Environment

• opencode version: 1.18.x (Windows 11, PowerShell terminal)
• Model: deepseek-v4-flash (accessed via an OpenAI-compatible interface)
• Access method: third-party relay/proxy service (OpenAI-compatible protocol)

Problem description

When using a relay/proxy, the model's reasoning content is not placed in the standard reasoning_content field. Instead, it is mixed directly into the content field and pushed with extremely fine granularity (1-4 characters per chunk), word by word.

opencode renders the beginning of the content stream (the reasoning text before the actual answer) as a Thought block. Because the granularity is so fine, this produces a spam of one Thought line per chunk, causing the terminal to scroll uncontrollably.

Steps to reproduce

1. Configure an OpenAI-compatible relay provider and connect a reasoning-capable model (e.g. deepseek-v4-flash)
2. Send a complex question that triggers deep reasoning
3. Observe the TUI: during the reasoning phase, a large number of single-line Thought indicators scroll by rapidly, spamming the screen

SSE evidence (raw captured data)

1. reasoning_content is empty in every chunk

Out of 2081 streamed chunks captured from the same request, 0 chunks carried a non-empty reasoning_content field:

data: {"id":"xxx-...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","content":"","reasoning_content":"","function_call":null,"refusal":"","tool_calls":[]},"finish_reason":""}],"usage":null}

2. Reasoning text is pushed word-by-word inside content

2079 non-empty content chunks, with the reasoning text cut into 1-4 character fragments:

data: {"id":"xxx-...","choices":[{"index":0,"delta":{"content":"这个","reasoning_content":"","..." }},"finish_reason":""}]}
data: {"id":"xxx-...","choices":[{"index":0,"delta":{"content":"问题","reasoning_content":"","..." }},"finish_reason":""}]}
data: {"id":"xxx-...","choices":[{"index":0,"delta":{"content":"涉及","reasoning_content":"","..." }},"finish_reason":""}]}
data: {"id":"xxx-...","choices":[{"index":0,"delta":{"content":"分布式","reasoning_content":"","..." }},"finish_reason":""}]}
...

3. Chunk length statistics

• Total chunks: 2081
• Chunks with non-empty reasoning_content: 0
• Non-empty content chunks: 2079
• Total content characters: 4438
• Average chunk length: 2.1 characters
• 93% of chunks are ≤ 4 characters long

4. Structure of the concatenated content

After concatenating all 2079 chunks, the content reads as: a reasoning preamble ("I will build the solution via ...") followed by the full answer. There is no protocol-level separator between the reasoning section and the actual answer (no reasoning_content, no thinking field), so opencode cannot distinguish between them.

Expected behavior

Reasoning content should be delivered via a standard field (e.g. OpenAI's reasoning_content / Anthropic's thinking), or there should be a recognizable boundary between the reasoning text and the answer inside the content stream. opencode should not misclassify the leading text of an ordinary content stream as a reasoning block and render it per chunk.

Impact

• Terminal spam, making the conversation unreadable
• The /thinking toggle cannot hide these lines (it can only hide proper reasoning blocks; this content arrives via the content field, so hiding has no effect)
• No way to keep the model's reasoning ability while removing the visual noise

Notes

The root cause is likely the relay service's non-compliant passthrough of the upstream reasoning stream. At the same time, opencode's recognition/rendering strategy for reasoning text inside the content stream could be more robust (e.g. coalescing rendering when detecting a burst of consecutive high-frequency short chunks). Responsibility attribution is up to the opencode maintainers.

Attachments

• Full SSE capture file: wb_stream2.txt (681KB, 2081 chunks)
• Analysis script: analyze_stream.py (paths contain no service-provider information)