from types import SimpleNamespace from agent.usage_pricing import ( CanonicalUsage, format_cost_label, estimate_usage_cost, get_pricing_entry, normalize_usage, resolve_billing_route, ) from decimal import Decimal def test_normalize_usage_reads_deepseek_native_cache_hit_tokens(): """DeepSeek's native API (api.deepseek.com) reports context-cache hits as top-level prompt_cache_hit_tokens / prompt_cache_miss_tokens (with prompt_tokens = hit + miss), not OpenAI's nested prompt_tokens_details.cached_tokens. Before this fix, direct DeepSeek sessions always normalized to cache_read_tokens=0 — cache hits were invisible in accounting and billed at the full input rate (#61871).""" usage = SimpleNamespace( prompt_tokens=2000, completion_tokens=400, prompt_cache_hit_tokens=1500, prompt_cache_miss_tokens=500, ) normalized = normalize_usage(usage, provider="deepseek", api_mode="chat_completions") assert normalized.cache_read_tokens == 1500 # prompt_tokens includes cached; input = 2000 - 1500 = the miss bucket assert normalized.input_tokens == 500 assert normalized.output_tokens == 400 def test_normalize_usage_openai_reads_top_level_anthropic_cache_fields(): """Some OpenAI-compatible proxies (OpenRouter, Vercel AI Gateway, Cline) expose Anthropic-style cache token counts at the top level of the usage object when routing Claude models, instead of nesting them in prompt_tokens_details. Regression guard for the bug fixed in cline/cline#10266 — before this fix, the chat-completions branch of normalize_usage() only read prompt_tokens_details.cache_write_tokens and completely missed the cache_creation_input_tokens case, so cache writes showed as 0 and reflected inputTokens were overstated by the cache-write amount. """ usage = SimpleNamespace( prompt_tokens=1000, completion_tokens=200, prompt_tokens_details=SimpleNamespace(cached_tokens=500), cache_creation_input_tokens=300, ) normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") # Expected: cache read from prompt_tokens_details.cached_tokens (preferred), # cache write from top-level cache_creation_input_tokens (fallback). assert normalized.cache_read_tokens == 500 assert normalized.cache_write_tokens == 300 # input_tokens = prompt_total - cache_read - cache_write = 1000 - 500 - 300 = 200 assert normalized.input_tokens == 200 assert normalized.output_tokens == 200 def test_deepseek_v4_pro_pricing_entry_exists(): """Regression test: deepseek-v4-pro must have a pricing entry. Before this fix, deepseek-v4-pro sessions showed as unknown cost in hermes insights because the _OFFICIAL_DOCS_PRICING table had no entry for that model. See #24218. Rates track the 2026-07 price cut ($1.74/$3.48 → $0.435/$0.87). """ entry = get_pricing_entry( "deepseek-v4-pro", provider="deepseek", ) assert entry is not None assert entry.input_cost_per_million is not None assert entry.output_cost_per_million is not None assert float(entry.input_cost_per_million) == 0.435 assert float(entry.output_cost_per_million) == 0.87 assert float(entry.cache_read_cost_per_million) == 0.003625 def test_deepseek_deprecated_aliases_price_as_v4_flash(): """Invariant: deepseek-chat / deepseek-reasoner are deprecated aliases for deepseek-v4-flash's non-thinking / thinking modes (deprecation 2026-07-24) — they must bill at identical rates to the flash entry, or sessions on the legacy names over/under-report cost.""" flash = get_pricing_entry("deepseek-v4-flash", provider="deepseek") assert flash is not None for alias in ("deepseek-chat", "deepseek-reasoner"): entry = get_pricing_entry(alias, provider="deepseek") assert entry is not None, alias assert entry.input_cost_per_million == flash.input_cost_per_million, alias assert entry.output_cost_per_million == flash.output_cost_per_million, alias assert ( entry.cache_read_cost_per_million == flash.cache_read_cost_per_million ), alias def test_bedrock_claude_rows_all_carry_cache_pricing(): """Invariant: every Bedrock Claude pricing row must carry cache-read AND cache-write rates, otherwise a cached session prices as ``unknown``. Bedrock Claude routes through the AnthropicBedrock SDK and injects cache_control, so