Short answer
“DeepSeek V3.1 Thinking” refers to the Think mode of DeepSeek-V3.1, not a separately established model named “DeepSeek-V3.1 Thinking.” DeepSeek’s August 21, 2025 release note described V3.1 as one model with Think and Non-Think modes, mapping deepseek-reasoner to thinking and deepseek-chat to non-thinking. It also said users could switch modes in DeepSeek Chat with the DeepThink button. (DeepSeek V3.1 release note)
For a new production API integration, don’t use that historical mapping as a way to select V3.1. Current DeepSeek API documentation lists newer models, and its change log says the legacy deepseek-chat and deepseek-reasoner names point to V4 Flash modes during the transition period—not to V3.1. The current quick-start uses deepseek-flash, which DeepSeek says is served by DeepSeek-V4.1-Flash-1017. (DeepSeek API change log)
What the V3.1 release claimed
The V3.1 release note stated 128K context for both modes. It also claimed that V3.1-Think reached answers in less time than DeepSeek-R1-0528, and reported better SWE / Terminal-Bench results, stronger multi-step reasoning for complex search tasks, and improved thinking efficiency. These are release-note claims: the available text does not provide the scores, timing figures, or evaluation methodology needed to establish performance for your workload. (DeepSeek V3.1 release note)
The same note described stronger tool use and multi-step agent tasks, and said strict function calling was supported in the Beta API. That beta qualification matters: the release note is not a current implementation guide or a guarantee that V3.1-specific tool behavior remains available. (DeepSeek V3.1 release note)
What to use for a production API today
DeepSeek’s current quick-start shows a request to https://api.deepseek.com/chat/completions using a Bearer API key, the deepseek-flash model name, and the thinking and reasoning_effort fields. The quick-start says deepseek-flash is now served by DeepSeek-V4.1-Flash-1017. This example is for the current model, not a V3.1 request. (DeepSeek quick-start)
curl https://api.deepseek.com/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DEEPSEEK_API_KEY}" \
-d '{
"model": "deepseek-flash",
"messages": [{"role": "user", "content": "Solve this problem and explain the result."}],
"thinking": {"type": "enabled"},
"reasoning_effort": "high",
"stream": false
}'
Before deployment, confirm the current model ID and its capabilities with DeepSeek’s model-list documentation/API, then validate the request format, thinking controls, context and output limits, tool behavior, and current pricing for your provider and account. DeepSeek says its model-list endpoint provides metadata such as context windows, output limits, supported effort levels, and per-protocol API capabilities. (DeepSeek model-list documentation)
A separate general Thinking Mode guide also shows thinking controls and an SDK example using deepseek-flash, but its parameter notes name other models too. Don’t assume those model-specific details apply to V3.1. (DeepSeek Thinking Mode guide)
Which should you choose?
- Trying to understand or reproduce the original V3.1 release: use the release-note mapping as historical context:
deepseek-reasonermeant Think anddeepseek-chatmeant Non-Think in that release. - Building a new DeepSeek API integration: select from the current model catalog and test the current thinking controls. Don’t treat the legacy V3.1-era names as V3.1 selectors; current documentation associates them with V4 Flash modes. (DeepSeek API change log)
- Choosing on cost or speed: don’t rely on the V3.1 release note’s qualitative claims or its dated 2025 pricing announcement. It gives no current price or billing-unit quote. Compare current prices and run representative workload tests before committing. (DeepSeek V3.1 release note)
In short: V3.1 Thinking was a mode of V3.1, but the old API names no longer identify that model in current documentation. For a new integration, use the current catalog and verify the selected model and settings rather than copying the historical V3.1 mapping.