# 2.11 — Anthropic non-streaming response translation (GREEN)

Phase: 2 — LLM surfaces
Stage: GREEN
Depends on: [2.10](2.10-anthropic-request-translation.md), [2.9](2.9-canonical-request-model.md)

A non-streaming `POST /llm/v1/messages` gets an Anthropic Messages response, built
from whatever the upstream returned. Upstreams speak the OpenAI chat-completion shape;
the client on this surface must never see it.

- [ ] The response body is `{"id","type":"message","role":"assistant","content":[...],
      "model","stop_reason","stop_sequence","usage":{"input_tokens","output_tokens"}}`
      with `type` literally `message` and `role` literally `assistant`
- [ ] `model` echoes the model name the client sent, not an upstream-internal name
- [ ] Upstream `finish_reason` `stop` becomes `stop_reason` `end_turn`, and `length`
      becomes `max_tokens`
- [ ] A generation halted by a client-supplied stop sequence reports `stop_reason`
      `stop_sequence` and puts the matched string in `stop_sequence`; otherwise
      `stop_sequence` is null and present, not omitted
- [ ] Upstream `prompt_tokens` becomes `usage.input_tokens` and `completion_tokens`
      becomes `usage.output_tokens`; no other usage fields are invented
- [ ] `reasoning_content`, which vLLM returns as a field separate from `content` for
      `reasoning`, becomes a `thinking` content block that PRECEDES the `text` block
- [ ] When `reasoning_content` is absent or empty, no `thinking` block is emitted and
      `content` holds only the `text` block
- [ ] When `content` is empty but `reasoning_content` is not, the `thinking` block is
      still returned rather than an empty `content` array
- [ ] `Content-Type` is `application/json`, and no OpenAI field name such as `choices`,
      `finish_reason` or `object` appears anywhere in the body

`reasoning` runs DeepSeek-R1-Distill-Qwen-32B under vLLM with
`--reasoning-parser=deepseek_r1`, which is why the reasoning text arrives as its own
field and maps cleanly onto a thinking block.

## Verify

```bash
curl -s localhost:8080/llm/v1/messages -H 'content-type: application/json' \
  -d '{"model":"reasoning","max_tokens":64,"messages":[{"role":"user","content":"hi"}]}'
# expected: 200, type=message, role=assistant, content[0].type=thinking, content[1].type=text

curl -s localhost:8080/llm/v1/messages -H 'content-type: application/json' \
  -d '{"model":"reasoning","max_tokens":64,"messages":[{"role":"user","content":"hi"}]}' \
  | grep -c -E '"choices"|"finish_reason"|"object"'
# expected: 0

curl -s localhost:8080/llm/v1/messages -H 'content-type: application/json' \
  -d '{"model":"qwen2.5:3b-instruct","max_tokens":4,"messages":[{"role":"user","content":"count to fifty"}]}'
# expected: 200, stop_reason=max_tokens, usage has input_tokens and output_tokens only
```