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fix(gateway): normalize reasoning tokens - #3774
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…ing-tokens # Conflicts: # apps/gateway/src/lib/logs.spec.ts # apps/gateway/src/lib/logs.ts
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Included review availability: Your plan provides up to 4 included reviews per hour; 1 remains after this review. WalkthroughThe gateway now normalizes separately reported reasoning tokens into completion counts when totals confirm they are excluded. Response totals and costs use prompt plus normalized completion tokens. Provider parsers and tests cover nested reasoning usage fields across providers. ChangesReasoning token accounting
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: ⚪ Minimal · up to The PR centralizes reasoning-token normalization for billing and usage reporting; no actionable merge-blocking risk remains, so it is merge-ready after normal checks and review. Suggested reviewers: Sequence Diagram(s)sequenceDiagram
participant Provider
participant parseProviderResponse
participant normalizeCompletionTokens
participant transformResponseToOpenAI
participant calculateCosts
Provider->>parseProviderResponse: return usage fields
parseProviderResponse->>normalizeCompletionTokens: normalize completion tokens
normalizeCompletionTokens-->>parseProviderResponse: return normalized completion count
parseProviderResponse->>transformResponseToOpenAI: pass normalized usage
transformResponseToOpenAI->>calculateCosts: pass prompt and completion tokens
calculateCosts-->>transformResponseToOpenAI: return calculated costs
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| (costs.promptTokens ?? calculatedPromptTokens ?? 0) + | ||
| (costs.completionTokens ?? calculatedCompletionTokens ?? 0) + | ||
| (reasoningTokens ?? 0), | ||
| (costs.completionTokens ?? calculatedCompletionTokens ?? 0), |
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When EXPERIMENTAL is enabled and an upstream response reports positive reasoning tokens, this now returns total_tokens = prompt_tokens + completion_tokens, but apps/gateway/src/chat-basic.e2e.ts:161-165 still asserts that reasoning_tokens must be added again. Any reasoning-capable model exercised by that suite will therefore fail even though completion_tokens has intentionally become inclusive; update the existing assertion as part of this semantic change.
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Inline comments:
In `@apps/gateway/src/lib/costs.ts`:
- Line 740: Update the fallback calculation around calculatedCompletionTokens
and totalOutputTokens to include separately reported reasoning tokens when
completionTokens is unavailable. Ensure the resulting completion count and
totalTokens passed to calculateCosts remain consistent, while preserving the
existing behavior when completionTokens is present.
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apps/gateway/src/aisdk/spec.tsapps/gateway/src/chat/chat.tsapps/gateway/src/chat/tools/extract-token-usage.spec.tsapps/gateway/src/chat/tools/extract-token-usage.tsapps/gateway/src/chat/tools/parse-provider-response.spec.tsapps/gateway/src/chat/tools/parse-provider-response.tsapps/gateway/src/chat/tools/transform-response-to-openai.spec.tsapps/gateway/src/chat/tools/transform-response-to-openai.tsapps/gateway/src/lib/costs.spec.tsapps/gateway/src/lib/costs.tspackages/models/src/models/deepseek.tspackages/models/src/models/google.ts
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| totalTokens === null | ||
| ) { | ||
| return completionTokens; |
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When an xAI-style response reports completion_tokens and separate reasoning_tokens but omits total_tokens, this returns the exclusive completion count unchanged; the non-streaming parser similarly synthesizes prompt + completion before calling this helper, so the equality check also fails there. Since calculateCosts now ignores its reasoning-token argument, such responses underbill output tokens and expose incorrect completion/total usage; retain a provider-aware fallback for known separate-reporting formats when the total is unavailable.
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| if (completionTokens === null && reasoningTokens !== null) { | ||
| calculatedCompletionTokens = | ||
| (calculatedCompletionTokens ?? 0) + reasoningTokens; |
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When an upstream response provides reasoning_tokens but omits completion_tokens while still returning visible content, both the streaming and non-streaming paths estimate a non-null completion count before calling calculateCosts (chat.ts:11158-11166 and 14133-14166). This guard therefore does not run, so the separately reported reasoning is excluded from billing, logs, and returned completion totals; base the fallback on whether the upstream count was absent rather than whether the already-estimated argument is null.
