qwen/qwen3.8-flash

1M context · $0.0900 / M input tokens · $0.2820 / M output tokens

Qwen3.8 Flash is a cost-efficient Qwen3.8 route on OurToken for developers evaluating near-flagship chat, coding, multimodal understanding, long-context work, and production assistant workloads at a lower price.

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24H Status Monitor

Historical uptime data is collected over time. Current status reflects the latest health check.

Pricing

Pay-per-use

No upfront costs, pay only for what you use

60% of official price

Input$0.15 / M$0.0900 / M Tokens

Output$0.47 / M$0.2820 / M Tokens

Cached input$0.016 / M$0.0096 / M Tokens

Cache writes$0.20 / M$0.1200 / M Tokens

API Usage

API Access Guide

Base URLhttps://api.ourtoken.ai/v1

API Endpointchat/completions

Full URLhttps://api.ourtoken.ai/v1/chat/completions

Model IDqwen3.8-flash

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Code examples

Use the OurToken API endpoint for this model. The examples below use direct HTTP requests and the recommended endpoint for the model family.

curl https://api.ourtoken.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen3.8-flash",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ],
    "max_tokens": 256
  }'

Chat Completions API Reference

Create a chat response with the OpenAI Chat Completions-compatible endpoint. Use https://api.ourtoken.ai/v1 as the SDK Base URL and POST /chat/completions as the endpoint.

Authorization

Content-Type	application/json
Authorization	Bearer YOUR_API_KEY

Request Body

Field	Type	Required	Description
model	string	Required	Model ID to call.
messages	array<object>	Required	Conversation messages sent to the model.
max_tokens	integer	Optional	Maximum number of output tokens.
temperature	number	Optional	Sampling temperature.
top_p	number	Optional	Nucleus sampling parameter.
stream	boolean	Optional	Whether to return a streaming response.
stream_options	object	Optional	Additional options for streaming responses.
tools	array<object>	Optional	Tools available to the model.
tool_choice	string \| object	Optional	Controls how the model selects tools.
response_format	object	Optional	Controls structured output, such as JSON object responses.

Response Body

Field	Type	Required	Description
id	string	Required	Unique chat completion identifier.
object	"chat.completion"	Required	Object type returned by the Chat Completions API.
created	integer	Required	Unix timestamp when the response was created.
model	string	Required	Model that produced the response.
choices	array<object>	Required	Candidate responses returned by the model.
choices[].message.role	string	Required	Role of the returned chat message.
choices[].message.content	string	Optional	Text content in the returned chat message.
choices[].finish_reason	string	Optional	Reason generation stopped.
usage	object	Optional	Token usage information for the chat completion.
usage.prompt_tokens	integer	Optional	Input token count.
usage.completion_tokens	integer	Optional	Output token count.
usage.total_tokens	integer	Optional	Total token count.
usage.prompt_tokens_details	object	Optional	Breakdown of input token usage.
usage.prompt_tokens_details.cached_tokens	integer	Optional	Tokens served from cache.

Model Introduction

Qwen qwen3.8-flash

Qwen3.8 Flash delivers near-flagship Qwen3.8 capability with lower inference cost, combining multimodal understanding, coding, and a 1M-token context window according to supplied launch material. Use qwen3.8-flash api through OurToken when you want one endpoint for model testing, pricing review, API keys, usage logs, and production integration.

Why It Looks Great

Cost-efficient Qwen3.8 route for evaluation and production testing.
OpenAI-compatible chat completions setup through the OurToken endpoint.
Dedicated route page for model ID, code examples, and 60% of official price pricing review.
Useful for comparing near-flagship quality against lower token cost.
Clean path from Qwen discovery into API implementation.

Key Features

Model ID: qwen3.8-flash
Provider: Qwen
Input price: $0.0900 per 1M tokens on OurToken
Output price: $0.2820 per 1M tokens on OurToken
Cache read price: $0.0096 per 1M tokens on OurToken
Cache write price: $0.1200 per 1M tokens on OurToken
API endpoint: chat completions
Evaluation focus: cost-aware coding, chat, multimodal understanding, and long-context tasks

Specifications

ProviderQwen

Model IDqwen3.8-flash

Model TypeMultimodal Large Model (LLM + VLM)

OurToken Input Price$0.0900 / 1M tokens

OurToken Output Price$0.2820 / 1M tokens

OurToken Cache Read Price$0.0096 / 1M tokens

OurToken Cache Write Price$0.1200 / 1M tokens

Official Input Reference$0.15 / 1M tokens

Official Output Reference$0.47 / 1M tokens

Official Cache Read Reference$0.016 / 1M tokens

Official Cache Write Reference$0.20 / 1M tokens

Context Window1M tokens

API Endpointhttps://api.ourtoken.ai/v1/chat/completions

qwen3.8 flash api Features for Developers

Use qwen3.8 flash api access to review qwen3.8 flash pricing at 60% of official price and test near-flagship benchmark claims.