cached tokens are always reported — the pricing layer must be able to value them. See #50295. """ from agent.usage_pricing import _OFFICIAL_DOCS_PRICING claude_rows = [ (prov, model) for (prov, model) in _OFFICIAL_DOCS_PRICING if prov == "bedrock" and "claude" in model ] assert claude_rows, "expected at least one bedrock Claude pricing row" for key in claude_rows: entry = _OFFICIAL_DOCS_PRICING[key] assert entry.input_cost_per_million is not None, key assert entry.cache_read_cost_per_million is not None, key assert entry.cache_write_cost_per_million is not None, key # Cache reads are cheaper than fresh input; cache writes cost more. assert entry.cache_read_cost_per_million < entry.input_cost_per_million, key assert entry.cache_write_cost_per_million > entry.input_cost_per_million, key def test_bedrock_current_gen_claude_rows_resolve(): """Current-gen Claude models (Opus 4.8/4.7, Sonnet 5) must have Bedrock pricing rows so cached sessions report a dollar cost, not ``unknown``. Assert each resolves via the bare id and a cross-region inference profile (us./global. prefix), that every id for a given model resolves to the same entry, and that the row carries the cache fields a Bedrock Claude session needs. (Version-suffixed IDs like ``...-v1:0`` are covered separately by the normalizer test in the suffix-strip change; this test intentionally sticks to id shapes that resolve on ``main`` so it is independent of that PR.) """ url = "https://bedrock-runtime.us-east-1.amazonaws.com" for bare in ( "anthropic.claude-opus-4-8", "anthropic.claude-opus-4-7", "anthropic.claude-sonnet-5", ): ref = get_pricing_entry(bare, provider="bedrock", base_url=url) assert ref is not None, bare assert ref.input_cost_per_million is not None, bare assert ref.output_cost_per_million is not None, bare # Output costs more than input across the Claude line; sanity-check the # row isn't malformed (input < output). assert ref.output_cost_per_million > ref.input_cost_per_million, bare # Cache fields present so cached sessions price correctly (the #50295 # symptom was unknown cost on cached Bedrock Claude sessions). assert ref.cache_read_cost_per_million is not None, bare assert ref.cache_write_cost_per_million is not None, bare # Cross-region inference profiles resolve to the same entry. for mid in (f"us.{bare}", f"global.{bare}"): entry = get_pricing_entry(mid, provider="bedrock", base_url=url) assert entry is not None, mid assert entry.input_cost_per_million == ref.input_cost_per_million, mid assert entry.output_cost_per_million == ref.output_cost_per_million, mid def test_bedrock_versioned_inference_profile_resolves_to_bare_pricing(): """Bedrock profile IDs may include the provider's dated version suffix. The pricing table intentionally uses shorter model-family IDs, so the lookup needs a longest-prefix fallback after stripping the region scope. """ bare = get_pricing_entry("anthropic.claude-sonnet-4-6", provider="bedrock") assert bare is not None for model in ( "us.anthropic.claude-sonnet-4-6-20250514-v1:0", "global.anthropic.claude-sonnet-4-6-20250514-v1:0", ): scoped = get_pricing_entry(model, provider="bedrock") assert scoped is not None, model assert scoped.input_cost_per_million == bare.input_cost_per_million assert scoped.output_cost_per_million == bare.output_cost_per_million assert scoped.cache_read_cost_per_million == bare.cache_read_cost_per_million assert scoped.cache_write_cost_per_million == bare.cache_write_cost_per_million def test_bedrock_claude_cached_session_estimates_cost_not_unknown(): """A Bedrock Claude session with cache hits must produce a dollar estimate, not ``unknown`` — the user-visible symptom in #50295. """ bedrock_url = "https://bedrock-runtime.us-east-1.amazonaws.com" usage = SimpleNamespace( input_tokens=55, output_tokens=7113, cache_read_input_tokens=1369379, cache_creation_input_tokens=42135, ) canonical = normalize_usage(usage, provider="bedrock", api_mode="anthropic_messages") assert canonical.cache_read_tokens == 1369379 assert canonical.cache_write_tokens == 42135 result = estimate_usage_cost( "us.anthropic.claude-opus-4-6", canonical, provider="bedrock", base_url=bedrock_url, ) assert result.status == "estimated" assert result.amount_usd is not None def test_fireworks_router_fast_tier_prices_distinctly(): """Fast serving tiers live under accounts/fireworks/routers/-fast and bill at higher rates than the standard model — the routing layer's rsplit("/", 1) must land on the distinct fast-tier entry.""" standard = get_pricing_entry( "accounts/fireworks/models/kimi-k2p6", provider="fireworks", base_url="https://api.fireworks.ai/inference/v1", ) fast = get_pricing_entry( "accounts/fireworks/routers/kimi-k2p6-fast", provider="fireworks", base_url="https://api.fireworks.ai/inference/v1", ) assert standard is not None and fast is not None assert fast.input_cost_per_million > standard.input_cost_per_million assert fast.output_cost_per_million > standard.output_cost_per_million def test_google_and_vertex_routes_share_official_pricing_snapshot(): """Direct Gemini, Vertex, and Vertex's OpenAI-compatible hostname must all normalize to the Google official-pricing route. """ routes = ( resolve_billing_route("model", provider="gemini"), resolve_billing_route("google/model", provider="vertex"), resolve_billing_route( "google/model", provider="custom", base_url="https://aiplatform.googleapis.com/v1/projects/example", ), ) assert all(route.provider == "google" for route in routes) assert all(route.billing_mode == "official_docs_snapshot" for route in routes) def test_vertex_default_model_estimates_cached_usage(monkeypatch): """The bundled Vertex profile's default auxiliary model must fall back to Google snapshot pricing when the OpenAI-compatible endpoint has no model metadata, including for cache-read accounting. """ from providers import get_provider_profile monkeypatch.setattr( "agent.usage_pricing.fetch_endpoint_model_metadata", lambda *_args, **_kwargs: {}, ) vertex = get_provider_profile("vertex") result = estimate_usage_cost( vertex.default_aux_model, CanonicalUsage(input_tokens=100, output_tokens=100, cache_read_tokens=100), provider=vertex.name, base_url=vertex.base_url, ) assert result.status == "estimated" assert result.amount_usd is not None and result.amount_usd > 0 def test_normalize_usage_minimax_logs_cache_observability(caplog): """MiniMax providers on the Anthropic wire emit a debug-level cache-observability line recording the observable fields (input_tokens, output_tokens, cache_read_tokens, cache_write_tokens), so an operator can see real cache behavior without trusting the misleading cache_read number (constant +128 floor on MiniMax-M3). Standard logging level gating applies — no separate opt-in flag. """ usage = SimpleNamespace( input_tokens=1, output_tokens=11, cache_read_input_tokens=8594, cache_creation_input_tokens=0, ) with caplog.at_level("DEBUG", logger="agent.usage_pricing"): normalize_usage( usage, provider="minimax-cn", api_mode="anthropic_messages", ) cache_obs_records = [r for r in caplog.records if "cache_observability" in r.message] assert len(cache_obs_records) == 1 record = cache_obs_records[0] assert "input_tokens=1" in record.message assert "output_tokens=11" in record.message assert "cache_read_tokens=8594" in record.message assert "cache_write_tokens=0" in record.message def test_normalize_usage_native_anthropic_no_cache_observability(caplog): """The MiniMax cache-observability line must NOT fire for native Anthropic: there cache_read_input_tokens is exact and billable, so the MiniMax-specific "+128 floor / unreliable hit signal" note would be false and misleading in the logs. """ usage = SimpleNamespace( input_tokens=100, output_tokens=20, cache_read_input_tokens=50, cache_creation_input_tokens=10, ) with caplog.at_level("DEBUG", logger="agent.usage_pricing"): result = normalize_usage( usage, provider="anthropic", api_mode="anthropic_messages", ) assert all("cache_observability" not in rec.message for rec in caplog.records) # Token normalization itself is unaffected. assert result.input_tokens == 100 assert result.cache_read_tokens == 50 assert result.cache_write_tokens == 10 # --------------------------------------------------------------------------- # Cost