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Adds Tencent Cloud's OpenAI-compatible model gateway (**TokenHub**) as the `tencent` provider with **19 mappings** — 17 chat and 2 text embedding — of which 6 are new catalogue entries (Hy4 Preview, Hy-MT2 Plus, GLM-5 Turbo, GLM-5V Turbo, and two Kinfra text embedding models under a new `kinfra` family). Every mapping is proven with a live request; see below. Base URL `https://tokenhub-intl.tencentcloudmaas.com/v1`, key `LLM_TENCENT_API_KEY`, Singapore region. ## Region matters for pricing TokenHub's rate card is **three region tabs with different prices for the same models** — Singapore, Guangzhou, Silicon Valley. GLM-5.1 is flat 1.4/4.4 in Singapore but input-length-tiered in Guangzhou; Kimi K3 is 3/15 vs 2.731/13.653. All mappings here carry **Singapore** prices, matching the only endpoint wired up. Adding a region later means adding its own mappings, never reusing these. ## Mappings | Model | externalId | in / cache / out (USD/M) | ctx | maxOut | reasoning efforts | vision | tool_choice | json / schema | |---|---|---|---|---|---|---|---|---| | hy3 | `hy3` | 0.132 / 0.033 / 0.528 | 262144 | 131072 | none…xhigh | ✗ | all | ✓ / ✓ | | hy4-preview *(new)* | `hy4-preview` | 0.834 / 0.042 / 2.501 | 1000000 | 65536 | all 7 | ✗ | all | ✓ / ✓ | | hy-mt2-plus *(new)* | `hy-mt2-plus` | 0.074 / — / 0.295 | 8192 | 4096 | — | ✗ | — | ✗ / ✗ | | deepseek-v4-flash | `deepseek-v4-flash-202605` | 0.14 / 0.0028 / 0.28 | 1000000 | 393216 | all 7 | ✗ | auto,none | ✓ / ✗ | | deepseek-v4-pro | `deepseek-v4-pro-202606` | 0.435 / 0.00363 / 0.87 | 1000000 | 393216 | all 7 | ✗ | auto,none | ✓ / ✗ | | glm-5.2 | `glm-5.2` | 1.4 / 0.26 / 4.4 | 1000000 | 128000 | all 7 | ✗ | all | ✓ / ✗ | | glm-5.1 | `glm-5.1` | 1.4 / 0.26 / 4.4 | 200000 | 128000 | low…max | ✗ | all | ✓ / ✗ | | glm-5 | `glm-5` | 1 / 0.2 / 3.2 | 200000 | 128000 | all 7 | ✗ | all | ✓ / ✗ | | glm-5-turbo *(new)* | `glm-5-turbo` | 1.2 / 0.24 / 4 | 200000 | 128000 | low…max | ✗ | all | ✓ / ✗ | | glm-5v-turbo *(new)* | `glm-5v-turbo` | 1.2 / 0.24 / 4 | 200000 | 128000 | low…max | ✓ | all | ✓ / ✗ | | kimi-k3 | `kimi-k3` | 3 / 0.3 / 15 | 1048576 | 1048576 | all 7 | ✓ | auto,none,required | ✓ / ✓ | | kimi-k2.7-code | `kimi-k2.7-code` | 0.95 / 0.19 / 4 | 262144 | 262144 | minimal…max | ✓ | auto,none | ✓ / ✓ | | kimi-k2.7-code-highspeed | `kimi-k2.7-code-highspeed` | 1.9 / 0.38 / 8 | 262144 | 262144 | minimal…max | ✓ | auto,none | ✓ / ✓ | | kimi-k2.6 | `kimi-k2.6` | 0.858 / 0.145 / 3.566 | 262144 | 262144 | all 7 | ✓ | auto,none | ✓ / ✓ | | minimax-m3 | `minimax-m3` | 0.3 / 0.06 / 1.2 *(tiered)* | 1000000 | 131072 | low…max | ✓ | all | ✗ / ✗ | | minimax-m2.7 | `minimax-m2.7` | 0.3 / 0.06 / 1.2 | 200000 | 128000 | low…max | ✗ | all | ✗ / ✗ | | mimo-v2.5-pro | `mimo-v2.5-pro` | 0.435 / 0.0036 / 0.87 | 1000000 | 131072 | none,low,medium,high | ✗ | all | ✓ / ✗ | Embeddings (new): `kinfra-text-embedding-0.6b` 0.07, `kinfra-text-embedding-4b` 0.084. `minimax-m3` uses `pricingTiers`: ≤512K at 0.3/1.2/0.06, above at 0.6/2.4/0.12. ## The DeepSeek trap TokenHub serves DeepSeek V4 twice, and the naming is counterintuitive — the **dated** ids are the cheap "Vendor Direct" passthrough: | API id | rate card row | in / out | |---|---|---| | `deepseek-v4-pro-202606` | Vendor Direct | **0.435 / 0.87** | | `deepseek-v4-pro` | Tencent-hosted | 1.74 / 3.48 | Both mappings point at the dated ids. Mapping the undated ones would have billed DeepSeek V4 Pro at **4x**. The Tencent-hosted variants are deliberately not mapped (documented in-line). ## How prices were verified Reconciled end-to-end through a local gateway against the persisted `log` row, not just the upstream `usage` block. `tencent/hy3`, 26 prompt / 719 completion (660 reasoning): ``` input 26 x 0.132/M = 3.432e-06 log.input_cost 3.432e-06 output 719 x 0.528/M = 3.79632e-4 log.output_cost 0.000379632 total log.cost 0.000383064 ``` `tencent/kimi-k2.6`, 22 prompt (3 cached) / 195 completion — exercises all three