MiscSubjects

Collection

DeepSeek

76 model catalog entries

DeepSeek: DeepSeek V4.1 Flash 1017
DeepSeekDeepSeek V4.1 Flash 1017
deepseek/deepseek-v4.1-flash-1017
2026-10-17text+image->textreasoning: max / high / low / none
Intelligence index
28.6
Artificial Analysis
Design Arena
1290 Elo · #6
spreadsheet · 51.2% wins
Input
$0.15
per 1M tokens
Output
$0.60
per 1M tokens
Cached input
$0
per 1M tokens
Context
1.0M
tokens it reads
Max output
393K
tokens it writes
DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model...
inspect — the catalog entry
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  "name": "DeepSeek: DeepSeek V4.1 Flash 1017",
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  "description": "DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the `deepseek-flash` API model name. V4.1 Flash is a sparse mixture-of-experts model...",
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DeepSeek: DeepSeek V4.1 Flash
DeepSeekDeepSeek V4.1 Flash
deepseek/deepseek-v4.1-flash
2026-09-10text+image->textreasoning: max / high / low / none
Input
$0.04
per 1M tokens
Output
$0.50
per 1M tokens
Cached input
$0.02
per 1M tokens
Context
1.0M
tokens it reads
Max output
944K
tokens it writes
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
inspect — the catalog entry
{
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DeepSeek: DeepSeek V4.1 Flash (batch)
DeepSeekDeepSeek V4.1 Flash (batch)
deepseek/deepseek-v4.1-flash:batch
2026-09-10text+image->textreasoning: max / high / low / none
Input
$0.05
per 1M tokens
Output
$0.15
per 1M tokens
Cached input
$0
per 1M tokens
Context
1.0M
tokens it reads
Max output
384K
tokens it writes
DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...
inspect — the catalog entry
{
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  "name": "DeepSeek: DeepSeek V4.1 Flash (batch)",
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}
DeepSeek: DeepSeek V4 Flash Vision Exp
DeepSeekDeepSeek V4 Flash Vision Exp
deepseek/deepseek-v4-flash-vision-exp
2026-08-21text+image->textreasoning: max / high / low / none
Coding index
65
Artificial Analysis
Agentic index
47.5
Artificial Analysis
Input
$0.22
per 1M tokens
Output
$0.65
per 1M tokens
Cached input
$0.01
per 1M tokens
Context
1.0M
tokens it reads
Max output
262K
tokens it writes
DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of [DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...
inspect — the catalog entry
{
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  "hugging_face_id": "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
  "name": "DeepSeek: DeepSeek V4 Flash Vision Exp",
  "created": 1787311563,
  "description": "DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of [DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...",
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DeepSeek: DeepSeek V4 Pro 0813
DeepSeekDeepSeek V4 Pro 0813
deepseek/deepseek-v4-pro-0813
2026-08-12text->textreasoning: max / high / low / none
Intelligence index
23.1
Artificial Analysis
Coding index
68.8
Artificial Analysis
Agentic index
41.3
Artificial Analysis
Input
$0.11
per 1M tokens
Output
$4.60
per 1M tokens
Cached input
$0.11
per 1M tokens
Context
1.0M
tokens it reads
Max output
944K
tokens it writes
DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.
inspect — the catalog entry
{
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DeepSeek: DeepSeek V4 Flash 0731
DeepSeekDeepSeek V4 Flash 0731
deepseek/deepseek-v4-flash-0731
2026-07-31text->textreasoning: max / high / low / none
Coding index
69.1
Artificial Analysis
Agentic index
41
Artificial Analysis
Design Arena
1180 Elo · #37
svg · 45.1% wins
Input
$0.04
per 1M tokens
Output
$0.30
per 1M tokens
Cached input
$0.04
per 1M tokens
Context
1.0M
tokens it reads
Max output
944K
tokens it writes
DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....
