## 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>
Adds an opt-in compression interceptor that buffers Anthropic
/v1/messages requests, runs IntelligentContextManager over the
messages array, and forwards the (possibly trimmed) body upstream.
All other paths, methods, and content-types stay on the original
streaming passthrough — so existing operators see zero change.
Behaviour gates ALL must be true to buffer + compress:
- --compression flag (or HEADROOM_PROXY_COMPRESSION=1)
- method == POST
- path == /v1/messages
- Content-Type: application/json
- ICM constructed successfully at startup
Falls through to streaming on any failure: parse, missing fields,
unknown model, body-too-large. Compression must never break a
request — that's the safety contract.
Model context windows come from a vendored LiteLLM snapshot at
crates/headroom-proxy/data/model_prices_and_context_window.json
parsed once into an OnceLock<HashMap>. Refresh via
scripts/refresh_model_limits.sh. Rationale documented inline:
hardcoded tables silently rot; LiteLLM is the canonical source
the entire LLM-tooling ecosystem relies on.
New tests:
- 16 unit tests across compression::{anthropic, icm, model_limits}
- 5 integration tests: off-passthrough, on-short-passthrough,
on-oversized-trim, on-non-json-skip, on-non-llm-path-skip
Verification:
- cargo test --workspace -> 884 passed, 0 failed
- cargo clippy --workspace -- -D warnings -> clean
- cargo fmt --check -> clean