rates including a real cache hit: ``` uncached 19 x 0.858/M = 1.6302e-05 log.input_cost 1.6302e-05 cached 3 x 0.145/M = 4.35e-07 log.cached_input_cost 4.35e-07 output 195 x 3.566/M = 6.9537e-04 log.output_cost 0.00069537 total log.cost 0.000712107 ``` **Reasoning tokens are inside `completion_tokens`** (ct=719 with rt=660), so output must not be billed on completion + reasoning — that would have charged the hy3 request above on 1379 tokens, ~1.9x over. This originally added `tencent` to the `completionIncludesReasoning` allowlist in `costs.ts`; #3774 has since removed that allowlist and normalized reasoning tokens globally, so the branch now carries no `costs.ts` change and the reconciliation below still holds. ## Capability probing corrected several assumptions **Vision — four models return 200 and cannot see.** A 1×1-pixel probe passes on hy3, deepseek-v4-flash/pro and minimax-m2.7; given a real image with a digit drawn in it, hy3 guesses "8" for a "7" and the DeepSeek pair replies "I cannot see the image." All four are `vision: false`. The genuinely sighted ones (glm-5v-turbo, kimi-k3/k2.7-code/k2.7-code-highspeed/k2.6, minimax-m3) read the digit correctly, which agrees with the existing native mappings. **json_schema is accepted-and-ignored by the GLM and MiniMax families.** They return 200 and answer in markdown prose (`**Name:** Alice`). Only a conformance check catches this, so those carry `jsonOutputSchema: false`. `glm-5` hard-400s instead, matching TokenHub's model list which omits Structured Output for GLM-5 alone. **json_object needed a non-JSON-natural prompt to test.** Asking for JSON proves compliance, not enforcement; asking "say hello in one sentence" under `json_object` shows minimax-m3/m2.7 and hy-mt2-plus ignore it (`jsonOutput: false`). DeepSeek 400s with "'messages' must contain the word 'json'" — its documented constraint, not missing support, so those stay `jsonOutput: true`. **tool_choice `required` needed a tool-worthy prompt.** With "hi" several models answer in text; with "What is the weather in Paris?" they all call the tool. The genuine rejections are the DeepSeek and Kimi K2.x reasoning models ("Thinking mode does not support this tool_choice"), plus named-function on kimi-k3. **Reasoning tiers were probed individually.** `none`/`minimal` are accepted but *don't stop the thinking* on glm-5.1, glm-5-turbo and glm-5v-turbo (~125-200 reasoning tokens either way), so those tiers are omitted rather than declared. hy3 400s on `max`; the Kimi K2.7 pair 400s on `none`; mimo-v2.5-pro accepts only none/low/medium/high, matching its native Xiaomi mapping. ## Tests `pnpm build` green, `packages/models` + `packages/actions` green (789 passed). Scoped e2e across all 16 mappings — 171 provider cases: ``` TEST_MODELS="tencent/hy3,tencent/hy-mt2-plus,tencent/deepseek-v4-flash,…" CI=true pnpm test:e2e ``` All pass. The one real failure was `tencent/mimo-v2.5-pro` JSON-schema (both streaming and not, 5 retries each): under `json_schema` it returns truncated or thinking-leaked bodies (`{"is_user`, `{The user wants`), reproduced 3/3 by hand. Fixed by setting `jsonOutputSchema: false`; re-ran and it passes. ## Hy4 Preview Tencent's next-generation Hunyuan model, `hy4-preview`, online on the Singapore endpoint since 2026-08-28. Prices come from the same Singapore rate card tab as the rest; I re-verified the tab by checking four known rows against the catalogue (Hy3 0.132/0.528/0.033, Hy-MT2-Plus, GLM-5.2, Kimi K3) before reading Hy4's row. Probed live rather than taken from the capability column: - **All seven reasoning tiers accepted**, and `none` genuinely disables thinking (rt=0, ct=2) — unlike Hy3, which 400s on `max`. - **Every `tool_choice` mode works** (auto / none / required / named), so no `supportedToolChoices` narrowing. - **json_object and json_schema are both enforced**, checked with a prompt that would not naturally be JSON and a strict extraction schema, not just a 200. - **No vision.