deepseek
DeepSeek: DeepSeek V3.1
deepseek/deepseek-chat-v3.1
A text-model candidate for comparing multilingual instructions and structured responses. Use the same prompt and rubric for each language rather than treating an English result as a multilingual benchmark.
Provider-supported parameters
These are upstream capabilities. Jevrouter supports the subset described in the API contract; chat input is currently text only.
Capabilities are imported from OpenRouter’s catalog. This does not mean every model has been independently tested by Jevrouter.
Catalog updated: 2026-09-22T08:51:50.371Z · Price version: cmubxr2ec07uajn0zb771skjb:3
Provider pricing fields · USD per indicated unit
{
"prompt": "0.00000025",
"completion": "0.00000095",
"input_cache_read": "0.00000013"
}JEVROUTER INTEGRATION NOTES · REVIEWED 2026-09-22
What to check in your application
- Reasoning-mode controls outside the documented Jevrouter contract are not forwarded.
- If JSON is required, use a supported response_format and validate the resulting object in your application.
Start with the example below, then test representative inputs against your own acceptance criteria.
Make a request
curl --no-buffer https://api.jevrouter.io/v1/chat/completions \
-H "Authorization: Bearer $JEVROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d @- <<'JSON'
{
"model": "deepseek/deepseek-chat-v3.1",
"messages": [
{
"role": "user",
"content": "Return a JSON object with language and intent for: 请帮我更新发票地址。"
}
],
"max_tokens": 1024
}
JSONPlan your integration
Related models to evaluate
Use identical inputs and scoring rules. No ranking is implied by this selection.
Source: OpenRouter model catalog. Imported descriptions and capabilities are provider declarations, not independent benchmark results. Jevrouter currently uses OpenRouter as its managed upstream.