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Model infoModelsSnapshot: 15 Oct 2026

Zoom / Zoom SLM

Zoom-SLM-2.3B · Translation research

Specialized research adaptation of Zoom’s 2.3B SLM for machine translation. The descriptive variant label is not a public API model name.

restrictedClosed weightsText translation
Official documentation (opens in a new tab)Reviewed 13 Oct 2026
5 of 17 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedZoom research · no public standalone inference
Output billingNot collectedNo inferred price

Specifications

Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.

Context / input limit
Not collected
Maximum output
Not collected
Reasoning
Not collected
Tool calling
Not collected
Structured output
Not collected
Streaming
Not collected
Batch processing
Not collected
Fine-tuning
Not collected
Knowledge cutoff
Not collected
Release date
Not collected
Parameters
2.3 billionSource for Parameters (opens in a new tab)

Exact table label; the article prose rounds the category to 2 billion. Not a token limit.

Architecture
Not collected
Languages
Evaluated across 14 language pairs, including Chinese, English, French, Japanese, Portuguese and SpanishSource for Languages (opens in a new tab)

Examples and evaluation scope, not an exhaustive public deployment language list.

Context caching
Not collected
Published training data
11.5 billion machine-translation adaptation tokens, including synthetic dataSource for Published training data (opens in a new tab)

Access & pricing

Prices belong to an endpoint and billing tier. A provider listing does not guarantee access for every account or region.

Zoom research · no public standalone inference

Provider source (opens in a new tab)
API model ID
Not collected

No public inference selector documented for this research variant.

Access
Research-only inventory record. The publication describes future AI Studio customization and internal evaluations; no public endpoint or numeric inference rate is documented.
Regions
Not collected

Endpoint pricing has not been collected. Check the provider for applicable rates.

Data & enterprise terms

Current service terms are linked for provider context; no original research/evaluation retention agreement is inferred.

Read the service policy (opens in a new tab)

Caching

Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.

01 / INITIAL INPUTProcess & retain

The system processes eligible input and retains reusable state.

02 / LATER REQUESTCheck available state

Reuse depends on matching input, session state and the cache policy.

03 / CACHE HITReuse & generate

Reusing state can reduce repeated work. Any charges follow the billing rules below.

Zoom research · no public standalone inference

Not verified

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

How it works
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

Minimum reusable input
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

Lifetime & refresh
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

How to enable it
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

Usage fields
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

Billing rules
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

Limits & data boundaries
Not collected

No model-specific discounted prompt-cache contract was verified. Audio buffering, S3 job storage and orchestration memory are not treated as prompt caching.

No verified cache rate for this endpoint. An empty rate is not a zero charge.

What the measurements say

Machine translation · COMET-22

Zoom · provider-reported
89.1COMET-22 score

Zoom internal evaluation after translation-specific adaptation across 14 language pairs. Full per-pair results and dataset composition are not supplied in the article.

Evaluation source (opens in a new tab)

Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.

The record behind the model

Why it is included

Zoom describes training and customizing its own SLM internally; the official tables name Zoom-SLM-2.3B. Later research distinguishes its proprietary native SLMs from external open- and closed-weight models. No public weight release or independently callable research checkpoint is established.

Classification evidence (opens in a new tab)

Lifecycle dates

Research publicly described19 Feb 2025Source (opens in a new tab)

Sources reviewed