** It accepts image parts and answers confidently: asked which digit a test image shows, it replied "I don't see an image attached". Same trap as Hy3. - Caching is real and nested inside `prompt_tokens` (1920 of 2108 cached on a repeat). `maxTemperature: 1` is the one non-obvious field. The deployment accepts temperature up to 2 **without erroring** and then never returns — a one-line prompt answers in 7s at 1.0 and had produced nothing after 420s at 1.9. That surfaced as the `temperature above ceiling` e2e case timing out six times. Clamping is per-mapping, so the other 20 `tencent` mappings are unaffected. ### Price reconciliation Reasoning request, 33 prompt / 6234 completion (6182 reasoning): ``` input 33 x 0.834/M = 2.7522e-05 log.input_cost 2.7522e-05 output 6234 x 2.501/M = 0.015591234 log.output_cost 0.015591234 total log.cost 0.015618756 ``` Billing output on completion + reasoning would have been 0.031052416 — 1.99x. Cache hit, 2108 prompt of which 1920 cached: ``` uncached 188 x 0.834/M = 0.000156792 log.input_cost 0.000156792 cached 1920 x 0.042/M = 8.064e-05 log.cached_input_cost 8.064e-05 output 2 x 2.501/M = 5.002e-06 total = 0.000242434 log.cost 0.000242434 ``` ### Not verified `contextSize` and `maxOutput` come from Tencent's model list (1M window / 960k input / 64k output) and could not be probed: the deployment silently accepts any `max_tokens` (1,000,000 included) without validating it, and the account's 1M TPM quota rejects any request large enough to exceed the context window before the window itself does. The same doc's Hy3 row matches what I verified independently in-browser. ### e2e ``` TEST_MODELS="tencent/hy4-preview" FULL_MODE=true CI=true CONCURRENT_TESTS=false pnpm test:e2e → 29 files passed, 113 tests passed, 0 failed ``` `CONCURRENT_TESTS=false` is needed: the default concurrent run trips Tencent's per-account serving-capacity limit (32 `rate_limit_error` upstream errors), which is a quota artefact, not a mapping defect. ## Every mapping proven with a live request One real request per mapping through a local gateway, pinned with `x-no-fallback: true` and `x-no-cache: true` so nothing can pass by falling back to another provider or replaying a cached body. Serialized, because concurrency trips the account's serving-capacity limit. **19/19 pass.** Chat mappings returned `finish=stop` with content and a computed cost; both embedding mappings returned vectors of the expected dimensionality and were billed on input tokens. | mapping | result | |---|---| | hy3 | ok · ct=78 · $4.5144e-05 | | hy4-preview | ok · ct=211 · $0.000561071 | | hy-mt2-plus | ok · ct=2 · $2.736e-06 | | deepseek-v4-flash | ok · ct=19 · $1.26e-05 | | deepseek-v4-pro | ok · ct=12 · $5.568e-05 | | glm-5.2 | ok · ct=261 · $0.0011974 | | glm-5.1 | ok · ct=170 · $0.0007872 | | glm-5 | ok · ct=80 · $0.000289 | | glm-5-turbo | ok · ct=193 · $0.0008068 | | glm-5v-turbo | ok · ct=58 · $0.000268 | | kimi-k3 | ok · ct=207 · $0.003417 | | kimi-k2.7-code | ok · ct=149 · $0.00062355 | | kimi-k2.7-code-highspeed | ok · ct=111 · $0.0009469 | | kimi-k2.6 | ok · ct=232 · $0.000834369 | | minimax-m3 | ok · ct=56 · $9.528e-05 | | minimax-m2.7 | ok · ct=222 · $0.00028254 | | mimo-v2.5-pro | ok · ct=43 · $7.63812e-05 | | kinfra-text-embedding-0.6b | ok · 1024 dims · 25 tok · $1.75e-06 | | kinfra-text-embedding-4b | ok · 2560 dims · 23 tok · $1.932e-06 | Embedding costs check out by hand: `25 x 0.07/M = 1.75e-06` and `23 x 0.084/M = 1.932e-06`. The embeddings response shape does not echo a cost, so those came from the persisted `log` rows. ### Two mappings removed as a result `kinfra-vl-embedding-2b` and `kinfra-vl-embedding-8b` **could never have served a request** and have been dropped. Upstream rejects them on the embeddings route: ``` Model kinfra-vl-embedding-2b does not support the requested protocol or capability /embeddings. Please switch the protocol/capability or check the console invocation examples. ``` They answer only on `POST /v1/embeddings/multimodal` with a structured `input` array (`{type:"text"|"image_url"|"video_url"}`) — verified working there, returning 2048 and 4096 dims. The gateway's embeddings route speaks only OpenAI-shaped `/v1/embeddings`, so re-adding them needs multimodal-embeddings support first. I had priced them at their text rate on the assumption they were reachable with text input; that assumption was never tested, and it was wrong. ### One upstream status change to watch `glm-5.1`, `glm-5` and `glm-5-turbo` are now listed `pre-offline` by Tencent. All three still serve normally (verified above), so they ship as-is — but they are flagged for retirement upstream and will need `deactivatedAt` when Tencent pulls them. ## Not included - **`tripo-3d-3.1` / `tripo-3d-p1`** — 3D generation. The catalogue has no 3D output capability (only image/video/embedding/speech/transcription/ocr/rerank), and neither model appears on TokenHub's pricing page or model list, so there is no verified rate to bill. Adding them means a new endpoint type, not a catalogue entry. - **`deepseek-v3.2`, `kimi-k2.5`, `minimax-m2.5`** — listed by `/v1/models` as `status: "pre-offline"` and every call 400s with "The model or service ID does not exist". Listed but not callable. - **Guangzhou / Silicon Valley regions** — different prices, no endpoint wired up. ## Note on the local suite `packages/actions/src/provider-key/env-inventory.spec.ts` fails 4 tests locally with Redis hook timeouts. It fails identically on a clean `origin/main` checkout with this branch's code absent, so it is pre-existing and environmental. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Added Tencent Cloud TokenHub as a supported provider with API-key authentication, streaming, cancellation, and provider branding. - Added Tencent-hosted models from DeepSeek, MiniMax, Kimi, GLM, Xiaomi, and Tencent’s Hy series. - Added support for vision, reasoning, tool use, structured JSON, and translation capabilities where available. - Added two Kinfra text embedding models to the model catalogue. - **Changes** - Removed two Kinfra multimodal embedding models from the catalogue. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
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Adds Atria ASI's OpenAI-compatible API (`api.atria-asi.ai`) as the `atria` provider, with its single model **Atria-Dawn-Preview** — a 744B agentic MoE from Shanghai AI Lab, post-trained on GLM-5.2, served on vLLM 0.26. Key env var is `LLM_ATRIA_KEY` (matching what is already configured; `LLM_RUNPOD_KEY` is the existing precedent for the non-`_API_KEY` suffix). ## Mapping | field | value | |---|---| | externalId | `Atria-Dawn-Preview` | | in / cached / out | **0 / — / 0** (free preview) | | contextSize | 262144 | | maxOutput | 65536 | | reasoning efforts | all 7 (`none`…`max`) | | vision | ✗ | | tool_choice | `auto`, `none` | | json / schema | ✗ / ✗ | Catalogued `free: true` (all-zero prices, satisfying the `free-models.spec.ts` invariant) and `stability: "unstable"` so a $0 mapping cannot win auto/cheapest routing. It stays directly addressable as `atria/atria-dawn-preview`. ## Pricing **There is no published rate card.** Atria is in free preview (new accounts get free tokens); there is no pricing page, no billing/usage endpoint (`/v1/usage`, `/v1/balance`, … all 404), and no third-party listing of an Atria-billed rate. Prices are therefore 0, which makes cost reconciliation trivial — a live gateway request returned `cost: 0` with every component of `cost_details` at 0. If Atria starts charging, this mapping must be re-priced before it can serve paid traffic. Reasoning tokens sit inside `completion_tokens` (ct=18 with rt=15), which is the current global norm — no `costs.ts` change is needed since #3774 normalized this. ## Probing corrected several assumptions **The advertised 256K context is real, but a payload cap hides it.