inspect — the catalog entry
{
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}
DeepSeek: DeepSeek V4 Pro 0423
DeepSeekDeepSeek V4 Pro 0423
deepseek/deepseek-v4-pro
2026-04-24text->textreasoning: xhigh / high / none
Coding index
59.4
Artificial Analysis
Agentic index
26.3
Artificial Analysis
Design Arena
1059 Elo · #32
godotgamedev · 34% wins
Input
$1.60
per 1M tokens
Output
$3.20
per 1M tokens
Cached input
$0.14
per 1M tokens
Context
1.0M
tokens it reads
Max output
393K
tokens it writes
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
inspect — the catalog entry
{
  "id": "deepseek/deepseek-v4-pro",
  "canonical_slug": "deepseek/deepseek-v4-pro-20260423",
  "hugging_face_id": "deepseek-ai/DeepSeek-V4-Pro",
  "name": "DeepSeek: DeepSeek V4 Pro 0423",
  "created": 1777000679,
  "description": "DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...",
  "context_length": 1048576,
  "architecture": {
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      "text"
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    "output_modalities": [
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    "tokenizer": "DeepSeek",
    "instruct_type": null
  },
  "pricing": {
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    "completion": "0.0000032",
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  "per_request_limits": null,
  "supported_parameters": [
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    "include_reasoning",
    "logit_bias",
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    "reasoning",
    "reasoning_effort",
    "repetition_penalty",
    "response_format",
    "seed",
    "stop",
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    "tools",
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    "top_logprobs",
    "top_p"
  ],
  "default_parameters": {
    "temperature": 1,
    "top_p": 1
  },
  "supported_voices": null,
  "knowledge_cutoff": null,
  "expiration_date": null,
  "links": {
    "details": "/api/v1/models/deepseek/deepseek-v4-pro-20260423/endpoints"
  },
  "endpoints": [
    "/api/v1/chat/completions",
    "/api/v1/completions",
    "/api/v1/messages",
    "/api/v1/responses"
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  "benchmarks": {
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        "category": "fullstack",
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      {
        "arena": "agents",
        "category": "webapps",
        "elo": 1000,
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        "category": "3d",
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        "rank": 33
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        "arena": "models",
        "category": "asciiart",
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        "win_rate": 46.4,
        "rank": 47
      },
      {
        "arena": "models",
        "category": "codecategories",
        "elo": 1238,
        "win_rate": 51.9,
        "rank": 47
      },
      {
        "arena": "models",
        "category": "dataviz",
        "elo": 1203,
        "win_rate": 48.7,
        "rank": 59
      },
      {
        "arena": "models",
        "category": "gamedev",
        "elo": 1229,
        "win_rate": 52.3,
        "rank": 44
      },
      {
        "arena": "models",
        "category": "svg",
        "elo": 1136,
        "win_rate": 45.4,
        "rank": 55
      },
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        "arena": "models",
        "category": "uicomponent",
        "elo": 1216,
        "win_rate": 50.6,
        "rank": 54
      },
      {
        "arena": "models",
        "category": "website",
        "elo": 1233,
        "win_rate": 50.5,
        "rank": 53
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    "artificial_analysis": {
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      "coding_index": 59.4,
      "agentic_index": 26.3
    }
  },
  "reasoning": {
    "mandatory": false,
    "supported_efforts": [
      "xhigh",
      "high",
      "none"
    ],
    "default_effort": "high"
  }
}
DeepSeek: DeepSeek V4 Flash 0423
DeepSeekDeepSeek V4 Flash 0423
deepseek/deepseek-v4-flash
2026-04-24text->textreasoning: xhigh / high / none
Coding index
56.2
Artificial Analysis
Agentic index
22.2
Artificial Analysis
Design Arena
1152 Elo · #47
svg · 48.4% wins
Input
$0.09
per 1M tokens
Output
$0.18
per 1M tokens
Cached input
$0.02
per 1M tokens
Context
1.0M
tokens it reads
Max output
944K
tokens it writes
DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...
