## Description
Strips dangling terminal-style model suffixes like `[1m]` from
Anthropic-compatible model ids before Headroom forwards `/v1/messages`
upstream.
Closes#1812
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- Generalized `sanitize_anthropic_model_id()` so the existing dangling
ANSI-style suffix cleanup applies to Anthropic-compatible non-Claude
models, including `glm-5.2[1m]`.
- Added a provider-level regression for `glm-5.2[1m] -> glm-5.2`.
- Added a `/v1/messages` handler regression that captures the upstream
request body and verifies Headroom forwards `glm-5.2`, not
`glm-5.2[1m]`.
## Testing
- [x] Unit tests pass (`pytest`) — focused local tests and full CI test
matrix passed
- [x] Linting passes (`ruff check .`) — local Ruff and CI lint passed
- [x] Type checking passes (`mypy headroom`) — local mypy and CI lint
passed
- [x] New tests added for new functionality
- [ ] Manual testing performed
### Test Output
```text
$ rtk proxy env HEADROOM_REQUIRE_RUST_CORE=false PYTHONPATH=/Users/vinaygupta/Desktop/git/headroom-fix-1812-1m-model-suffix /tmp/headroom-1812-testenv/bin/python -c '<inject local headroom._core test stub; pytest.main(["tests/test_providers/test_anthropic.py", "tests/test_proxy_anthropic_model_sanitization.py"])>'
============================= test session starts ==============================
platform darwin -- Python 3.13.11, pytest-9.1.1, pluggy-1.6.0 -- /private/tmp/headroom-1812-testenv/bin/python
collected 17 items
tests/test_providers/test_anthropic.py::TestAnthropicModelSanitization::test_sanitize_model_id_removes_ansi_escape_sequences PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelSanitization::test_sanitize_model_id_removes_displayed_style_suffix PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelSanitization::test_sanitize_model_metadata_cleans_nested_model_ids PASSED
tests/test_providers/test_anthropic.py::TestAnthropicTokenCounting::test_count_text_fallback PASSED
tests/test_providers/test_anthropic.py::TestAnthropicTokenCounting::test_count_messages_basic PASSED
tests/test_providers/test_anthropic.py::TestAnthropicTokenCounting::test_count_text_allows_literal_special_tokens PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_get_context_limit_claude_sonnet PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_get_context_limit_claude_opus PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_get_context_limit_strips_ansi_model_suffix PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_get_context_limit_claude_5_family PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_supports_model_known PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_supports_model_prefix PASSED
tests/test_providers/test_anthropic.py::TestAnthropicModelLimits::test_token_counter_cache_uses_sanitized_model_id PASSED
tests/test_providers/test_anthropic.py::TestAnthropicCostEstimation::test_estimate_cost_basic PASSED
tests/test_providers/test_anthropic.py::TestAnthropicCostEstimation::test_pricing_lookup_strips_ansi_model_suffix PASSED
tests/test_providers/test_anthropic.py::TestAnthropicCostEstimation::test_pricing_claude_5_family PASSED
tests/test_proxy_anthropic_model_sanitization.py::test_anthropic_messages_strips_local_1m_model_suffix_before_forwarding PASSED
======================== 17 passed, 3 warnings in 2.11s ========================
$ rtk uvx ruff check headroom/providers/anthropic.py tests/test_providers/test_anthropic.py tests/test_proxy_anthropic_model_sanitization.py
All checks passed!
$ rtk uvx ruff format --check headroom/providers/anthropic.py tests/test_providers/test_anthropic.py tests/test_proxy_anthropic_model_sanitization.py
3 files already formatted
```
The normal editable test command was attempted but did not reach test
execution in this local checkout because the native extension build
failed:
```text
$ rtk uv run pytest tests/test_providers/test_anthropic.py tests/test_proxy_anthropic_model_sanitization.py
× Failed to build `headroom-ai @ file:///Users/vinaygupta/Desktop/git/headroom-fix-1812-1m-model-suffix`
warning: esaxx-rs@0.1.10: src/esaxx.cpp:620:10: fatal error: 'cstdint' file not found
error: failed to run custom build command for `esaxx-rs v0.1.10`
```
## Real Behavior Proof
- Environment: local macOS worktree from current upstream `main`; Python
3.13.11 throwaway test environment; `HEADROOM_REQUIRE_RUST_CORE=false`;
in-memory `headroom._core` stub used only to avoid the local missing
native extension during Python-level tests.
