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Two coupled defects on Anthropic's 1M-context tier: Headroom **under-budgeted** those sessions and **under-priced** them by ~2x. The second gets worse once the first is fixed, so they ship together. --- # Part 1 — `[1m]` was lost before the context budget was sized `sanitize_anthropic_model_id()` strips a trailing `[1m]`, which is correct for the wire — upstream Anthropic rejects the suffix, and #2027 added the strip for exactly that reason. But `[1m]` is not only an ANSI artifact. Claude Code appends it to a model id to request the **1M context tier**, and only sends the `context-1m` beta header when it is present (#1158 — what `headroom wrap claude --1m` sets up). `get_context_limit()` sanitized *before* resolving, so the tier was gone by lookup time: ```python provider.get_context_limit("claude-sonnet-4-5[1m]") # 200_000 ← real window is 1M ``` The request still reached Anthropic correctly and still got a 1M window — the beta header goes through untouched. What broke is our **budget**: Headroom sized a 1M session at 200K and began compacting at a fifth of the available room. Models whose base is already 1M (`claude-opus-5`, `claude-sonnet-5`) resolved to 1M either way, which is why this went unnoticed. It bites the Sonnet 4 / 4.5 family — the models `[1m]` exists for. **Fix:** read the tier off the id *before* sanitizing; raise the resolved limit to at least 1M. `max()` rather than assignment, so a base wider than 1M keeps its own window. Detection is deliberately narrower than the sanitizer — only a literal `[1m]`; `[0m]`, `[1;32m]` and real `ESC[` sequences still strip without promoting. | model id | wire id (unchanged) | limit before | limit after | |---|---|---|---| | `claude-sonnet-4-5` | `claude-sonnet-4-5` | 200K | 200K | | `claude-sonnet-4-5[1m]` | `claude-sonnet-4-5` | **200K** | **1M** | | `claude-opus-5[1m]` | `claude-opus-5` | 1M | 1M | | `claude-sonnet-4-5[0m]` | `claude-sonnet-4-5` | 200K | 200K | | `ESC[1m claude-sonnet-4-5 ESC[0m` | `claude-sonnet-4-5` | 200K | 200K | The wire id is unchanged in every case, so #2027 holds — guarded by a regression test. --- # Part 2 — the pricing that reports those sessions was wrong ### 2a. The LiteLLM cost path was dead in every provider `litellm.completion_cost()` no longer accepts `prompt_tokens` / `completion_tokens`. Every call raised `TypeError`: ``` TypeError: completion_cost() got an unexpected keyword argument 'prompt_tokens' ``` All five providers — `anthropic`, `openai`, `google`, `cohere`, `litellm` — caught it with a bare `except` and silently fell through to their hand-maintained tables. The "up-to-date pricing from LiteLLM" the docstrings promise **has not run at all**. Anthropic additionally passed `input_tokens - cached_tokens`, the wrong convention (LiteLLM expects the cache-inclusive total), which would also have suppressed the long-context threshold even had the call worked. Replaced with `litellm.cost_per_token()` behind one shared helper, `pricing.litellm_pricing.estimate_cost_from_tokens()`, which reuses the existing gateway-alias candidate chain and returns `None` (not an exception) when LiteLLM can't price a model. ### 2b. Neither path applied Anthropic's long-context premium On the Sonnet 4 / 4.5 family a prompt over 200K re-prices the **whole** request — input 2×, output 1.5×, cache 2× — not just the tokens past the threshold. Rates confirmed from LiteLLM's `*_above_200k_tokens` fields. | request (`claude-sonnet-4-5`) | reported before | true | error | |---|---|---|---| | 100K in / 5K out | $0.3750 | $0.3750 | — | | 300K in / 5K out | $0.9750 | **$1.9125** | −49% | | 300K in (150K cached) / 5K out | $0.5700 | **$1.1025** | −48% | LiteLLM applies this itself once the call works. The manual fallback needed `_apply_long_context_premium()` — the LiteLLM dependency is gated `python_version < '3.14'`, so on 3.14 the fallback is the *only* path. **Both paths now agree to four decimal places on every case under test.** --- ## What I checked and did *not* change The fork report that prompted this claimed the Anthropic tables were materially stale ("Opus 4.x priced wrong"). **That does not hold.** I audited every entry against LiteLLM's vendored table: - **Anthropic** — every model LiteLLM knows matches exactly, Opus 4.x included. - **OpenAI** — all 17 entries match; the two that don't resolve are retired models. The defect was the mechanism, not the numbers, so the rate cards are untouched. One thing the repaired path fixes for free: OpenAI's cached-input discount is **50% on gpt-4o, 75% on gpt-4.1, 90% on gpt-5**, but the manual path applies a flat 50% estimate. With LiteLLM live, real per-model rates are used. The flat estimate remains only as the offline fallback. ## Scope **No Rust change needed.** `crates/headroom-proxy/src/compression/model_limits.rs` resolves context windows but has **no in-tree callers**; the Rust `[1m]` handling is wire-body sanitization only, correct as-is, and its integration tests assert behavior this PR does not touch. **Judgment call worth a reviewer's eye:** the `[1m]` marker is honored for *any* model, including ones with no 1M tier (`claude-haiku-4-5-20251001[1m]` → 1M). Gating on an allowlist would be more precise but reintroduces a hand-maintained table that rots — the failure mode `model_limits.rs` already documents against. Since `[1m]` is set by our own wrapper and Claude Code's opt-in, honoring it seemed the better default. Happy to tighten. ## Tests - `TestContext1MSuffix` — detection, the 200K→1M promotion, the `max()` floor, ANSI non-promotion, and the wire-id guard for #2027. - `TestLongContextPricing` — the premium on both paths (parametrized), threshold boundary (200,000 vs 200,001), an untiered model charged no premium, and the two halves meeting: a `[1m]` request gets both the 1M window and the premium rate. - `TestLiteLLMCostHelper` — unknown model returns `None`, a known model prices correctly, and `input_tokens` is cache-inclusive. Two existing tests were updated, both pinned to the broken behavior: - `test_estimate_cost_basic` probed a "per 1M" rate by sending exactly 1M tokens, which now crosses the 200K threshold. Re-probed at 100K. (Worth knowing: `claude-3-5-sonnet-20241022` is retired and no longer in LiteLLM, so the alias chain resolves it to `claude-sonnet-4-20250514` and it inherits that model's tier. Harmless — a 200K-window model can't exceed 200K in reality — but it explains the number.) - `test_litellm_provider_info_and_cost_fallbacks` monkeypatched `litellm.completion_cost`; repointed at the new helper seam. ``` ruff check / ruff format / mypy — clean across all six changed source files ``` 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Tejas Chopra <tejas@Tejass-MacBook-Pro.local> Co-authored-by: Claude Opus 5 <noreply@anthropic.com> |
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| .. | ||
| __init__.py | ||
| test_anthropic.py | ||
| test_cohere.py | ||
| test_deepseek.py | ||
| test_openai.py | ||
| test_universal.py | ||