context.

Reasoning effort is best treated as a control knob. Low or medium effort is usually enough for renames, small test additions, and mechanical refactors. Increase it for unfamiliar architecture, cross-service changes, security-sensitive fixes, or a failure that survives the first repair. Stop after two or three failed repair loops and involve an engineer. Endless autonomous persistence is often just expensive confusion.

Prompt caching can reduce input cost and prefill time when the prefix remains stable. System instructions, tool definitions, repository metadata, and durable agent notes are good candidates. Test output, command results, and changing file contents belong later in the prompt.

Cache behavior is less reliable than a simple “hit or miss” label suggests:

• A changed system instruction or tool schema can invalidate the prefix.
• Reordering tools or repository metadata can destroy reuse.
• Long conversations may evict earlier KV-cache blocks.
• Concurrent sessions can pressure the cache even when prompts are identical.
• Memory fragmentation can reduce usable capacity before GPU memory appears full.

Caching also doesn’t eliminate decode time, tool execution, queueing, or test runs. Measure cache-hit and cache-miss paths separately.

Runtime Choices: vLLM, SGLang, and TensorRT-LLM

For long-context agents, model weights are only the beginning:

Total GPU memory ≈ model weights + KV cache + runtime overhead + CUDA workspace

KV-cache use rises with context length, generated reasoning, concurrency, attention dimensions, and parallel sampling. A server that handles 100 short prompts may support only a fraction of that workload at 128K context.

Runtime selection is therefore a workload decision:

SGLang can benefit workflows that repeatedly reuse a large prompt prefix. vLLM is often the sensible starting point for teams that need broad compatibility and mature deployment patterns. TensorRT-LLM becomes attractive when a dedicated performance team can justify kernel and hardware tuning.

Two runtime details deserve special attention. First, prefix caching can lose its advantage when small prompt changes invalidate a large shared prefix. Keep stable content byte-for-byte consistent where possible. Second, speculative decoding can improve generation speed when a smaller draft model predicts tokens that the larger model accepts. It helps decode-heavy responses, but it won’t accelerate shell commands, test execution, queueing, or failed tool calls.

Also distinguish throughput from agent latency. Tokens per second measured with many independent short requests rewards batching. An autonomous agent usually cares about the wall-clock time for one multi-turn task, including prefill, cache misses, tool waits, and retries. A system can post excellent throughput numbers and still feel slow to developers.

Test with realistic prompt lengths, cache states, concurrency, reasoning settings, and tool loops:

{
  "context_tokens": [8000, 32000, 128000],
  "concurrency": [1, 8, 32],
  "cache_state": ["hit", "miss"],
  "reasoning_effort": ["low", "medium", "high"],
  "metrics": [
    "ttft_p95",
    "task_time_p95",
    "tokens_per_second",
    "kv_cache_gb",
    "retry_rate"
  ]
}

The practical setup is usually hybrid. Route predictable work to a cheaper model, escalate when tests fail or architectural uncertainty appears, keep stable prefixes cacheable, and cap repair loops. Use a stronger reasoning model when uncertainty is more expensive than inference.

Measure the result in secure, mergeable changes per dollar, GPU-hour, and engineer minute. That number will tell you far more than an isolated leaderboard score.

Frequently Asked Questions

What is the average token cost per autonomous coding PR?

There is no dependable universal average. Measure uncached input, cached input, output, reasoning, tool calls, retries, and review labor by task category. Report p50, p90, and p95 cost per accepted PR.

Does prompt caching reduce coding-agent latency?

It can reduce repeated prompt-prefill work and input cost for stable prefixes. It does not remove decoding, tool execution, queueing, tests, or cache invalidation. Measure complete task latency.

Is vLLM or SGLang better for long-context coding agents?

vLLM is the safer general-purpose starting point. SGLang may perform better when agents reuse long prefixes or use structured generation. Test both with your real context lengths and concurrency.

How many reasoning tokens should a coding agent use?

Use low or medium effort for routine edits. Escalate after failed tests, repeated tool errors, unfamiliar architecture, or security-sensitive work. High effort should be targeted, not automatic.