Muse Spark 1.3 Contributor
muse-spark-1.3-contributorCompare muse-spark-1.3-contributor API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Modalities
- →
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
BPE (vendor-specific)Performance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Muse Spark 1.3 Contributor
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Muse Spark 1.3 Contributor
Muse Spark 1.3 是 Meta 面向智能体与编程任务的最新版本，相比 1.2 改进了多步工具调用、浏览器与长任务表现以及编程能力，支持图片、视频与 PDF 输入和 100 万 token 上下文。Contributor 档价格大幅降低，但 Meta 可以使用请求与回答内容训练模型。
支持 1,048,576 token 上下文、工具调用、JSON Schema 结构化输出和自动提示缓存。模型始终先思考再作答，思考 token 计入输出，但不返回思考过程。
Contributor 数据政策：Meta 可将请求和回答用于模型训练。请勿提交敏感或保密内容。
Use cases and prompting
通过 OpenAI 兼容的 POST /v1/chat/completions 调用，model 填 muse-spark-1.3-contributor。需要保留多轮推理或内置联网搜索时，使用 POST /v1/responses 或 POST /v1/messages；Chat Completions 不提供内置联网搜索。
思考强度可选 minimal / low / medium / high / xhigh，none 不受支持。Chat 使用 reasoning_effort，Responses 使用 reasoning.effort。图片、视频、PDF 等通过公网 URL 或内联 base64 传入；不支持 /v1/files 上传。
该版本的音频理解支持尚不完整；需要音频理解时可选 Muse Spark 1.2，纯转写可选 Muse Voice Transcribe 1.0。
Contributor 数据政策：Meta 可将请求和回答用于模型训练。请勿提交敏感或保密内容。
美元基础价（每百万 token）：输入 $0.1，输出 $0.2，缓存读取 $0.002。内置联网搜索为 $2.50/千次查询，另加搜索内容对应的 token 费用。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Meta | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about muse-spark-1.3-contributor
What is muse-spark-1.3-contributor?
Compare muse-spark-1.3-contributor API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call muse-spark-1.3-contributor?
Create an API key with access to muse-spark-1.3-contributor, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is muse-spark-1.3-contributor priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of muse-spark-1.3-contributor?
The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.
How should I evaluate muse-spark-1.3-contributor for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.