- Exact command / steps: POST a TestClient `/v1/messages` request with
`{"model": "glm-5.2[1m]", ...}` and replace `_retry_request` with a test
double that records the upstream body.
- Observed result: the recorded upstream request body contains
`{"model": "glm-5.2"}` and `mutation_reasons == ["sanitize_model_id"]`,
so the mutated JSON body is serialized instead of forwarding the
original bytes.
- Not tested: live Z.AI credentials/provider call; full local pytest;
local `mypy headroom`.
## 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
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [ ] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] I have updated the CHANGELOG.md if applicable
## Screenshots (if applicable)
N/A
## Additional Notes
- All non-skipped GitHub Actions checks are green after the rebase onto
`main`; skipped jobs are path-gated.
- No code comments were added because the fix reuses the existing
sanitizer and mutation-tracking path.
- Documentation and CHANGELOG updates are N/A for this narrow proxy
compatibility fix.
- The local pytest warnings were from the throwaway environment/test
tooling (`asyncio_mode`, Starlette TestClient deprecation, and the
existing AnthropicProvider no-client warning), not from the changed code
path.
## 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>
## Description
`tiktoken`'s `Encoding.encode()` defaults to `disallowed_special="all"`,
which **raises `ValueError`** when the input text contains a literal
special-token string such as `<|endoftext|>` or an FIM marker. Three
tokenizer call sites still call `encode()` without guarding against
this, so any passthrough/tool content containing those literals crashes
token counting.
In the proxy this aborts compression of `/v1/responses` requests. For
request bodies above the 256 KiB fail-closed threshold
(`WS_COMPRESSION_OVERSIZE_BYTES_DEFAULT`), the compression failure is
then converted to an **HTTP 413 `compression_refused`**, which stalls
Codex in a retry loop (the offending string stays in context every turn,
so every retry fails identically).
Observed in production with the token-mode proxy in front of Codex:
```text
WARNING /v1/responses compression failed (bytes=588269):
ValueError: Encountered text corresponding to disallowed special token '<|endoftext|>'.
ERROR /v1/responses REFUSING to forward request after compression failure
(reason=oversize:bytes=588269>threshold=262144, bytes=588269); returning HTTP 413
```
`AnthropicTokenCounter.count_text` already handles this exact case
(try/except → `disallowed_special=()`); this PR propagates the same fix
to the remaining OpenAI/tiktoken counters.
Closes # <!-- no issue filed; happy to open one if preferred -->
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- `headroom/providers/openai.py` — `OpenAITokenCounter.count_text`: fall
back to `disallowed_special=()` on `ValueError`.
- `headroom/tokenizers/tiktoken_counter.py` — same fallback in
`TiktokenCounter.count_text` **and** `TiktokenCounter.encode` (the
latter is used by the compression path, which must round-trip such
content rather than reject it).
- Each fallback mirrors the existing `AnthropicTokenCounter.count_text`
idiom and comments.
- Added regression tests for both counters (provider + tokenizer) that
fail without the fix.
## Testing
- [x] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [x] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [x] Manual testing performed
### Test Output
```text
$ pytest -q tests/test_tokenizers.py tests/test_tokenizer.py \
tests/test_providers/test_openai.py tests/test_providers/test_anthropic.py
75 passed, 14 skipped, 2 warnings in 2.22s
$ pytest -q tests/test_proxy_count_tokens_integration.py \
tests/test_openai_responses_context_compaction.py \
tests/test_openai_codex_routing.py
23 passed, 20 skipped, 1 warning in 4.33s
$ ruff check <changed files> # All checks passed!