** Probing with `'word '*N` (5 bytes/token) failed at ~175K tokens, which reads as a ~170K window. It is actually a ~875KB request-body limit. Re-probing with a byte-dense filler (`'a '*N`, 2 bytes/token) reached **262113 tokens OK / 262200 rejected** — exactly 262144, matching their docs. **Structured output is broken upstream.** `response_format` prepends a spurious opening brace, producing unparseable bodies: ``` {{\n"age": 31\n , "name": "P1"\n } {"{"city": "New York", "population": 8} ``` Measured **1/8 valid for `json_object`, 0/6 for `json_schema`**. The control matters: with no `response_format` at all, asking for JSON gives valid JSON **4/4**. So this is a guided-decoding defect, not a model limitation — hence `jsonOutput: false` and `jsonOutputSchema: false`, and `response_format` is absent from `supportedParameters`. **`tool_choice` `required` and named-function are broken.** Both return `finish_reason: "tool_calls"` with `tool_calls: null` and the raw template leaked into content (`<tool_call>get_weather<tool_call>get_weather<arg_key>city…`). `auto` and `none` work correctly, so `supportedToolChoices` narrows to those two. **All 7 reasoning tiers are genuinely honoured**, not accepted-and-ignored. The decisive check: a bogus `reasoning_effort: "banana"` is **rejected**, so the server validates the field. And `none` truly disables thinking — 0 reasoning chars across 3 runs vs 4242/4738 chars on the same prompts without it. **No vision.** Image input returns `upstream_error`. **Prompt caching is real but not dependable, so it is left undeclared.** Upstream does cache prefixes (`created_cache_tokens: 4800` then `cached_tokens: 4736` on a repeat), but a third identical call reported `cached_tokens: 0` again — hits only land when a request reaches the replica that built the prefix. Declaring `cachedInputPrice` opts the mapping into the prompt-caching e2e, which failed all 5 retries. Since the rate is 0 either way, the field is omitted rather than promising caching the gateway cannot rely on. Also verified: streaming (with usage), developer role, assistant prefill, `temperature`/`top_p`/penalties/`stop`/`seed`/`logprobs`/`n`. ## No website or legal metadata Atria publishes **no terms, privacy policy, or usage policy** — every candidate path 404s, and there are no such links in the site bundles or the console SPA. `providers.spec.ts` requires all four legal fields once `website` is set, so the entry carries `website: null` with `dataPolicy: null` (fail-closed for compliance routing) and `headquarters: "CN"`. Worth revisiting if Atria publishes terms. ## Tests - `pnpm build` green, `pnpm lint` green. - `pnpm test:unit`: **6940 passed**, 1 unrelated pre-existing flake (`admin-enterprise-deals.spec.ts` asserting a `createdAt` within a 6ms window — `expected …064 to be ≤ …058`); passes on rerun, and touches nothing in this diff. - Scoped e2e, after the caching fix: ``` TEST_MODELS="atria/atria-dawn-preview" FULL_MODE=true CI=true pnpm test:e2e → 30 files passed | 3 skipped, 110 tests passed, 0 failed ``` Every mapping was also proven with a live request through a local gateway pinned with `x-no-fallback: true` and `x-no-cache: true` — non-streaming chat, streaming (deltas assembling to `1, 2, 3` plus a usage chunk), and a tool call returning `{"city": "Oslo"}`, each billed at $0. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added Atria as a supported AI provider. * Added access to the Atria Dawn Preview model, including streaming and tool-use support. * Added Atria authentication and API connectivity. * Added Atria branding and provider logo display throughout the interface. * Improved compatibility for streaming responses from supported providers. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Problem
Billing used a provider allowlist to decide whether reasoning was already included in completion tokens. That list was incomplete and could double-count most providers while requiring manual updates for every new integration. A missing completion count also discarded a separately reported reasoning count.
Changes
Verification
pnpm formatpnpm buildSummary by CodeRabbit