Qwen3.8 Flash vs DeepSeek V4 Pro 0813

Qwen3.8 Flash and DeepSeek V4 Pro 0813 side by side: specs, capabilities, parameters, and live pricing from the EmpirioLabs catalog.

Alibaba Cloud DeepSeek Text Generation

Qwen3.8 Flash vs DeepSeek V4 Pro 0813 at a glance

Qwen3.8 Flash from Alibaba Cloud and DeepSeek V4 Pro 0813 from DeepSeek are both in the Text Generation category, available through the EmpirioLabs API.

Key differences

  • Qwen3.8 Flash is the newer release (Aug 26, 2026); DeepSeek V4 Pro 0813 was released on Aug 13, 2026.
  • DeepSeek V4 Pro 0813 has the larger context window: 1M tokens, against 983.6K for Qwen3.8 Flash.
  • DeepSeek V4 Pro 0813 returns up to 393,216 output tokens per response, Qwen3.8 Flash up to 131,072.
  • Input types only Qwen3.8 Flash accepts: Image, Video.
  • Capabilities listed only for Qwen3.8 Flash: vision, multimodal, web search, code interpreter.
  • Qwen3.8 Flash lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens).

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: specs

SpecQwen3.8 FlashDeepSeek V4 Pro 0813
Model IDqwen3-8-flashdeepseek-v4-pro-0813
AuthorAlibaba CloudDeepSeek
ReleasedAug 26, 2026Aug 13, 2026
Context window983.6K tokens1M tokens
Max output131,072 tokens393,216 tokens
Input
Text Image Video
Text
Output
Text
Text
Structured outputJSON SchemaJSON Schema
RegionSingaporeSingapore
Endpoints
POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/qwen3-8-flash:generateContent
POST /v1/chat/completions POST /v1/responses POST /v1/messages POST /v1beta/models/deepseek-v4-pro-0813:generateContent
Capabilities
reasoning vision multimodal web search code interpreter function calling structured output agentic coding
reasoning function calling structured output agentic coding

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: API pricing

Live rates from the EmpirioLabs catalog for both models. Each rate card always matches what the API charges.

Type
Spec
Rate
Input
per 1M prompt tokens
$0.16
Output
per 1M generated tokens
$0.47
Web search
per call when invoked
$0.02
Text-to-image search
per call when invoked
$0.02
Image-to-image search
per call when invoked
$0.02
Web extractor
per call when invoked
$0.00
Code interpreter
per call when invoked
$0.00
Compare on the full pricing page

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: API parameters

Request parameters each model lists on EmpirioLabs, with the allowed values or range where one applies. A dash marks a parameter the model does not list.

ParameterQwen3.8 FlashDeepSeek V4 Pro 0813
temperature0 to 1.99 Default: 0.70 to 2 Default: 0.7
top_p0 to 1 Default: 0.90 to 1 Default: 0.9
max_tokens1 to 1310721 to 393216
stopstringstring
enable_thinkingboolean Default: trueboolean Default: true
reasoning_effortnone, low, medium, high, maxnone, low, medium, high, max Default: medium
thinking_budget1 to 2621441 to 393216 Default: 32768
preserve_thinkingboolean Default: true-
seed0 to 2147483647-
top_k1 to 100-
presence_penalty-2 to 2-
repetition_penalty0.1 to 2-
n1 to 4 Default: 1-
logprobsboolean Default: false-
top_logprobs0 to 5 Default: 0-
vl_high_resolution_imagesboolean Default: true-

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: limits and notes

Qwen3.8 Flash

Text, image, and video input are supported. Web search, web extractor, code interpreter, text-to-image search, and image-to-image search are optional built-in tools exposed through tool_* parameters. Web search, text-to-image search, and image-to-image search add $0.02 for each invoked call; web extractor and code interpreter run at no extra cost. Web extractor requires web search, and both web extractor and code interpreter require thinking. A single request can invoke a tool more than once, and each invoked call is billed. Thinking tokens are billed as output tokens.

DeepSeek V4 Pro 0813

Thinking is enabled by default and can be disabled. Reasoning tokens are billed as output tokens. Optional Linkup web search is billed only when invoked.

How to call Qwen3.8 Flash and DeepSeek V4 Pro 0813

Qwen3.8 Flash serves the OpenAI-compatible Chat Completions API. Point any OpenAI SDK at https://api.empiriolabs.ai/v1 with your EmpirioLabs API key and use the model id qwen3-8-flash. Both models are called through the same endpoint. Set model to qwen3-8-flash or deepseek-v4-pro-0813. Get an API key from the EmpirioLabs dashboard.

cURL
curl https://api.empiriolabs.ai/v1/chat/completions \
  -H "Authorization: Bearer $EMPIRIOLABS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-8-flash",
    "messages": [
      {"role": "user", "content": "Write a haiku about the ocean."}
    ]
  }'

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: tests and guides

Prompt tests and comparison runs that cover both models.

Qwen3.8 Flash vs DeepSeek V4 Pro 0813: common questions

What is the difference between Qwen3.8 Flash and DeepSeek V4 Pro 0813?

Qwen3.8 Flash is the newer release (Aug 26, 2026); DeepSeek V4 Pro 0813 was released on Aug 13, 2026. DeepSeek V4 Pro 0813 has the larger context window: 1M tokens, against 983.6K for Qwen3.8 Flash. DeepSeek V4 Pro 0813 returns up to 393,216 output tokens per response, Qwen3.8 Flash up to 131,072. The specs, rate cards, and parameters on this page come from the live EmpirioLabs catalog.

Which is newer, Qwen3.8 Flash or DeepSeek V4 Pro 0813?

Qwen3.8 Flash is the newer release (Aug 26, 2026); DeepSeek V4 Pro 0813 was released on Aug 13, 2026.

Which has the lower rate, Qwen3.8 Flash or DeepSeek V4 Pro 0813?

Qwen3.8 Flash lists the lower rate for Input (per 1M prompt tokens) and Output (per 1M generated tokens). Both live rate cards are on this page and always match what the API charges.

Can I switch between Qwen3.8 Flash and DeepSeek V4 Pro 0813 without rebuilding my integration?

Yes. Both models use POST /v1/chat/completions with the same EmpirioLabs API key, so most integrations switch by changing the model value. Check the parameters table on this page first if you rely on a model-specific parameter.

Can I try Qwen3.8 Flash and DeepSeek V4 Pro 0813 in the browser before integrating?

Yes. Open each model in the EmpirioLabs playground and run the same prompt on both, with the parameters the API exposes.

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