inspect — the catalog entry
{
  "id": "deepseek/deepseek-v4-flash",
  "canonical_slug": "deepseek/deepseek-v4-flash-20260423",
  "hugging_face_id": "deepseek-ai/DeepSeek-V4-Flash",
  "name": "DeepSeek: DeepSeek V4 Flash 0423",
  "created": 1777000666,
  "description": "DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...",
  "context_length": 1048576,
  "architecture": {
    "modality": "text->text",
    "input_modalities": [
      "text"
    ],
    "output_modalities": [
      "text"
    ],
    "tokenizer": "DeepSeek",
    "instruct_type": null
  },
  "pricing": {
    "prompt": "0.000000091",
    "completion": "0.000000182",
    "input_cache_read": "0.0000000182"
  },
  "top_provider": {
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    "max_completion_tokens": 943717,
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  },
  "per_request_limits": null,
  "supported_parameters": [
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    "logprobs",
    "max_completion_tokens",
    "max_tokens",
    "min_p",
    "presence_penalty",
    "reasoning",
    "reasoning_effort",
    "repetition_penalty",
    "response_format",
    "seed",
    "stop",
    "structured_outputs",
    "temperature",
    "tool_choice",
    "tools",
    "top_a",
    "top_k",
    "top_logprobs",
    "top_p"
  ],
  "default_parameters": {},
  "supported_voices": null,
  "knowledge_cutoff": null,
  "expiration_date": null,
  "links": {
    "details": "/api/v1/models/deepseek/deepseek-v4-flash-20260423/endpoints"
  },
  "endpoints": [
    "/api/v1/chat/completions",
    "/api/v1/completions",
    "/api/v1/messages",
    "/api/v1/responses"
  ],
  "benchmarks": {
    "design_arena": [
      {
        "arena": "models",
        "category": "3d",
        "elo": 1186,
        "win_rate": 49.3,
        "rank": 57
      },
      {
        "arena": "models",
        "category": "asciiart",
        "elo": 1107,
        "win_rate": 42.7,
        "rank": 61
      },
      {
        "arena": "models",
        "category": "codecategories",
        "elo": 1204,
        "win_rate": 48.9,
        "rank": 62
      },
      {
        "arena": "models",
        "category": "dataviz",
        "elo": 1127,
        "win_rate": 40.7,
        "rank": 93
      },
      {
        "arena": "models",
        "category": "gamedev",
        "elo": 1197,
        "win_rate": 50.2,
        "rank": 58
      },
      {
        "arena": "models",
        "category": "svg",
        "elo": 1152,
        "win_rate": 48.4,
        "rank": 47
      },
      {
        "arena": "models",
        "category": "uicomponent",
        "elo": 1159,
        "win_rate": 44.7,
        "rank": 76
      },
      {
        "arena": "models",
        "category": "website",
        "elo": 1206,
        "win_rate": 49.1,
        "rank": 63
      }
    ],
    "artificial_analysis": {
      "intelligence_index": null,
      "coding_index": 56.2,
      "agentic_index": 22.2
    }
  },
  "reasoning": {
    "mandatory": false,
    "supported_efforts": [
      "xhigh",
      "high",
      "none"
    ],
    "default_effort": "high"
  }
}
DeepSeek: DeepSeek V3.2
DeepSeekDeepSeek V3.2
deepseek/deepseek-v3.2
2025-12-01text->text
Coding index
44.2
Artificial Analysis
Design Arena
1080 Elo · #68
asciiart · 40.5% wins
Input
$0.26
per 1M tokens
Output
$0.38
per 1M tokens
Cached input
$0.13
per 1M tokens
Context
164K
tokens it reads
Max output
147K
tokens it writes
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