API Access

Call qwen3.8 flash api through the OurToken unified endpoint with the qwen3.8-flash model ID. This gives developers a direct route for testing Qwen3.8 prompts at lower cost while keeping API keys, request examples, and usage review in one place.

Pricing Review

Review qwen3.8 flash pricing before routing traffic. OurToken lists $0.0900 input, $0.2820 output, $0.0096 cache read, and $0.1200 cache write per 1M tokens, with official references of $0.15, $0.47, $0.016, and $0.20.

Near-Flagship Quality

Evaluate Qwen3.8 Flash on coding, chat, reasoning, and agent-style prompts that come close to flagship Qwen3.8 behavior while targeting a much lower token cost for high-volume workloads.

Built-in Tools

Test built-in tools such as web search, code interpreter, and multimodal search through the Qwen3.8 Flash route, reducing the need to wire separate tool integrations for agent-style prompts.

Long-Context Tasks

Use the 1M-token context window for repository-scale analysis, long documents, and long-horizon agent sessions at a fraction of flagship cost.

Production Evaluation

Use qwen3.8 flash benchmark searches as guidance for what to test, then measure quality, stability, latency, and cost inside your own application workflow before scaling.

How to Use qwen3.8 flash api on OurToken

Create an API key, use qwen3.8-flash, compare 60% of official price pricing, run tests, and monitor usage.

Create Key

Create an OurToken API key from the dashboard and store it in a secure server-side environment variable. This gives your backend a stable way to test qwen3.8 flash api without exposing credentials in browser code.

Copy Model

Use qwen3.8-flash as the model value in your request body. Keeping the exact model ID in configuration helps developers avoid naming mistakes while comparing Qwen routes across local tests, staging traffic, and production deployments.

Call Endpoint

Send chat completions requests to the OurToken unified endpoint with your qwen3.8-flash model ID. Existing OpenAI-compatible client patterns can usually be reused after changing the base URL, API key, and model value.

Review Pricing

Before scaling usage, review qwen3.8 flash pricing: $0.0900 input, $0.2820 output, $0.0096 cache read, and $0.1200 cache write per 1M tokens. Compare those rows with expected prompt size, output length, and request volume.

Test Workflows

Run representative prompts for coding, multilingual chat, reasoning, retrieval, and assistant behavior, then compare output quality, stability, latency, token usage, and cost with production requirements.

Monitor Cost

After testing, review request count, token usage, failures, latency, and spend in OurToken history. This helps decide whether qwen3.8 flash api should become a default route or remain an evaluation option.

qwen3.8 flash api FAQ

Answers about qwen3.8 flash pricing, qwen3.8-flash model selection, benchmark testing, model ID, and provider comparison.

What is qwen3.8 flash api?

qwen3.8 flash api is the OurToken route page for calling the qwen3.8-flash model through a unified API workflow. Developers can copy the model ID, create an API key, run chat completions requests, review current pricing at 60% of official price, and compare real outputs before production rollout.

How should I check qwen3.8 flash pricing?

qwen3.8 flash pricing on OurToken is $0.0900 per 1M input tokens and $0.2820 per 1M output tokens. Cache read is $0.0096 per 1M tokens, and cache write is $0.1200 per 1M tokens. Official references are $0.15 input, $0.47 output, $0.016 cache read, and $0.20 cache write.

Which model ID should I use for Qwen3.8 Flash?

Use qwen3.8-flash as the model value when calling this route through OurToken. Keep it in configuration rather than hard-coding it across many files, because that makes it easier to compare qwen3.8 flash api with Qwen3.8 Max or other provider routes later.

How does Qwen3.8 Flash compare with Qwen3.8 Max?

Qwen3.8 Flash targets near-flagship capability at a lower token cost, while Qwen3.8 Max is the flagship route with the highest evaluation ceiling. The right choice depends on workload quality, latency, budget, and whether your prompts need maximum reasoning depth.

Can Qwen3.8 Flash understand images and videos?

As part of the Qwen3.8 generation, Qwen3.8 Flash is a native vision-language model supporting image and video understanding, from STEM diagrams and documents to hour-scale videos, on top of text reasoning and coding.

How should I interpret a qwen3.8 flash benchmark?

A qwen3.8 flash benchmark is useful for deciding what to test, but it should not replace your own evaluation. Run representative prompts for coding, reasoning, retrieval, and assistant behavior, then compare quality, stability, latency, token usage, and cost with production requirements.