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Models

DeepSeek V3 0324

By DeepSeek. 684.5 billion parameters, a context window of 163,840 tokens and the licence mit.

Facts

Released
2025-03-24 the day the repository was first published on Hugging Face Hugging Face, read
Licence
mit the licence the model card declares Hugging Face, read
Open weights
Yes the weights are published in this Hugging Face repository Hugging Face, read
Parameters
684.5 billion counted from the safetensors weight files Hugging Face, read
Active parameters
51.1 billion estimate estimated from the expert counts and sizes in the configuration model configuration, read
Knowledge cutoff
2024-07-31 the knowledge cutoff OpenRouter lists OpenRouter, read
Context window
  • 163,840 tokens the maximum position embeddings in the model configuration model configuration, read
  • 163,840 tokens the context length OpenRouter lists OpenRouter, read
Longest output
  • 147,456 tokens the largest output OpenRouter's first provider allows OpenRouter, read
Tool calling
  • No the chat template in the tokenizer configuration has no place for tool definitions Hugging Face, read
  • Yes OpenRouter lists tools among the supported parameters OpenRouter, read
Structured output
  • Yes OpenRouter lists structured outputs among the supported parameters OpenRouter, read
Reasoning controls
Unknown
Inputs and outputs
text in, text out Hugging Face, read
Good for
No source names a use.

Prices

Prices in US dollars per million tokens, as each source lists them
ProviderInputOutputContextAs ofSource
submodel direct0.200 USD0.800 USD75,000 older than 30 days; check the providermodels.dev, read
Deep Infra direct0.240 USD0.900 USD163,840 older than 30 days; check the providermodels.dev, read
DeepInfra through OpenRouter0.240 USD0.900 USD163,840OpenRouter endpoints, read
Meganova direct0.250 USD0.880 USD163,840 older than 30 days; check the providermodels.dev, read
SiliconFlow through OpenRouter0.250 USD1.00 USD163,840OpenRouter endpoints, read
Hugging Face direct0.270 USD1.12 USD163,840 older than 30 days; check the providermodels.dev, read
GMICloud through OpenRouter0.290 USD1.14 USD163,840OpenRouter endpoints, read
Crusoe direct0.500 USD1.50 USD163,840 older than 30 days; check the providermodels.dev, read

Run it on your own hardware

Weights are the sizes of the files a source lists, or an estimate from the parameter count where none does. Memory is an estimate: weights plus KV cache plus 512 MiB and 5 percent of the weights for runtime buffers. Check it against your hardware.

QuantizationWeightsMemory at 8,192 tokensMemory at 32,768 tokens
IQ1_S124 GiB132 GiB133 GiB
IQ1_M139 GiB147 GiB148 GiB
Q2_K227 GiB240 GiB241 GiB
Q3_K_S269 GiB284 GiB285 GiB

Start it with a local runtime

  • Ollama
    ollama run hf.co/MaziyarPanahi/DeepSeek-V3-0324-GGUF:IQ1_S
  • LM Studio
    lms get MaziyarPanahi/DeepSeek-V3-0324-GGUF
    lms server start
  • llama.cpp server
    llama-server -hf MaziyarPanahi/DeepSeek-V3-0324-GGUF:IQ1_S --jinja
  • vLLM
    vllm serve deepseek-ai/DeepSeek-V3-0324
  • SGLang
    sglang serve --model-path deepseek-ai/DeepSeek-V3-0324 --port 30000

Use it from your harness

Set up for Crusoe with the model deepseek-ai/DeepSeek-V3-0324. Each endpoint page has the same setup for its own address.

OpenCode

Put this in opencode.json in your project folder:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "crusoe": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Crusoe",
      "options": {
        "baseURL": "https://api.inference.crusoecloud.com/v1",
        "apiKey": "{env:CRUSOE_API_KEY}"
      },
      "models": {
        "deepseek-ai/DeepSeek-V3-0324": {
          "name": "DeepSeek V3 0324",
          "limit": {
            "context": 163840,
            "output": 147456
          }
        }
      }
    }
  }
}
  • OpenCode reads any OpenAI-compatible address through the @ai-sdk/openai-compatible package, and an address that speaks the Responses API through @ai-sdk/openai.

From OpenCode documentation, read .

Pi

Put this in ~/.pi/agent/models.json:

{
  "providers": {
    "crusoe": {
      "baseUrl": "https://api.inference.crusoecloud.com/v1",
      "api": "openai-completions",
      "apiKey": "$CRUSOE_API_KEY",
      "models": [
        {
          "id": "deepseek-ai/DeepSeek-V3-0324"
        }
      ]
    }
  }
}
  • The apiKey field can name an environment variable as $NAME.

From Pi documentation, read .

Codex

Codex speaks only the Responses API, and Crusoe documents no Responses address. A gateway that offers one can sit in between.

  • Codex speaks the Responses API only: responses is the one supported wire API of a custom provider. Ollama and LM Studio are built in and start with --oss.

From Codex documentation, read .

Claude Code

Claude Code sends Anthropic Messages requests, and Crusoe documents no such address. A gateway that translates to that API can sit in between.

  • Claude Code sends Anthropic Messages requests to ANTHROPIC_BASE_URL. Anthropic says it does not support routing Claude Code to models other than Claude through any gateway, so some features may not work with another model.

From Claude Code documentation, read .

Other ways to reach it

Published results

These are results other people published. Baltor did not run them and does not rank models by them.