inspect — the catalog entry
{
  "id": "deepseek/deepseek-v3.2",
  "canonical_slug": "deepseek/deepseek-v3.2-20251201",
  "hugging_face_id": "deepseek-ai/DeepSeek-V3.2",
  "name": "DeepSeek: DeepSeek V3.2",
  "created": 1764594642,
  "description": "DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...",
  "context_length": 163840,
  "architecture": {
    "modality": "text->text",
    "input_modalities": [
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    ],
    "output_modalities": [
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    ],
    "tokenizer": "DeepSeek",
    "instruct_type": null
  },
  "pricing": {
    "prompt": "0.00000026",
    "completion": "0.00000038",
    "input_cache_read": "0.00000013"
  },
  "top_provider": {
    "context_length": 163840,
    "max_completion_tokens": 147456,
    "is_moderated": false
  },
  "per_request_limits": null,
  "supported_parameters": [
    "frequency_penalty",
    "include_reasoning",
    "logit_bias",
    "logprobs",
    "max_tokens",
    "min_p",
    "presence_penalty",
    "reasoning",
    "repetition_penalty",
    "response_format",
    "seed",
    "stop",
    "structured_outputs",
    "temperature",
    "tool_choice",
    "tools",
    "top_k",
    "top_logprobs",
    "top_p"
  ],
  "default_parameters": {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": null,
    "frequency_penalty": null,
    "presence_penalty": null,
    "repetition_penalty": null
  },
  "supported_voices": null,
  "knowledge_cutoff": null,
  "expiration_date": null,
  "links": {
    "details": "/api/v1/models/deepseek/deepseek-v3.2-20251201/endpoints"
  },
  "endpoints": [
    "/api/v1/chat/completions",
    "/api/v1/completions",
    "/api/v1/messages",
    "/api/v1/responses"
  ],
  "benchmarks": {
    "design_arena": [
      {
        "arena": "models",
        "category": "3d",
        "elo": 1130,
        "win_rate": 49.4,
        "rank": 77
      },
      {
        "arena": "models",
        "category": "asciiart",
        "elo": 1080,
        "win_rate": 40.5,
        "rank": 68
      },
      {
        "arena": "models",
        "category": "codecategories",
        "elo": 1162,
        "win_rate": 49.3,
        "rank": 82
      },
      {
        "arena": "models",
        "category": "dataviz",
        "elo": 1158,
        "win_rate": 48.2,
        "rank": 79
      },
      {
        "arena": "models",
        "category": "gamedev",
        "elo": 1130,
        "win_rate": 46.5,
        "rank": 88
      },
      {
        "arena": "models",
        "category": "svg",
        "elo": 1029,
        "win_rate": 40.8,
        "rank": 84
      },
      {
        "arena": "models",
        "category": "uicomponent",
        "elo": 1144,
        "win_rate": 46.8,
        "rank": 81
      },
      {
        "arena": "models",
        "category": "website",
        "elo": 1173,
        "win_rate": 50.2,
        "rank": 81
      }
    ],
    "artificial_analysis": {
      "intelligence_index": null,
      "coding_index": 44.2,
      "agentic_index": null
    }
  },
  "reasoning": {
    "mandatory": false,
    "default_enabled": false
  }
}
DeepSeek: DeepSeek V3.2 Exp
DeepSeekDeepSeek V3.2 Exp
deepseek/deepseek-v3.2-exp
2025-09-29text->textknows up to 2025-07-31
Design Arena
1145 Elo · #71
3d · 56.4% wins
Input
$0.27
per 1M tokens
Output
$0.41
per 1M tokens
Context
164K
tokens it reads
Max output
147K
tokens it writes
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...