$ ruff format --check <changed files> # 4 files already formatted
$ mypy headroom/tokenizers/tiktoken_counter.py headroom/providers/openai.py
Success: no issues found in 2 source files
```
## Real Behavior Proof
- Environment: clean clone at `v0.26.0-41-g7c26a54d`, editable install
(`pip install -e ".[dev,proxy]"`), Python 3.14.
- Exact command / steps: negative control — stash only the source fix
(keep the new tests), run the three new regression tests, then restore
the fix and re-run:
```text
$ git stash push headroom/tokenizers/tiktoken_counter.py headroom/providers/openai.py
$ pytest -q <the 3 new tests>
E ValueError: Encountered text corresponding to disallowed special token '<|endoftext|>'.
3 failed in 0.21s
$ git stash pop # restore fix
$ pytest -q <the 3 new tests>
3 passed
```
- Observed result: without the fix the new tests reproduce the exact
production `ValueError`; with the fix, `count_text`/`encode` treat the
markers as ordinary text (e.g. `"x <|endoftext|> y"` → 16 tokens,
`decode(encode(text)) == text`).
- Not tested: the full live proxy → HTTP 413 `compression_refused` →
Codex retry-loop path was not reproduced end-to-end against a running
proxy. Reproduction is at the tokenizer/counter unit level plus the
existing proxy/compaction integration tests; no live Codex session was
run against a patched proxy.
## Review Readiness
- [x] I have performed a self-review
- [x] This PR is ready for human review
## Description
Update `_DEFAULT_CONTEXT_LIMITS` so DeepSeek V3/V4 use their actual 1M
(1,048,576) context window instead of the outdated 128K. The hardcoded
128K causes Headroom to trigger compression far too early, defeating the
purpose of using a long-context model.
Closes#1038
## Type of Change
- [x] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [ ] Performance improvement
- [ ] Code refactoring (no functional changes)
## Changes Made
- Update `deepseek` default from 32,768 to 1,048,576 (V3/V4 family
default)
- Update `deepseek-v3` from 128,000 to 1,048,576
- Update `deepseek-coder` from 16,384 to 128,000 (Coder V2+)
- Add `deepseek-v4` entry at 1,048,576
- `deepseek-v2` stays at 128,000 (unchanged)
## Testing
- [x] Unit tests pass (`pytest`)
- [x] Linting passes (`ruff check .`)
- [ ] Type checking passes (`mypy headroom`)
- [x] New tests added for new functionality
- [ ] Manual testing performed
### Test Output
```text
$ python -m pytest tests/test_providers/test_universal.py -v -x
37 passed, 3 skipped in 38.08s
```
## Real Behavior Proof
- Environment: Windows 11, Python 3.11, headroom main (6904d47)
- Exact command / steps: python -m pytest
tests/test_providers/test_universal.py::TestOpenAICompatibleProvider::test_get_context_limit_deepseek_v3_is_1m
-v
- Observed result: PASSED - deepseek-v3 returns 1048576, deepseek-v4
returns 1048576, deepseek returns 1048576, deepseek-v2 returns 128000
- Not tested: no manual proxy testing with a live DeepSeek endpoint
## 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
- [ ] I have commented my code, particularly in hard-to-understand areas
- [ ] I have made corresponding changes to the documentation
- [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 context limit values are based on the DeepSeek V3 technical report
and API documentation, which specify a 1M-token context window. DeepSeek
Coder V2+ also supports 128K, up from the original Coder's 16K.
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
CodeAwareCompressor:
- Tree-sitter based AST parsing for Python, JS, TS, Go, Rust, Java, C, C++
- Preserves imports, signatures, type annotations, error handlers
- Guarantees syntactically valid output
- Uses tree-sitter-language-pack for broad language support
ContentRouter:
- Intelligent compression orchestrator
- Auto-routes content to optimal compressor based on type detection
- Source hint support for high-confidence routing
Custom Model Configuration:
- HEADROOM_MODEL_LIMITS env var and ~/.headroom/models.json support
- Pattern-based inference for unknown models (opus/sonnet/haiku tiers)
- Support for Claude 4.5, Claude 4, o3, o3-mini
- Graceful fallback - never crashes on unknown models
