headroom/tests/test_pricing.py

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from __future__ import annotations
from dataclasses import FrozenInstanceError
from datetime import date, timedelta
import pytest
import headroom.pricing as pricing
from headroom.pricing.anthropic_prices import ANTHROPIC_PRICES, get_anthropic_registry
from headroom.pricing.openai_prices import OPENAI_PRICES, get_openai_registry
from headroom.pricing.registry import ModelPricing, PricingRegistry
def test_pricing_public_exports_and_provider_registries() -> None:
assert pricing.ModelPricing is ModelPricing
assert pricing.PricingRegistry is PricingRegistry
assert "get_openai_registry" in pricing.__all__
assert "get_anthropic_registry" in pricing.__all__
assert "estimate_cost" in pricing.__all__
openai_registry = get_openai_registry()
anthropic_registry = get_anthropic_registry()
assert openai_registry.source_url == "https://openai.com/api/pricing/"
assert anthropic_registry.source_url == "https://www.anthropic.com/pricing"
assert openai_registry.prices["gpt-4o"] == OPENAI_PRICES["gpt-4o"]
assert (
anthropic_registry.prices["claude-3-5-sonnet-20241022"]
== ANTHROPIC_PRICES["claude-3-5-sonnet-20241022"]
)
feat(pricing): add DeepSeek V4 model pricing (deepseek-v4-flash, deepseek-v4-pro) (#1168) ## Description Adds pricing support for DeepSeek V4 models (`deepseek-v4-flash` and `deepseek-v4-pro`) when routing Headroom through `--anthropic-api-url https://api.deepseek.com/anthropic`. The vendored LiteLLM pricing database predates DeepSeek V4, so cost estimation silently returned `None` for these models. ## Type of Change - [x] New feature (non-breaking change that adds functionality) ## Changes Made - **`headroom/pricing/deepseek_prices.py`** — New pricing data module with `ModelPricing` dataclass entries for both V4 models, following the pattern of `anthropic_prices.py` - **`headroom/pricing/__init__.py`** — Exports `DEEPSEEK_PRICES`, `get_deepseek_registry()`, `DEEPSEEK_LAST_UPDATED` - **`headroom/pricing/litellm_pricing.py`** — Runtime injection of DeepSeek V4 pricing into `litellm.model_cost`, plus `deepseek-` prefix added to `resolve_litellm_model()` provider prefix list - **`headroom/providers/anthropic.py`** — DeepSeek fallback in `_get_pricing()` when model starts with `deepseek-` and LiteLLM is unavailable - **`crates/headroom-proxy/data/model_prices_and_context_window.json`** — Vendored JSON entries (bare + provider-prefixed) for Rust-side context window lookups - **`tests/test_providers/test_deepseek.py`** — 20 tests across 3 test classes (pricing data, LiteLLM injection, Anthropic fallback) - **`tests/test_pricing.py`** — Added DeepSeek export validation alongside existing OpenAI/Anthropic assertions ## Testing - [x] Unit tests pass (`pytest`) - [ ] Linting passes (`ruff check .`) - [ ] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [ ] Manual testing performed ### Test Output ``` ========================= 137 passed, 8 warnings in 8.47s ========================= ``` ## Real Behavior Proof - Environment: Windows 10, Python 3.12, litellm 1.60+ - Exact command / steps: `python -c "from headroom.proxy.cost import CostTracker; t = CostTracker(); print(t.estimate_cost('deepseek-v4-flash', input_tokens=1000000, output_tokens=1000000))"` - Observed result: `$0.4200` (0.14 input + 0.28 output per 1M tokens) - Not tested: Live DeepSeek API routing via `--anthropic-api-url` (requires API key and Docker deployment) ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Additional Notes The 90% cache discount heuristic in `AnthropicProvider.estimate_cost()` (line 680) is a pre-existing pattern. DeepSeek V4 has much deeper cache discounts (98-99%), but the LiteLLM path currently falls through to the manual fallback which uses correct cached prices. A future improvement could prefer `cache_read_input_token_cost` from model info over the hardcoded `* 0.1` heuristic. --------- Co-authored-by: Claude <noreply@anthropic.com>