inspect — the catalog entry
{
  "id": "deepseek/deepseek-v3.2-exp",
  "canonical_slug": "deepseek/deepseek-v3.2-exp",
  "hugging_face_id": "deepseek-ai/DeepSeek-V3.2-Exp",
  "name": "DeepSeek: DeepSeek V3.2 Exp",
  "created": 1759150481,
  "description": "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...",
  "context_length": 163840,
  "architecture": {
    "modality": "text->text",
    "input_modalities": [
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    ],
    "output_modalities": [
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    "tokenizer": "DeepSeek",
    "instruct_type": "deepseek-v3.1"
  },
  "pricing": {
    "prompt": "0.00000027",
    "completion": "0.00000041"
  },
  "top_provider": {
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    "max_completion_tokens": 147456,
    "is_moderated": false
  },
  "per_request_limits": null,
  "supported_parameters": [
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    "include_reasoning",
    "max_tokens",
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    "response_format",
    "structured_outputs",
    "temperature",
    "tool_choice",
    "tools",
    "top_k",
    "top_p"
  ],
  "default_parameters": {
    "temperature": 0.6,
    "top_p": 0.95,
    "frequency_penalty": null
  },
  "supported_voices": null,
  "knowledge_cutoff": "2025-07-31",
  "expiration_date": null,
  "links": {
    "details": "/api/v1/models/deepseek/deepseek-v3.2-exp/endpoints"
  },
  "endpoints": [
    "/api/v1/chat/completions",
    "/api/v1/completions",
    "/api/v1/messages",
    "/api/v1/responses"
  ],
  "benchmarks": {
    "design_arena": [
      {
        "arena": "models",
        "category": "3d",
        "elo": 1145,
        "win_rate": 56.4,
        "rank": 71
      },
      {
        "arena": "models",
        "category": "codecategories",
        "elo": 1170,
        "win_rate": 54.2,
        "rank": 74
      },
      {
        "arena": "models",
        "category": "dataviz",
        "elo": 1152,
        "win_rate": 50.6,
        "rank": 82
      },
      {
        "arena": "models",
        "category": "gamedev",
        "elo": 1144,
        "win_rate": 53,
        "rank": 77
      },
      {
        "arena": "models",
        "category": "svg",
        "elo": 1028,
        "win_rate": 42,
        "rank": 85
      },
      {
        "arena": "models",
        "category": "uicomponent",
        "elo": 1161,
        "win_rate": 53.3,
        "rank": 73
      },
      {
        "arena": "models",
        "category": "website",
        "elo": 1177,
        "win_rate": 54.3,
        "rank": 77
      }
    ]
  },
  "reasoning": {
    "mandatory": false
  }
}
DeepSeek: DeepSeek V3.1 Terminus
DeepSeekDeepSeek V3.1 Terminus
deepseek/deepseek-v3.1-terminus
2025-09-22text->textknows up to 2025-03-31
Coding index
43.5
Artificial Analysis
Agentic index
6.8
Artificial Analysis
Design Arena
1177 Elo · #67
uicomponent · 59.5% wins
Input
$0.27
per 1M tokens
Output
$1
per 1M tokens
Context
164K
tokens it reads
Max output
147K
tokens it writes
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...
inspect — the catalog entry
{
  "id": "deepseek/deepseek-v3.1-terminus",
  "canonical_slug": "deepseek/deepseek-v3.1-terminus",
  "hugging_face_id": "deepseek-ai/DeepSeek-V3.1-Terminus",
  "name": "DeepSeek: DeepSeek V3.1 Terminus",
  "created": 1758548275,
  "description": "DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...",
  "context_length": 163840,
  "architecture": {
    "modality": "text->text",
    "input_modalities": [
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    ],
    "output_modalities": [
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    "tokenizer": "DeepSeek",
    "instruct_type": "deepseek-v3.1"
  },
  "pricing": {
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    "completion": "0.000001"
  },
  "top_provider": {
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    "max_completion_tokens": 147456,
    "is_moderated": false
  },
  "per_request_limits": null,
  "supported_parameters": [
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    "tools",
    "top_k",
    "top_p"
  ],
  "default_parameters": {
    "temperature": null,
    "top_p": null,
    "frequency_penalty": null
  },
  "supported_voices": null,
  "knowledge_cutoff": "2025-03-31",
  "expiration_date": null,
  "links": {
    "details": "/api/v1/models/deepseek/deepseek-v3.1-terminus/endpoints"
  },
  "endpoints": [
    "/api/v1/chat/completions",
    "/api/v1/completions",
    "/api/v1/messages",
    "/api/v1/responses"
  ],
  "benchmarks": {
    "design_arena": [
      {
        "arena": "models",
        "category": "3d",
        "elo": 1138,
        "win_rate": 56,
        "rank": 73
      },
      {
        "arena": "models",
        "category": "codecategories",
        "elo": 1173,
        "win_rate": 56,
        "rank": 73
      },
      {
        "arena": "models",
        "category": "dataviz",
        "elo": 1168,
        "win_rate": 53.4,
        "rank": 73
      },
      {
        "arena": "models",
        "category": "gamedev",
        "elo": 1129,
        "win_rate": 52.5,
        "rank": 89
      },
      {
        "arena": "models",
        "category": "svg",
        "elo": 1061,
        "win_rate": 50.1,
        "rank": 77
      },
      {
        "arena": "models",
        "category": "uicomponent",
        "elo": 1177,
        "win_rate": 59.5,
        "rank": 67
      },
      {
        "arena": "models",
        "category": "website",
        "elo": 1185,
        "win_rate": 56.4,
        "rank": 73
      }
    ],
    "artificial_analysis": {
      "intelligence_index": null,
      "coding_index": 43.5,
      "agentic_index": 6.8
    }
  },
  "reasoning": {
    "mandatory": false
  }
}
DeepSeek: DeepSeek V3.1
DeepSeekDeepSeek V3.1
deepseek/deepseek-chat-v3.1
2025-08-21text->textknows up to 2025-03-31
Design Arena
963 Elo · #95
svg · 38.2% wins
Input
$0.25
per 1M tokens
Output
$0.95
per 1M tokens
Cached input
$0.13
per 1M tokens
Context
164K
tokens it reads
Max output
147K
tokens it writes
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...
