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Meta: SAM 3.1

meta/sam-3.1
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Open-weight segmentation model served on Meta Model API. Name an object in a short text prompt and get a box and pixel-accurate mask for every match; in video it follows each object across frames.
ModalitiesTextImageVideoEmbeddings
In / out price— / — per 1M
Context—
Released—
ProvidersPricingPerformanceAvailabilityAppsActivityFAQExplore

Providers

Where this model runs, and what each route costs. Requests are sent to a healthy provider automatically; if one errors, the gateway retries against another serving the same model.

ProviderInput /MOutput /MCache read /MLatencyThroughput
Meta—————

Capabilities (3)

StreamingVideo InputObject Tracking

Pricing

What this model costs to run, next to the rate it is posted at. Caching and discounts mean the price actually paid is often below the listed one.

Effective input price
—
/M tokens
Effective output price
—
/M tokens
RateListedChargedUnit
Per image$0.0025$0.0025per image
UTC
Price history starts building from the first daily snapshot — there is not enough of it yet to draw a trend.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better). Throughput and TTFT are measured on streaming requests, which are the only ones with a first-token moment to time.

Throughput
—tok/s
P50, streaming requests
Latency
—s
P50, end to end
UTC
No requests to this model in the selected window, so there is nothing to measure yet.

Availability

The share of requests to this model that completed successfully. When an upstream provider errors, the gateway retries against another provider serving the same model, so a single provider incident does not necessarily show up here.

Success rate (30d)
0.00%
over 65 requests
Days with traffic
1
of the last 30

Days are UTC days. Days with no requests are omitted rather than drawn at 100% — no traffic is not evidence of availability.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

  1. 1.Oxyy Tester102 tokens

Activity

Token volume and request traffic to this model over time. Daily totals, UTC.

Prompt102
Completion0

Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length. Reasoning is not priced separately for this model, so it is not itemised here.

Frequently asked questions

What is SAM 3.1?
Open-weight segmentation model served on Meta Model API. Name an object in a short text prompt and get a box and pixel-accurate mask for every match; in video it follows each object across frames.
How much does SAM 3.1 cost?
Pricing for SAM 3.1 is listed on this page under Pricing. You pay per request, with no subscription.
What is the context length of SAM 3.1?
The context window for SAM 3.1 is not published.
Does SAM 3.1 support tool calling and structured outputs?
Neither tool calling nor structured outputs are listed among this model's capabilities. Any unsupported parameter you send is ignored rather than rejected.
What inputs and outputs does SAM 3.1 support?
SAM 3.1 accepts text, image, video as input and returns embeddings.
What other models does Meta have?
Meta also offers Muse Glimmer, Muse Image 1.0, Muse Spark 1.1, Muse Spark 1.2 through Oxyy.
When was SAM 3.1 released?
No release date is published for SAM 3.1.

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