costscope 0.3.0

Cost and wall-time estimation for batched LLM jobs (per-iteration sampling with confidence intervals).

costscope

Cost + time estimation for batched LLM jobs.

Sample a handful of iterations, project the total cost and wall time with a confidence interval, confirm before spending the rest. Each "iteration" can be a single call or a multi-call pipeline. Works with OpenAI (chat completions + Responses API, including gpt-image-1), Anthropic, or a built-in synthetic backend for tests and demos.

Install

pip install -e .             # core
pip install -e '.[openai]'   # for OpenAI models
pip install -e '.[anthropic]' # for Claude models
pip install -e '.[dev]'      # with pytest

Requires Python 3.10+.

Usage

A single call per iteration (the classic case):

from costscope import CostEstimator

with CostEstimator(model="o1", total_iterations=500, sample_iterations=20) as ce:
    for prompt in prompts:
        response = ce.completion(messages=[{"role": "user", "content": prompt}])
        ...

Multiple calls per iteration — sample reflects the full pipeline cost:

with CostEstimator(model="claude-opus-4-7", total_iterations=500) as ce:
    for row in rows:
        with ce.iteration():
            facts = ce.completion(messages=[{"role": "user", "content": extract(row)}])
            summary = ce.completion(messages=[{"role": "user", "content": summarize(facts)}])

The first 20 iterations are billed normally and used to build a per-iteration cost and time distribution. After that you'll see something like:

┌────────────────────────────────────────────────────────────┐
│ Cost & Time Estimate                                       │
├────────────────────────────────────────────────────────────┤
│  Model:        claude-opus-4-7                             │
│  Sample:       20 of 500 iter  (actual $0.4321)            │
│  Per iter:     $0.0216  (σ $0.0042)                        │
│  Projected:    $10.81                                      │
│  95% CI cost:  $10.05 – $11.57  (±7.0%)                    │
│  Per iter time:  3.4s                                      │
│  Wall time:    28min                                       │
│  95% CI time:  26min – 30min                               │
└────────────────────────────────────────────────────────────┘
  → Proceed? [y/N]:

Decline and subsequent .completion() calls raise EstimationCancelled.

Skip the prompt

• auto_confirm=True — always proceed
• threshold_usd=10.0 — auto-proceed when the upper bound is under the threshold
• confirm_fn=... — supply your own confirmation callback

OpenAI Responses API

api="auto" (default) routes gpt-image-* and gpt-5* to the Responses API, leaving chat-style models on chat completions. Force one explicitly:

CostEstimator(model="gpt-5", api="responses", ...)

The adapter translates messages= → input= and reads tokens from response.usage.input_tokens / output_tokens (and output_tokens_details.image_tokens for image generation).

Image generation (gpt-image-1)

with CostEstimator(model="gpt-image-1", total_iterations=200) as ce:
    for prompt in prompts:
        ce.completion(input=prompt, tools=[{"type": "image_generation"}])

Image-output tokens are priced separately ($40/1M for gpt-image-1). See examples/image_generation.py.

Driving the SDK yourself

If you can't use ce.completion() (e.g. you call client.images.generate() directly, or stream), use the escape hatch:

with ce.iteration():
    resp = my_custom_call(...)
    ce.record(cost=compute_cost(resp), elapsed=measured_seconds)

Synthetic mode

For tests, demos, and dev loops where real API calls would cost money:

from costscope import CostEstimator, SyntheticConfig

cfg = SyntheticConfig(
    input_median=800, output_median=300, reasoning_median=2000,
    latency_median=1.2,            # simulate ~1.2s/call for time estimates
    image_output_median=4000,      # for image-gen models
    seed=42,
)

with CostEstimator(model="o1", total_iterations=500, synthetic=True, synthetic_config=cfg) as ce:
    ...

See examples/basic.py for a full runnable example.

Supported models (built-in pricing)

OpenAI o-series (o1, o3, o3-mini, ...), GPT-4o, GPT-5, gpt-image-1, Claude 4.x (Opus, Sonnet, Haiku). For other models, supply prices via SyntheticConfig.custom_prices or extend pricing.py.

Use it as a Claude Code skill

I've also packaged costscope as a Claude Code plugin so the wrapping happens automatically. When Claude is writing or editing code that loops over LLM calls — the canonical for x in items: client.chat.completions.create(...) shape, or its Anthropic equivalent — the skill nudges it to reach for CostEstimator before the diff ever lands. It skips one-shot calls, agentic loops with unpredictable branching, and jobs already gated by another budget mechanism, so it stays out of the way when sampling-based estimation isn't the right tool. The plugin manifest lives at .claude-plugin/marketplace.json and the skill itself is at skills/costscope/SKILL.md — point Claude Code at this repo as a marketplace source to install.

Tests

pytest