DeepSeek: DeepSeek V3.1
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Identifiers & provenance
- Primary ID
- deepseek/deepseek-chat-v3.1
- OpenRouter ID
- deepseek/deepseek-chat-v3.1
- Canonical slug
- deepseek/deepseek-chat-v3.1
Source semantics
- Arena rank is a human-preference leaderboard signal, not a universal truth metric.
- OpenRouter usage/popularity reflects adoption/traffic, not benchmark quality.
- Pricing fields may differ by provider and can include extra modes beyond prompt/completion.
Read more on Methodology & data sources.
Description
DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config) The model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. It succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.
Raw fields snapshot
{
"id": "deepseek/deepseek-chat-v3.1",
"name": "DeepSeek: DeepSeek V3.1",
"description": "DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. \n\nIt succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.",
"created": 1755779628,
"canonical_slug": "deepseek/deepseek-chat-v3.1",
"hugging_face_id": "deepseek-ai/DeepSeek-V3.1",
"source_type": "openrouter_only",
"context_length": 32768,
"max_completion_tokens": 7168,
"is_moderated": false,
"architecture": {
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"PPM": {
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"openrouter_raw": {
"id": "deepseek/deepseek-chat-v3.1",
"canonical_slug": "deepseek/deepseek-chat-v3.1",
"hugging_face_id": "deepseek-ai/DeepSeek-V3.1",
"name": "DeepSeek: DeepSeek V3.1",
"created": 1755779628,
"description": "DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context training process, reaching up to 128K tokens, and uses FP8 microscaling for efficient inference. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)\n\nThe model improves tool use, code generation, and reasoning efficiency, achieving performance comparable to DeepSeek-R1 on difficult benchmarks while responding more quickly. It supports structured tool calling, code agents, and search agents, making it suitable for research, coding, and agentic workflows. \n\nIt succeeds the [DeepSeek V3-0324](/deepseek/deepseek-chat-v3-0324) model and performs well on a variety of tasks.",
"context_length": 32768,
"architecture": {
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"default_parameters": {},
"expiration_date": null
}
}