Use this model when
- The workload fits the text or chat tasks shown in this model's current catalog record.
- The input fits within the published 64K-token context record, with output and system overhead budgeted separately.
DeepSeekChat
DeepSeek R1 Distill Qwen 14B (Free)
deepseek-r1-distill-qwen-14b:freeDeepSeek R1 Distill Qwen 14B (Free) by DeepSeek.
- Context
- 64K tokens
- Endpoint
Pricing
Input$0 / 1M
Output$0 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M
Prompt cache writes and reads are included at no additional cost.
Quick Start
Select an endpoint and copy a working example for this model.
Endpoint:
python
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="deepseek-r1-distill-qwen-14b:free", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="deepseek-r1-distill-qwen-14b:free",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsCommon7 params
modelmessagesmax_tokenstemperaturetop_pstreamtoolsExtended4 params
reasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
Fit the request contract before production.
Check before production
- Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
- Estimate a representative request from the current Free price fields instead of extrapolating from a tiny prompt.
- Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.
Compare with Other Models
See how this model compares to others from the same provider.
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 is DeepSeek's large-scale Mixture-of-Experts (MoE) model and the general availability (GA) release of DeepSeek V4 Pro. It is designed for high-capability workloads requiring advanced reasoning, coding, and agentic task execution. As the production-ready V4 Pro release, it is well suited for complex software engineering, long-horizon agent workflows, and demanding reasoning tasks where reliability and model capability are critical.
- Context
- 1M
- Input
- $0.435/M
- Output
- $0.87/M
DeepSeek V3.2 (Thinking)
DeepSeek-V3.2 is an efficiency-focused large model that combines strong reasoning with reliable tool use. It introduces DeepSeek Sparse Attention to lower compute costs for long contexts while preserving quality, and uses large-scale reinforcement learning to reach GPT-5-class reasoning (including top IMO/IOI results). An agentic task-synthesis pipeline improves how it reasons with tools in interactive settings — and developers can toggle reasoning on or off as needed.
- Context
- 163.8K
- Input
- $0.19/M
- Output
- $0.275/M
DeepSeek V3.2 Speciale
DeepSeek-V3.2-Speciale is a high-compute edition of V3.2 built for top-tier reasoning and agent performance. Using DeepSeek Sparse Attention and extensive reinforcement learning, it surpasses GPT-5 on tough reasoning benchmarks and approaches Gemini 3 Pro–level capability, while still remaining strong at coding and tool use. It also draws on a large agent-training pipeline to boost reliability and generalization in interactive environments.
- Context
- 163.8K
- Input
- $0.28/M
- Output
- $0.40/M
DeepSeek V3.1
DeepSeek-V3.1 is a hybrid reasoning model (671B total / 37B active) that supports switchable thinking and non-thinking modes. It extends DeepSeek-V3 with long-context training up to 128K tokens and efficient FP8 inference. It delivers faster performance while matching DeepSeek-R1 on tough reasoning and coding tasks, and supports structured tool use and agent workflows.
- Context
- 163.8K
- Input
- $0.20/M
- Output
- $0.80/M