2026-06-24 16:44:27 +02:00
assert "get_deepseek_registry" in pricing.__all__
assert "DEEPSEEK_PRICES" in pricing.__all__
deepseek_registry = pricing.get_deepseek_registry()
assert deepseek_registry.source_url == "https://api-docs.deepseek.com/quick_start/pricing"
flash = deepseek_registry.get_price("deepseek-v4-flash")
assert flash is not None
assert flash.input_per_1m == 0.14
assert flash.output_per_1m == 0.28
openai_registry.prices.pop("gpt-4o")
anthropic_registry.prices.pop("claude-3-5-sonnet-20241022")
assert "gpt-4o" in OPENAI_PRICES
assert "claude-3-5-sonnet-20241022" in ANTHROPIC_PRICES
def test_model_pricing_is_frozen() -> None:
model = ModelPricing(model="demo", provider="test", input_per_1m=1.5, output_per_1m=2.5)
with pytest.raises(FrozenInstanceError):
model.model = "other" # type: ignore[misc]
def test_registry_staleness_and_warning() -> None:
fresh = PricingRegistry(last_updated=date.today() - timedelta(days=30))
assert fresh.is_stale() is False
assert fresh.staleness_warning() is None
stale = PricingRegistry(
last_updated=date.today() - timedelta(days=31),
source_url="https://example.test/pricing",
)
assert stale.is_stale() is True
assert stale.staleness_warning() == (
f"Pricing data is 31 days old (last updated: {stale.last_updated})."
" Please verify at: https://example.test/pricing"
)
def test_registry_estimate_cost_with_all_token_types() -> None:
registry = PricingRegistry(
last_updated=date.today() - timedelta(days=31),
prices={
"demo": ModelPricing(
model="demo",
provider="test",
input_per_1m=2.0,
output_per_1m=4.0,
cached_input_per_1m=1.0,
batch_input_per_1m=0.5,
batch_output_per_1m=0.25,
)
},
)
estimate = registry.estimate_cost(
"demo",
input_tokens=1_000_000,
output_tokens=500_000,
cached_input_tokens=250_000,
batch_input_tokens=200_000,
batch_output_tokens=100_000,
)
assert estimate.cost_usd == pytest.approx(4.375)
assert estimate.breakdown == {
"input": {"tokens": 1_000_000, "rate_per_1m": 2.0, "cost_usd": 2.0},
"output": {"tokens": 500_000, "rate_per_1m": 4.0, "cost_usd": 2.0},
"cached_input": {"tokens": 250_000, "rate_per_1m": 1.0, "cost_usd": 0.25},
"batch_input": {"tokens": 200_000, "rate_per_1m": 0.5, "cost_usd": 0.1},
"batch_output": {"tokens": 100_000, "rate_per_1m": 0.25, "cost_usd": 0.025},
}
assert estimate.pricing_date == registry.last_updated
assert estimate.is_stale is True
assert estimate.warning == (
f"Pricing data is 31 days old (last updated: {registry.last_updated})."
)
def test_registry_estimate_cost_zero_usage_returns_empty_breakdown() -> None:
registry = PricingRegistry(
last_updated=date.today(),
prices={
"demo": ModelPricing(model="demo", provider="test", input_per_1m=1.0, output_per_1m=2.0)
},
)
estimate = registry.estimate_cost("demo")
assert estimate.cost_usd == 0.0
assert estimate.breakdown == {}
assert estimate.is_stale is False
assert estimate.warning is None
@pytest.mark.parametrize(
("kwargs", "message"),
[
({}, "Model 'missing' not found in registry"),
({"cached_input_tokens": 1}, "Model 'demo' does not have cached input pricing"),
({"batch_input_tokens": 1}, "Model 'demo' does not have batch input pricing"),
({"batch_output_tokens": 1}, "Model 'demo' does not have batch output pricing"),
],
)
def test_registry_estimate_cost_error_paths(kwargs: dict[str, int], message: str) -> None:
registry = PricingRegistry(
last_updated=date.today(),
prices={
"demo": ModelPricing(model="demo", provider="test", input_per_1m=1.0, output_per_1m=2.0)
},
)
with pytest.raises(ValueError, match=message):
registry.estimate_cost("missing" if not kwargs else "demo", **kwargs)