DeepSeek V3.2 Exp
by DeepSeekdeepseek/deepseek-v3.2-exp
LLMCompare cannot run DeepSeek V3.2 Exp yet.No route to this model is configured on this site yet. Its prices and parameters are listed below for reference.
See the models that can be run nowDeepSeek V3.2 Exp at a glance
- Context length
- 163,840 tokens
- Maximum output
- 147,456 tokens
- Accepts
- Text
- Returns
- Text
- Features
- JSON outputTool calling
- Reasoning effort levels
- Not supported
- Released
- 29 Sep 2025
- Knowledge cutoff
- 31 July 2025
Model facts synced from the OpenRouter model list on 21 Oct 2026.
DeepSeek V3.2 Exp pricing by route
A route is the service LLMCompare calls to reach the model. DeepSeek V3.2 Exp has 1 route. Prices are in US dollars.
| Route | On LLMCompare | Inputper 1M tokens | Outputper 1M tokens | Price verified on | Source |
|---|---|---|---|---|---|
| OpenRouterDefault | Not configured | $0.27 | $0.41 | openrouter.ai/deepseek/deepseek-v3.2-exp |
The cost shown in an LLMCompare report is calculated from these prices as they were at the time of the run.
Cost calculator
Estimate what a batch of calls to DeepSeek V3.2 Exp costs. The starting numbers are examples. Change them to match your task.
- Cost per call
- $0.00048
- 1 credits
- Cost of 100 calls
- $0.0475
- 100 credits
Calculated from the OpenRouter prices verified on 21 Oct 2026. Credits are the cost plus a 20% service fee, at $0.001 per credit, rounded up for each call, with a minimum of 1 per call. A real run is charged for the usage the provider reports.
Parameters you can set
These are the parameters LLMCompare can set when it calls the model through OpenRouter.
| Parameter | Type | Range or options | Default |
|---|---|---|---|
temperature | Number | 0 to 2in steps of 0.1 | Provider default |
top_p | Number | 0 to 1in steps of 0.05 | Provider default |
max_tokens | Integer | 1 to 147,456 | Provider default |
response_format | One of | textjson_object | Provider default |
About DeepSeek V3.2 Exp
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Description as published for this model. LLMCompare has not edited it.
Models to compare with DeepSeek V3.2 Exp
Text models from the same provider and models at the nearest price. The order is not a ranking.
| Add to comparison | Model | Context | Max output | Input priceper 1M tokens | Output priceper 1M tokens | Features | Released | On LLMCompare |
|---|---|---|---|---|---|---|---|---|
DeepSeek V3.2DeepSeekdeepseek/deepseek-v3.2 | 164k | 65.5k | $0.28 | $0.42 | JSONToolsCache | No route yet | ||
DeepSeek V4.1 Flash 1017DeepSeekdeepseek/deepseek-v4.1-flash-1017 | 1.05M | 393k | $0.15 | $0.60 | ReasoningJSONToolsCacheVision | No route yet | ||
DeepSeek V4 Flash Vision ExpDeepSeekdeepseek/deepseek-v4-flash-vision-exp | 1.05M | 262k | $0.2156 | $0.6468 | ReasoningJSONToolsCacheVision | No route yet | ||
Hunyuan A13B InstructTencenttencent/hunyuan-a13b-instruct | 131k | 118k | $0.14 | $0.57 | JSON | No route yet | ||
Nemotron 3.5 Super VLNVIDIAnvidia/nemotron-3.5-super-vl | 1.05M | 236k | $0.15 | $0.50 | JSONToolsCacheVision | No route yet | ||
GLM 5.3 FlashZ.aiz-ai/glm-5.3-flash | 1.05M | 944k | $0.15 | $0.50 | ReasoningJSONToolsCacheVision | No route yet |
Questions about DeepSeek V3.2 Exp
- How much does DeepSeek V3.2 Exp cost?
- On the OpenRouter route, DeepSeek V3.2 Exp costs $0.27 per 1 million input tokens and $0.41 per 1 million output tokens, in US dollars. LLMCompare verified this price on 21 Oct 2026.
- What is the context length of DeepSeek V3.2 Exp?
- DeepSeek V3.2 Exp has a context length of 163,840 tokens and returns up to 147,456 output tokens in one call.
- What inputs does DeepSeek V3.2 Exp accept?
- DeepSeek V3.2 Exp accepts these inputs: Text. DeepSeek V3.2 Exp returns: Text.
- Can I test DeepSeek V3.2 Exp on my own data?
- Not yet. LLMCompare lists the prices and parameters of DeepSeek V3.2 Exp, but no route to DeepSeek V3.2 Exp is configured on LLMCompare, so it cannot be selected for an evaluation.