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Codefuse Ai model product

F2LLM-v2-330M

Ranked model from MTEB.

Updated Aug 18, 2026. Default version: F2LLM-v2-330M

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LLMBoard Embedding Score68.7F2LLM-v2-330M
Coverage85%5 benchmark families
Context window41KTokens
Official input priceN/AOfficial price unavailable

On this page

  • Capability
  • Benchmarks
  • Arena
  • Pricing
  • Runtime
  • Specification
  • Versions
  • Compare
  • Similar models
  • About
  • FAQ

F2LLM-v2-330M Capability Profile

This profile uses the model's current scored version. Arena ratings and prices are shown separately.

F2LLM-v2-330M LLMBoard score breakdown

F2LLM-v2-330M Benchmark Results

Benchmark scores for F2LLM-v2-330M.

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Benchmark
Score
Rank
Participants
Percentile
Evidence
Evaluated
BenchmarkMTEB(eng, v2) ClusteringScore56.5%Rank27Participants213PercentileN/AEvidenceAEvaluatedAug 18, 2026
BenchmarkMTEB(eng, v2) ClassificationScore86.9%Rank38Participants253PercentileN/AEvidenceAEvaluatedAug 18, 2026
BenchmarkMTEB(eng, v2) Semantic SimilarityScore82.8%Rank57Participants207PercentileN/AEvidenceAEvaluatedAug 18, 2026
BenchmarkMTEB(eng, v2) RetrievalScore53.3%Rank77Participants208PercentileN/AEvidenceAEvaluatedAug 18, 2026
BenchmarkMTEB(eng, v2) RerankingScore47.0%Rank101Participants237PercentileN/AEvidenceAEvaluatedAug 18, 2026

F2LLM-v2-330M Arena Results

Preference and agent-evaluation results for the default version.

No Arena results

The default version does not have a matching Arena result yet.

F2LLM-v2-330M Pricing

Official vendor API pricing appears first, followed by individual provider offers.

Official API
N/A
Official provider
N/A
Lowest third-party
N/A
Tracked offerings
0
No provider prices

The default version has no current input or output token prices.

Official prices use only the vendor's configured official Provider and positive standard USD PAYG rates. Third-party offers remain explicitly labeled.

F2LLM-v2-330M Runtime Performance

Provider-specific output speed and catalog latency for F2LLM-v2-330M. Runtime does not affect the capability score.

No runtime data

No provider-specific speed or latency record is linked to the default version yet.

Browse runtime rankings

Output Speed is generated output tokens received per second. Catalog latency is reported separately from observed provider TTFT.

F2LLM-v2-330M Specifications

Technical details for the model's default version.

Version
F2LLM-v2-330M
Released
Mar 9, 2026
Knowledge cutoff
Unknown
Parameters
334M
Context window
41K
Max output
N/A
Inputs
text
Outputs
embedding
Open weights
Yes
License
apache-2.0

F2LLM-v2-330M Versions

Available versions of this model. The score column identifies the version used in the overall ranking.

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Version
Released
LLMBoard
Parameters
Context
Max output
Open weights
License
VersionF2LLM-v2-330MReleasedMar 9, 2026LLMBoard68.7Parameters334MContext41KMax outputN/AOpen weightsYesLicenseapache-2.0

F2LLM-v2-330M vs nearby models

Open a comparison with the three ranked models immediately above and below this model.

F2LLM-v2-330MvsGIST-large-Embedding-v0F2LLM-v2-330Mvsjina-embeddings-v3F2LLM-v2-330MvsNano-Em1-0.6B-v2.1F2LLM-v2-330Mvsmxbai-embed-large-v1F2LLM-v2-330MvsEVA02-CLIP-bigE-14-plusF2LLM-v2-330MvsCohere-embed-multilingual-v3.0

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What is F2LLM-v2-330M?

Key information about F2LLM-v2-330M and its available data.

Ranked model from MTEB.

Data as of 2026-08-17.

FAQ

Common questions about F2LLM-v2-330M.

When was F2LLM-v2-330M released?

F2LLM-v2-330M's default version was released on Mar 9, 2026.

How much does F2LLM-v2-330M cost?

No official standard PAYG price is currently available for F2LLM-v2-330M.

Who created F2LLM-v2-330M?

F2LLM-v2-330M was created by Codefuse Ai.

What is the context window for F2LLM-v2-330M?

The default version has a 41K token context window.

Is F2LLM-v2-330M open weight?

Yes. The default version is marked as open weight under apache-2.0.

How many API providers offer F2LLM-v2-330M?

No provider offering is currently linked to the default version.

What models should I compare F2LLM-v2-330M with?

Nearby ranked alternatives include GIST-large-Embedding-v0, jina-embeddings-v3, Nano-Em1-0.6B-v2.1.