Meta API and Models on GPTProto

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Explore Meta models available through GPTProto.Build coding assistants, research workflows, and text automation with Meta models through GPTProto. Compare task fit and current pricing, then choose a model for your application. Use one API key and a shared balance to test Meta alongside other supported providers and manage usage in one place.

Mô hình

Muse Spark 1.3MỚI

Access Meta’s Muse Spark 1.3 API on GPTProto for coding assistants, text analysis, and multi-step agent workflows. The model combines a documented 1M-token context window with tool calling and adjustable reasoning. Use one API key and one balance across 200+ models to evaluate the right fit for your application.

byMeta$1/1M tokens input$3.4/1M tokens outputĐộ ổn định

Muse Wonder 1.0MỚI

Access Meta’s Muse Wonder through GPTProto for code generation, debugging, complex reasoning, and agents that work through multiple steps. Use one GPTProto API key and shared balance to evaluate it alongside other supported models.

byMeta$3.2/1M tokens input$16/1M tokens outputĐộ ổn định

Mô hình					Độ ổn định	Đầu vào → Đầu ra
Muse Spark 1.3MỚI $		1.05M	$1.00 / $3.40 cho mỗi 1 triệu	— / $0.12 cho mỗi 1 triệu		Đầu vào: Văn bảnĐầu vào: Hình ảnhĐầu vào: VideoĐầu vào: Âm thanhĐầu vào: Tài liệu Đầu ra: Văn bản
Muse Wonder 1.0MỚI $		—	$3.20 / $16.00 cho mỗi 1 triệu	— / $0.16 cho mỗi 1 triệu		Đầu vào: Văn bảnĐầu vào: Hình ảnhĐầu vào: VideoĐầu vào: Âm thanhĐầu vào: Tài liệu Đầu ra: Văn bản

Giá

Kịch bản	Meta giá niêm yết	OpenRouter	GPTProto	Bạn tiết kiệm / tháng
Cá nhân10M token / tháng (4.8M trong cache)	$12.50	$13.19	$10.58	−$1.92≈ $23.09 / năm
Nhóm100M token / tháng (48M trong cache)	$125.00	$131.88	$105.76	−$19.24≈ $230.88 / năm
Doanh nghiệp500M token / tháng (240M trong cache)	$625.00	$659.38	$528.80	−$96.20≈ $1154.40 / năm

Chi phí hằng tháng theo nguồn

OpenRouter

$0.00

About Meta

Meta develops AI models for language, reasoning, and multimodal understanding. Its Muse models support work such as coding assistance, analysis, and applications that use tools. Muse Wonder and Muse Spark have different workload priorities, helping teams balance demanding tasks against the cost of repeated requests. Meta also develops the Llama family; availability on GPTProto depends on the specific model. This Meta models collection focuses on models supported through GPTProto, with model pages for pricing and integration details. The catalog updates as supported availability changes, so check the selected model’s status and request requirements before deployment.

Why Use Meta with GPTProto?

Choose Meta models by workload, then manage testing and usage alongside other supported providers in one GPTProto account.

One Key, Shared Balance

Use your GPTProto key and shared balance to evaluate Meta models alongside other supported providers.

Choose by Workload

Compare Meta options for demanding engineering work and routine text tasks before assigning them to your application.

Check Task Costs

Review live token rates and measure complete Meta workflows, including reasoning, repeated prompts, and retries.

Documented Model Controls

Check each Meta model’s GPTProto documentation for supported reasoning, tool, and output options before integration.

How to Access Meta Models

Start with a GPTProto account and the request example for your selected Meta model.

1
Create Your Account
Sign up for GPTProto and add credit to your balance before making paid requests.
2
Generate Your API Key
Create a GPTProto API key in the dashboard and store it securely for your application.
3
Send Your First Request
Open a model page, copy its current Model ID, and follow the GPTProto request example for your chosen endpoint.

Meta API FAQ

Which Meta models are available on GPTProto?

The catalog shows the Meta models supported through GPTProto. Open a model card to check its current status, price, and request requirements. Availability changes as models are added or removed.

How do Muse Wonder and Muse Spark differ?

Muse Wonder targets demanding engineering and extended agent tasks. Muse Spark is a useful starting point for routine tool loops and cost-sensitive text work. Compare both on representative tasks, using accepted results and total spend.

Does a Meta model run an entire coding agent for me?

The model can request tools and reason over their results. Your application provides the tools, executes approved actions, and returns feedback. File access, test execution, and permissions belong in that application.

Can these models generate images or videos?

Image and video inputs can support understanding tasks when the selected route accepts them. That is different from generating image or video files. Check the model card’s output type and route documentation.

Can a model access the web or remember earlier requests automatically?

Do not assume either feature from the model name. Web access requires a supported search integration, and your application must manage conversation context using the selected endpoint’s documented options.

Do I need a separate Meta API key?

For requests sent through GPTProto, use a GPTProto API key. Follow the selected model’s GPTProto endpoint and parameter examples when adapting an existing client.

How is Meta API usage billed?

GPTProto deducts usage from your prepaid balance at the selected model’s current rates. Check input, output, and applicable cache or tool charges. Reasoning tokens and repeated requests can affect the total cost.

How does GPTProto handle API content?

GPTProto may temporarily retain API inputs and outputs for transaction processing and error debugging, and may log technical usage metadata. Content sent upstream is also subject to that provider’s policies. See the Privacy Policy for details.

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