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Model infoModelsSnapshot: 15 Oct 2026

Fujitsu / Takane

Takane 1-bit (2025 research)

Research variant demonstrating low-bit compression of Takane. The descriptive suffix identifies the experiment, not a public API product.

previewClosed weightsText generation
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
6 of 20 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedFujitsu research / planned trial
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
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
2025-09-08Source for Release date (opens in a new tab)

Research announcement. Trial environments were planned for the second half of fiscal 2025, with no exact completed rollout verified.

Parameters
Not collected
Architecture
Not collected
Languages
JapaneseSource for Languages (opens in a new tab)

Documented focus, not an exhaustive list of supported languages.

Context caching
Not collected
Development partners
Fujitsu and CohereSource for Development partners (opens in a new tab)
Published price
Not collected

No numeric per-token price verified for this exact model. Enterprise service quotes and limited platform trials do not establish free production inference.

Quantization and compression
1-bit weight quantizationSource for Quantization and compression (opens in a new tab)

This compresses model weights; it is not prompt caching or a published cache discount.

Access & pricing

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

Fujitsu research / planned trial

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

The published display name is not a verified API request selector. Tenant endpoint URLs and keys are issued by the provider.

Access
Research reference with planned customer trial environments; no public endpoint or production token tariff verified.
Regions
Service, contract and deployment determine location and access.

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.

Fujitsu research / planned trial

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.

What the measurements say

Accuracy retention after quantization

Fujitsu · vendor-reported
89% of unquantized accuracy

September 2025 Takane 1-bit research announcement. Relative to the vendor’s unquantized baseline, with exact checkpoint, serving settings and hardware not fully pinned here. Accuracy retention is a relative measure, not 89% task accuracy. Not a production guarantee.

Evaluation source (opens in a new tab)

Memory-consumption reduction

Fujitsu · vendor-reported
94% reduction

September 2025 Takane 1-bit research announcement. Relative to the vendor’s unquantized baseline, with exact checkpoint, serving settings and hardware not fully pinned here. Accuracy retention is a relative measure, not 89% task accuracy. Not a production guarantee.

Evaluation source (opens in a new tab)

Inference speed increase

Fujitsu · vendor-reported
3x baseline

September 2025 Takane 1-bit research announcement. Relative to the vendor’s unquantized baseline, with exact checkpoint, serving settings and hardware not fully pinned here. Accuracy retention is a relative measure, not 89% task accuracy. Not a production 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

Takane is a Fujitsu-specific enterprise model developed with Cohere, offered through private services and deployments. No matching public Takane checkpoint release was verified. Classification is inferred for the named Takane product, not for public Command-family weights or every Fujitsu research model.

Classification evidence (opens in a new tab)

Sources reviewed