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Model infoModelsSnapshot: 15 Oct 2026

ABEJA / ABEJA Nemotron SFT research

ABEJA Nemotron 3 Nano SFT Stage 2

Financial-domain research checkpoint trained from ABEJA’s selected Stage 1 model. Improvements vary across financial tasks and generation settings.

previewClosed weightsText generation
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
13 of 25 specification fields collected
Context windowNot collectedAwaiting source review
Maximum outputNot collectedAwaiting source review
Input billingNot collectedABEJA / CODAS research
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
SupportedSource for Reasoning (opens in a new tab)

Research evaluation explicitly separates reasoning from final answers; no commercial reasoning-control API verified.

Tool calling
Not collected
Structured output
Not collected

JSON-oriented evaluation and parsing measurements are not a strict schema-constrained output contract.

Streaming
Not collected
Batch processing
Not collected
Fine-tuning
Not collected

Provider trained this research derivative; customer access to fine-tuning is not established.

Knowledge cutoff
Not collected
Release date
Not collected
Parameters
30B total / 3B active classSource for Parameters (opens in a new tab)

Published Nano 30B-A3B base class; not an independently counted derivative tensor total.

Architecture
Mamba / Transformer hybrid mixture of expertsSource for Architecture (opens in a new tab)
Context caching
Not collected

No model-specific prompt-caching contract or cached-input price verified. Training caches and recurrent-state dtype settings are not a published prompt-cache API.

Published price
Not collected

No numeric production price verified for this exact model or research checkpoint.

Base model
ABEJA SFT Stage 1, condition 1.2 final checkpointSource for Base model (opens in a new tab)
trainingSequenceLength
262,144Source for trainingSequenceLength (opens in a new tab)

Maximum sequence used for SFT, not a serving context guarantee. The upstream 1M claim is not inherited.

Published training method
Supervised fine-tuning with NVIDIA NeMo RLSource for Published training method (opens in a new tab)

The training library name does not mean this particular experiment used reinforcement learning.

trainingBlend
Approximately 66.7% Japanese financial data and 33.3% Stage 1 general dataSource for trainingBlend (opens in a new tab)

Sample-level blend, not token-level language composition.

evaluationSettings
Context setting 32,768; output cap 16,384 for Swallow and 4,096 for financial evaluation; mamba_ssm_cache_dtype float32Source for evaluationSettings (opens in a new tab)

Evaluation configuration only. Output-length experiments at 8192 and 16384 also change sampling to temperature 0.6/top-p 0.95, so those results must remain separate.

Access & pricing

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

API model ID
Not collected

Provider display label is not a verified API request selector.

Access
Selected checkpoint documented in a research report. No public weight release, commercial API or production rollout verified; the descriptive Atlas name is not an API model ID.
Regions
No production processing region or deployment agreement verified for this exact checkpoint.
researchCheckpoint
SFT Stage 2, final step 3,222Source for researchCheckpoint (opens in a new tab)

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.

ABEJA / CODAS research

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

Swallow Leaderboard v2 overall

ABEJA · vendor-reported
58.3score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Swallow Leaderboard v2 Japanese mean

ABEJA · vendor-reported
50.37score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Swallow Leaderboard v2 English mean

ABEJA · vendor-reported
66.23score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Japanese MT-Bench (rescaled)

ABEJA · vendor-reported
72.53score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

English MT-Bench (rescaled)

ABEJA · vendor-reported
77.49score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness

ABEJA · vendor-reported
68.34score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

EDINET-Bench official aggregation

ABEJA · vendor-reported
24.08score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness / Security Sales

ABEJA · vendor-reported
64.91score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness / CMA

ABEJA · vendor-reported
89.47score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness / CPA

ABEJA · vendor-reported
38.19score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness / FP2

ABEJA · vendor-reported
57.89score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

Fin-Harness / ChABSA

ABEJA · vendor-reported
91.21score / 100

September 30, 2026 provider experiment. Swallow evaluation uses a 32,768-token context setting and 16,384-token output cap; financial evaluation uses a 4,096-token output cap. Scores are on a 0–100 scale. EDINET official aggregation excludes unparsable outputs; it is not comparable to all-sample accuracy without the stated denominator.

Evaluation source (opens in a new tab)

EDINET-Bench all-sample aggregation

ABEJA · vendor-reported
13.79score / 100

Provider reanalysis counts unparsable answers as wrong. Three task means over 451 Earnings Forecast, 224 Fraud Detection and 496 Industry Prediction examples; 1,171 total. Context setting 32,768, output cap 4,096. This is not the official parseable-only score.

Evaluation source (opens in a new tab)

EDINET-Bench unparsable responses

ABEJA · vendor-reported
65.16% of 1,171 responses

Lower is better; 763 / 1,171 responses. Provider evaluation at the 4,096-token output cap; output-limit and sampling changes materially change results.

Evaluation source (opens in a new tab)

Responses without a final answer

ABEJA · vendor-reported
4.82% of 18,571 responses

Lower is better; 896 / 18,571 responses across eight Japanese Swallow tasks; MT-Bench excluded. Reasoning-only responses count as missing a final answer.

Evaluation source (opens in a new tab)

Unexpected third-language text in final answers

ABEJA · vendor-reported
0.1% of nonempty final answers

Lower is better; 17 / 17,675 nonempty final answers. Provider-specific character/phrase review, not a general toxicity or language 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 model or selected research checkpoint, but no matching public weights were found in the reviewed official inventory. Closed-weight classification is inferred for this exact artifact. Public base weights, training recipes and other ABEJA releases do not establish public availability of this derivative; private distribution terms remain unverified.

Classification evidence (opens in a new tab)

Lifecycle dates

Selected SFT research checkpoint reported30 Sept 2026Source (opens in a new tab)

Sources reviewed