label formatting (#79220: sub-cent costs render as $0.00) # --------------------------------------------------------------------------- class TestFormatCostLabel: """Tests for magnitude-scaled cost label formatting.""" def test_zero_renders_as_dollar_zero(self): assert format_cost_label(Decimal("0")) == "$0.00" def test_sub_cent_renders_4dp(self): """Costs below $0.01 render at 4 decimal places (#79220).""" label = format_cost_label(Decimal("0.004640")) assert label == "~$0.0046" # Must NOT be $0.00 assert "$0.00" != label def test_exactly_one_cent_renders_2dp(self): """$0.01 renders at 2dp.""" assert format_cost_label(Decimal("0.01")) == "~$0.01" def test_normal_cost_renders_2dp(self): assert format_cost_label(Decimal("1.23")) == "~$1.23" def test_large_cost_renders_2dp(self): assert format_cost_label(Decimal("42.50")) == "~$42.50" def test_very_small_sub_cent(self): """Even very small costs render non-zero.""" label = format_cost_label(Decimal("0.0001")) assert label == "~$0.0001" assert label != "$0.00" def test_below_4dp_floor_never_reads_zero(self): """Amounts below $0.00005 must not render as '~$0.0000' (#79220). 4dp truncation of a positive amount would produce a zero-looking label — the exact dishonesty the formatter exists to fix. """ label = format_cost_label(Decimal("0.00004")) assert label == "~$<0.0001" # Exact boundary: $0.00005 rounds to 0.0000 under ROUND_HALF_EVEN # and must also take the fallback. assert format_cost_label(Decimal("0.00005")) == "~$<0.0001" def test_sub_cent_deepseek_scenario(self): """Reproduce the #79220 reproduction: DeepSeek at $0.004640.""" # DeepSeek V4 Pro: 8K input + 1.2K output + 32K cache read # = $0.004640 per turn amount = Decimal("0.004640") label = format_cost_label(amount) assert "0.0046" in label assert label != "$0.00" assert label != "~$0.00" # --------------------------------------------------------------------------- # Subscription-included cost notes # --------------------------------------------------------------------------- class TestSubscriptionIncludedNotes: """Subscription-included costs should carry a note clarifying no invoice.""" def test_included_cost_has_note(self): """estimate_usage_cost for subscription-included route includes a note.""" # openai-codex is subscription_included usage = CanonicalUsage( input_tokens=1000, output_tokens=500, cache_read_tokens=0, cache_write_tokens=0, reasoning_tokens=0, ) result = estimate_usage_cost( "gpt-5.4-mini", usage, provider="openai-codex", ) assert result.status == "included" assert result.amount_usd == Decimal("0") assert len(result.notes) > 0 assert any("subscription" in note.lower() for note in result.notes) def test_normalize_usage_reads_kimi_top_level_cached_tokens(): """Kimi/Moonshot's native API reports context-cache hits as a top-level usage.cached_tokens, not OpenAI's nested prompt_tokens_details.cached_tokens and not DeepSeek's prompt_cache_hit_tokens. Neither existing fallback matches that name, so direct Kimi sessions normalized to cache_read_tokens=0 — the hits were invisible in accounting and billed at the full input rate (#65722).""" usage = SimpleNamespace( prompt_tokens=3000, completion_tokens=250, cached_tokens=1800, ) normalized = normalize_usage(usage, provider="kimi", api_mode="chat_completions") assert normalized.cache_read_tokens == 1800 # prompt_tokens includes the cached prefix: 3000 - 1800 = fresh input assert normalized.input_tokens == 1200 assert normalized.output_tokens == 250 def test_kimi_fallback_does_not_override_the_nested_openai_shape(): """A provider that reports BOTH shapes must keep the nested value. The new branch is last in the chain, so it only fills a genuine zero. """ usage = SimpleNamespace( prompt_tokens=1000, completion_tokens=100, prompt_tokens_details=SimpleNamespace(cached_tokens=400), cached_tokens=999, # must be ignored ) normalized = normalize_usage(usage, provider="kimi", api_mode="chat_completions") assert normalized.cache_read_tokens == 400 def test_kimi_fallback_does_not_override_deepseek_hit_tokens(): usage = SimpleNamespace( prompt_tokens=2000, completion_tokens=100, prompt_cache_hit_tokens=1500, cached_tokens=999, # must be ignored ) normalized = normalize_usage(usage, provider="deepseek", api_mode="chat_completions") assert normalized.cache_read_tokens == 1500 def test_usage_without_any_cache_fields_still_normalizes(): usage = SimpleNamespace(prompt_tokens=500, completion_tokens=50) normalized = normalize_usage(usage, provider="kimi", api_mode="chat_completions") assert normalized.cache_read_tokens == 0 assert normalized.input_tokens == 500 def test_normalize_usage_handles_dict_shaped_usage(): """Regression test for #74314: when the Responses API returns usage as a plain dict (e.g. from a middleware/proxy that deserialises JSON to dict instead of a typed SDK object), normalize_usage() must read the same token counts as it would from an attribute-style object. Before this fix, getattr() on a dict silently returned 0 for every field, so token counts and cost appeared as zero for dict-shaped usage. """ # Same payload as both a dict and a SimpleNamespace payload = { "input_tokens": 100, "output_tokens": 20, "input_tokens_details": {"cached_tokens": 60, "cache_creation_tokens": 10}, } ns = SimpleNamespace( input_tokens=100, output_tokens=20, input_tokens_details=SimpleNamespace(cached_tokens=60, cache_creation_tokens=10), ) dict_result = normalize_usage(payload, api_mode="codex_responses") ns_result = normalize_usage(ns, api_mode="codex_responses") assert dict_result.input_tokens == ns_result.input_tokens, f"input_tokens: dict={dict_result.input_tokens} vs ns={ns_result.input_tokens}" assert dict_result.output_tokens == ns_result.output_tokens, f"output_tokens: dict={dict_result.output_tokens} vs ns={ns_result.output_tokens}" assert dict_result.cache_read_tokens == ns_result.cache_read_tokens, f"cache_read: dict={dict_result.cache_read_tokens} vs ns={ns_result.cache_read_tokens}" assert dict_result.cache_write_tokens == ns_result.cache_write_tokens, f"cache_write: dict={dict_result.cache_write_tokens} vs ns={ns_result.cache_write_tokens}" # Sanity: values must be non-zero (the whole point of the bug) assert dict_result.input_tokens > 0 assert dict_result.cache_read_tokens > 0 def test_normalize_usage_handles_dict_openai_chat_completions(): """Dict-shaped usage must also work in the default (OpenAI chat-completions) branch, not just the codex_responses branch. """ payload = { "prompt_tokens": 500, "completion_tokens": 100, "prompt_tokens_details": {"cached_tokens": 200}, "completion_tokens_details": {"reasoning_tokens": 30}, } result = normalize_usage(payload, api_mode="chat_completions") assert result.output_tokens == 100 assert result.cache_read_tokens == 200 assert result.input_tokens == 500 - 200 # prompt_total - cache_read assert result.reasoning_tokens == 30 def test_normalize_usage_openai_reads_nested_cache_creation_tokens(): usage = SimpleNamespace( prompt_tokens=1000, completion_tokens=200, prompt_tokens_details=SimpleNamespace( cached_tokens=100, cache_creation_input_tokens=300, ), ) normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.cache_read_tokens == 100 assert normalized.cache_write_tokens == 300 assert normalized.input_tokens == 600 def test_normalize_usage_openai_reads_mapping_cache_creation_tokens(): usage = { "prompt_tokens": 1000, "completion_tokens": 200, "prompt_tokens_details": {"cache_creation_input_tokens": 300}, } normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.cache_write_tokens == 300 assert normalized.input_tokens == 700 def test_normalize_usage_openai_prefers_nested_cache_write_tokens(): usage = SimpleNamespace( prompt_tokens=1000, prompt_tokens_details=SimpleNamespace( cache_write_tokens=200, cache_creation_input_tokens=300, ), cache_creation_input_tokens=400, cache_write_tokens=500, ) normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.cache_write_tokens == 200 def test_normalize_usage_mapping_preserves_reasoning_tokens(): usage = { "prompt_tokens": 100, "completion_tokens": 20, "prompt_tokens_details": {"cached_tokens": 40}, "completion_tokens_details": {"reasoning_tokens": 12}, } normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.reasoning_tokens == 12 def test_normalize_usage_mapping_anthropic_fields(): usage = { "input_tokens": 80, "output_tokens": 20, "cache_read_input_tokens": 50, "cache_creation_input_tokens": 10, "output_tokens_details": {"reasoning_tokens": 7}, } normalized = normalize_usage(usage, provider="anthropic", api_mode="anthropic_messages") assert normalized.cache_read_tokens == 50 assert normalized.cache_write_tokens == 10 assert normalized.reasoning_tokens == 7 def test_normalize_usage_mapping_codex_fields(): usage = { "input_tokens": 100, "output_tokens": 20, "input_tokens_details": { "cached_tokens": 60, "cache_creation_tokens": 10, }, "output_tokens_details": {"reasoning_tokens": 5}, } normalized = normalize_usage(usage, provider="openai-codex", api_mode="codex_responses") assert normalized.input_tokens == 30 assert normalized.cache_read_tokens == 60 assert normalized.cache_write_tokens == 10 assert normalized.reasoning_tokens == 5 def test_normalize_usage_clamps_negative_counters(): usage = SimpleNamespace( prompt_tokens=100, completion_tokens=-5, prompt_tokens_details=SimpleNamespace( cached_tokens=-10, cache_write_tokens=-20, ), completion_tokens_details=SimpleNamespace(reasoning_tokens=-3), ) normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.input_tokens == 100 assert normalized.output_tokens == 0 assert normalized.cache_read_tokens == 0 assert normalized.cache_write_tokens == 0 assert normalized.reasoning_tokens == 0 def test_normalize_usage_clamps_inconsistent_cache_total(): usage = { "prompt_tokens": 100, "completion_tokens": 10, "prompt_tokens_details": { "cached_tokens": 80, "cache_creation_input_tokens": 50, }, } normalized = normalize_usage(usage, provider="openrouter", api_mode="chat_completions") assert normalized.input_tokens == 0 assert normalized.prompt_tokens == 130 def test_normalize_usage_codex_responses_reads_cache_write_tokens(): """GPT-5.6+ explicit prompt caching reports cache writes as input_tokens_details.cache_write_tokens (billed at 1.25x), per OpenAI's documented Responses API schema. Before this fix, the codex_responses branch only read the undocumented `cache_creation_tokens` name and always normalized cache writes to 0.""" usage = SimpleNamespace( input_tokens=2006, output_tokens=400, input_tokens_details=SimpleNamespace(cached_tokens=1920, cache_write_tokens=50), ) normalized = normalize_usage(usage, provider="openai", api_mode="codex_responses") assert normalized.cache_read_tokens == 1920 assert normalized.cache_write_tokens == 50 assert normalized.input_tokens == 2006 - 1920 - 50 def test_normalize_usage_codex_responses_falls_back_to_cache_creation_tokens(): """If cache_write_tokens is absent, fall back to the legacy cache_creation_tokens name rather than reporting 0.""" usage = SimpleNamespace( input_tokens=1000, output_tokens=100, input_tokens_details=SimpleNamespace(cached_tokens=200, cache_creation_tokens=80), ) normalized = normalize_usage(usage, provider="openai", api_mode="codex_responses") assert normalized.cache_write_tokens == 80 def test_normalize_usage_reads_qwen_flat_cached_tokens(): """Some Alibaba/Qwen regional endpoints report cache reads as a flat `usage.cached_tokens` field with no `prompt_tokens_details` wrapper at all. Before this fix, those responses fell through every branch and normalized to cache_read_tokens=0, undercounting cost.""" usage = SimpleNamespace( prompt_tokens=2000, completion_tokens=300, cached_tokens=1200, ) normalized = normalize_usage(usage, provider="qwen", api_mode="chat_completions") assert normalized.cache_read_tokens == 1200 assert normalized.input_tokens == 800 def test_normalize_usage_nested_details_win_over_qwen_flat_top_level(): """When both shapes are present, the nested OpenAI-style value wins and the flat Qwen field is not double-read.""" usage = SimpleNamespace( prompt_tokens=2000, completion_tokens=100, prompt_tokens_details=SimpleNamespace(cached_tokens=900), cached_tokens=1200, ) normalized = normalize_usage(usage, provider="qwen", api_mode="chat_completions") assert normalized.cache_read_tokens == 900 assert normalized.input_tokens == 1100