inspect — the catalog entry
{
  "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...",
  "context_length": 163840,
  "architecture": {
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    "input_modalities": [
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    ],
    "output_modalities": [
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    ],
    "tokenizer": "DeepSeek",
    "instruct_type": "deepseek-v3.1"
  },
  "pricing": {
    "prompt": "0.00000025",
    "completion": "0.00000095",
    "input_cache_read": "0.00000013"
  },
  "top_provider": {
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    "max_completion_tokens": 147456,
    "is_moderated": false
  },
  "per_request_limits": null,
  "supported_parameters": [
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DeepSeek: R1 0528
DeepSeekR1 0528
deepseek/deepseek-r1-0528
2025-05-28text->textknows up to 2025-03-31
Design Arena
1182 Elo · #65
dataviz · 60.9% wins
Input
$0.50
per 1M tokens
Output
$2.15
per 1M tokens
Cached input
$0.35
per 1M tokens
Context
164K
tokens it reads
Max output
33K
tokens it writes
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...
inspect — the catalog entry
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  "name": "DeepSeek: R1 0528",
  "created": 1748455170,
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}
DeepSeek: DeepSeek V3 0324
DeepSeekDeepSeek V3 0324
deepseek/deepseek-chat-v3-0324
2025-03-24text->textknows up to 2024-07-31
Coding index
21.2
Artificial Analysis
Agentic index
0.8
Artificial Analysis
Input
$0.29
per 1M tokens
Output
$1.14
per 1M tokens
Cached input
$0.11
per 1M tokens
Context
164K
tokens it reads
Max output
115K
tokens it writes
DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well...
inspect — the catalog entry
{
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  "name": "DeepSeek: DeepSeek V3 0324",
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DeepSeek: DeepSeek V3
DeepSeekDeepSeek V3
deepseek/deepseek-chat
2024-12-26text->textknows up to 2024-07-31
Design Arena
1084 Elo · #93
3d · 50.5% wins
Input
$0.26
per 1M tokens
Output
$1.03
per 1M tokens
Context
164K
tokens it reads
Max output
16K
tokens it writes
DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations...
inspect — the catalog entry
{
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  "created": 1735241320,
  "description": "DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations...",
  "context_length": 163840,
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  "knowledge_cutoff": "2024-07-31",
  "expiration_date": null,
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  },
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        "category": "3d",
        "elo": 1084,
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}
deepseek/deepseek-v4.1-flash-20261017
DeepSeekdeepseek/deepseek-v4.1-flash-20261017
deepseek/deepseek-v4.1-flash-20261017
inspect — the catalog entry
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  "cost_out": 6e-7,
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}
deepseek/deepseek-v4.1-flash-1017
DeepSeekdeepseek/deepseek-v4.1-flash-1017
deepseek/deepseek-v4.1-flash-1017
inspect — the catalog entry
{
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  "description": "",
  "context_length": null
}
deepseek/deepseek-v4.1-flash:batch
DeepSeekbatch
deepseek/deepseek-v4.1-flash:batch
inspect — the catalog entry
{
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  "owned_by": "deepseek",
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  "context_length": null
}