DeepSeek-V4-Flash-0731 is the officially updated version of the DeepSeek-V4-Flash model. The model is built on the same Mixture-of-Experts (MoE) architecture with 284 billion parameters, of which only 13 billion are activated per token, delivering high performance at relatively modest inference compute costs. The model supports a context length of up to one million tokens. It employs the signature engineering innovation of the V4 family — a hybrid attention mechanism that radically reduces the cost of long-context processing. Instead of uniformly handling all tokens, the model uses three modes simultaneously: Compressed Sparse Attention (CSA) compresses the context at a 1:4 ratio, after which the Lightning Indexer selects only the most relevant blocks for computation; Heavily Compressed Attention (HCA) applies extreme 1:128 compression and performs full global attention over ultra-compact representations of the entire history; in parallel, a sliding window of 128 tokens processes the immediate context without compression, preserving detailed local awareness.
In version 0731, a built-in DSpark module has been added that implements speculative decoding: the model can generate several tokens “ahead” and verify them in a single step, noticeably accelerating inference without quality loss (it increases memory footprint and is disabled by default; it can be enabled via the speculative parameter differently across inference engines). However, the key innovation is the improvement in post-training, as a result of which the model’s agentic capabilities have increased several-fold: compared to the preview version, scores on Terminal Bench 2.1 rose from 61.8% to 82.7%, on DeepSWE from 7.3% to 54.4%, and on Cybergym from 38.7% to 76.7%. Furthermore, 0731 outperforms much larger models, notably DeepSeek-V4-Pro (Preview) and the popular GLM-5.2, on most agentic benchmarks, and comes close to the strongest proprietary models such as Opus-4.8.
Use cases span a wide range of tasks where both context length and deployment cost-efficiency are critical: analysis and summarization of large documents — legal dossiers, scientific articles, corporate reports up to a million tokens; search across extensive corporate knowledge bases and information extraction from voluminous archives; multi-step agent work with long sessions and numerous tools; programming and mathematical modeling tasks that demand both reasoning accuracy and the ability to keep large codebases or specifications in view; building chatbots, automatic code generation systems, intelligent document assistants, and research agents. The MIT license allows unrestricted commercial use.
| Model Name | Context | Type | GPU | TPS | Tooling | Status | Link |
|---|---|---|---|---|---|---|---|
| deepseek-ai/DeepSeek-V4-Flash-0731 | 1M | Public | 8×H100 tensor |
315.0 | AVAILABLE | chat |
curl https://chat.immers.cloud/v1/endpoints/generate/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer USER_API_KEY" \
--data-binary @- <<"EOF"
{"model": "deepseek-v4-flash-0731", "messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"}
], "temperature": 0, "max_tokens": 150
}
EOF
$response = Invoke-WebRequest https://chat.immers.cloud/v1/endpoints/generate/chat/completions `
-Method POST `
-Headers @{
"Authorization" = "Bearer USER_API_KEY"
"Content-Type" = "application/json; charset=utf-8"
} `
-Body ([System.Text.Encoding]::UTF8.GetBytes((@{
model = "deepseek-v4-flash-0731"
messages = @(
@{ role = "system"; content = "You are a helpful assistant." },
@{ role = "user"; content = "Say this is a test" })
} | ConvertTo-Json -Depth 10)))
([System.Text.Encoding]::UTF8.GetString($response.RawContentStream.ToArray()) | ConvertFrom-Json).choices[0].message.content
#!pip install OpenAI --upgrade
from openai import OpenAI
client = OpenAI(
api_key="USER_API_KEY",
base_url="https://chat.immers.cloud/v1/endpoints/generate/",
)
chat_response = client.chat.completions.create(
model="deepseek-v4-flash-0731",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Say this is a test"},
]
)
print(chat_response.choices[0].message.content)
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