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Model infoModelsSnapshot: 15 Oct 2026

Ricoh / Ricoh LLM

Llama-3-Ricoh-70B-Instruct

Japanese, English and Chinese private LLM with an adapted tokenizer and instruction tuning.

Language models (LLMs)
historicalClosed weightsText generation
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
8 of 20 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedRicoh private cloud / on-premises
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 input tokens
Not collected
Maximum output
Not collected
Input modalities
TextSource for Input modalities (opens in a new tab)
Output modalities
TextSource for Output modalities (opens in a new tab)
Reasoning
Not collected
Tool calling
Not collected
Structured output
Not collected
Streaming
Not collected
Batch processing
Not collected
Fine-tuning
SupportedSource for Fine-tuning (opens in a new tab)

Provider describes customer-specific training or adaptation for private deployment, not a public self-service fine-tuning API.

Knowledge cutoff
Not collected
Release date
2024-08-21Source for Release date (opens in a new tab)

Public announcement date, not a date inferred from the model ID.

Parameters
70BSource for Parameters (opens in a new tab)
Architecture
Not collected
Languages
Japanese, English, ChineseSource for Languages (opens in a new tab)
Context caching
Not collected

No model-specific prompt-cache contract, lifetime or cached-input price verified. Quantization and image-token compression are not prompt caching.

Published price
Not collected

No numeric model-specific production rate verified. Private deployment does not establish free inference.

Base model
Llama-3-Swallow-70BSource for Base model (opens in a new tab)
Published training data
Additional Japanese/English/Chinese pretraining and approximately 16,000 instruction examplesSource 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.

Ricoh private cloud / on-premises

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

Published model name is not a verified API request selector.

Access
Historical generation announced for Japanese customers from autumn 2024, with cloud and on-premises options. Current exact-checkpoint availability requires confirmation.
Regions
Customer-selected private environment; region and residency depend on the deployment agreement.

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

Data & enterprise terms

Retention, training use, regional processing and enterprise terms have not been summarized for this endpoint.

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.

Ricoh private cloud / on-premises

Not verified
How it works
Not collected
Minimum reusable input
Not collected
Lifetime & refresh
Not collected
How to enable it
Not collected
Usage fields
Not collected
Billing rules
Not collected
Limits & data boundaries
Not collected

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

Caching reviewed 14 Oct 2026

What the measurements say

ELYZA-tasks-100

Ricoh · vendor-reported
4.02score / 5

August 9, 2024 provider evaluation, gpt-4-0613 judge. Reported response times use two DGX H100 GPUs and are not a general serving guarantee.

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

Provider identifies this separately trained model for research or private deployment. No matching public derivative was verified in the two provider-linked weight inventories. Closed-weight classification is inferred for this exact revision, not its public upstream base; private distribution terms remain to be confirmed.

Classification evidence (opens in a new tab)

Lifecycle dates

Model announced21 Aug 2024Source (opens in a new tab)

Sources reviewed