DeepSeek R1
deepseek/deepseek-r1The open reasoning model that reset expectations, deliberate chain-of-thought quality at open-weight prices.
Model overview
Context window
164K
tokens
Input price
—
quoted on request
Output price
—
quoted on request
Weekly volume
—
tokens / week
Open weights
Yes
self-hostable
Capabilities
4
of 11 tags
Description
DeepSeek R1 is a reasoning model from DeepSeek. Webparam does not stock this model yet and can source it on request — tell us what you need it for and we will come back with availability and a price. DeepSeek’s stated focus is open-weight frontier reasoning at disruptive economics. Its mean it can also be self-hosted under its licence.
Capabilities
- Reasoning
- Spends extra billed tokens thinking before it answers, trading latency and cost for accuracy.
- Tool calling
- Calls functions you define and returns their arguments as structured data, the basis of agents.
- Streaming
- Emits tokens as they are generated, so answers appear progressively rather than all at once.
- Open weights
- The parameters are published, so the model can be inspected, fine-tuned and self-hosted under its licence.
Strengths & weaknesses
Strengths
- Open-weight reasoning near frontier quality
- Transparent thinking traces
- Dramatic cost advantage over closed reasoning models
Weaknesses
- High output token consumption
- Slower first token than language models
Pricing
| Rate | Price | Unit |
|---|---|---|
| Input | — quoted on request | — |
| Output | — quoted on request | — |
This model is not provisioned yet; the rate is quoted on request.
Context window
164Ktokens
covers prompt and response together, so a long input leaves less room for the answer.
Supported features
| Feature | Support |
|---|---|
| Tool calling | Supported |
| JSON mode | Not supported |
| Streaming | Supported |
| Vision input | Not supported |
| Audio input | Not supported |
| Long context | Not supported |
| Open weights | Supported |
| Reasoning | Supported |
| Fine-tunable | Not supported |
| Batch | Not supported |
| Caching | Not supported |
Example use cases
Math and logic problems
Analysis pipelines
Verification passes
Comparison
Compare against anything →| Attribute | DeepSeek R1 | DeepSeek V3.2 | Kimi K2 Thinking |
|---|---|---|---|
| Context window | 164K | 128K | 256K |
| Input price | R0.00 | R0.00 | R0.00 |
| Output price | — not charged | — not charged | — not charged |
| Tool calling | Supported | Supported | Supported |
| JSON mode | Not supported | Supported | Not supported |
| Vision input | Not supported | Not supported | Not supported |
| Open weights | Supported | Supported | Supported |
| Prompt caching | Not supported | Supported | Not supported |
Best value in each row is highlighted. Blank cells are facts the supplier does not publish, not zeroes.
Provider information
View DeepSeek →DeepSeek · 深度求索 · Hangzhou, China · est. 2023
Spun out of a quantitative trading firm, DeepSeek built its reputation by releasing open-weight models that matched closed frontier quality at a fraction of the price. Its V-series redefined cost expectations for production LLMs, and its R-series brought open reasoning models into serious enterprise conversations.
Recent releases
Documentation
Frequently asked questions
Input is billed at R0.00 per 1M tokens, and there is no separate output charge. Every figure on this page is an illustrative placeholder.
Yes. DeepSeek R1's parameters are published, so it can be downloaded, inspected, fine-tuned and self-hosted under its licence. Self-hosting trades per-token pricing for infrastructure you run.
Prompt and response share one window: DeepSeek R1 accepts up to 164K tokens in total, so a long input leaves less room for the answer. The median across reasoning models is 200K. Recall is strongest well inside the limit. Test at your real prompt depth before relying on the ceiling.
DeepSeek V3.2 is the closest neighbour in the catalogue, with 128K of context at R0.00 per 1M tokens in. Put both in a comparison before wiring a fallback chain, so the trade-off is a decision rather than a default.