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Model comparison

DeepSeek V4 Flash Base vs LFM2.5-Embedding-350M

Updated August 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
DeepSeek V4 Flash Base

DeepSeek

—

Evidence status unavailable

90% interval unavailable

LFM2.5-Embedding-350M

LiquidAI

—

Evidence status unavailable

90% interval unavailable

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    DeepSeek V4 Flash Base

    DeepSeek V4 Flash Base has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2.5-Embedding-350M does not fit this workload in one request. DeepSeek V4 Flash Base has no comparable published API token rate. LFM2.5-Embedding-350M has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LFM2.5-Embedding-350M does not fit this workload in one request. DeepSeek V4 Flash Base has no comparable published API token rate. LFM2.5-Embedding-350M has no comparable published API token rate.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
DeepSeek V4 Flash Base only
24
LFM2.5-Embedding-350M only
2
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Flash Base
44.7
LFM2.5-Embedding-350M
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Flash Base
56.4
LFM2.5-Embedding-350M
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash Base
Not measured
LFM2.5-Embedding-350M
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

DeepSeek V4 Flash Base
API rate not published
Fits in one request
LFM2.5-Embedding-350M
Self-hosted; infrastructure cost varies
Fits in one request

DeepSeek V4 Flash Base has no comparable published API token rate. LFM2.5-Embedding-350M has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Flash Base
API rate not published
Fits in one request
LFM2.5-Embedding-350M
Self-hosted; infrastructure cost varies
Does not fit in one request

LFM2.5-Embedding-350M does not fit this workload in one request. DeepSeek V4 Flash Base has no comparable published API token rate. LFM2.5-Embedding-350M has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V4 Flash Base
API rate not published
Fits in one request
Cached-input rate unavailable
LFM2.5-Embedding-350M
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

LFM2.5-Embedding-350M does not fit this workload in one request. DeepSeek V4 Flash Base has no comparable published API token rate. LFM2.5-Embedding-350M has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V4 Flash Base

1M

DeepSeek V4 Flash model card

LFM2.5-Embedding-350M

32K

API model ID

DeepSeek V4 Flash Base

Not sourced

LFM2.5-Embedding-350M

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V4 Flash Base

No comparable hosted API rate

LFM2.5-Embedding-350M

No comparable hosted API rate

Reasoning profile

DeepSeek V4 Flash Base

Non-Reasoning

LFM2.5-Embedding-350M

Non-Reasoning

Weight access

DeepSeek V4 Flash Base

Open Weight

LFM2.5-Embedding-350M

Open Weight

License

DeepSeek V4 Flash Base

Open Weight

LFM2.5-Embedding-350M

Open Weight

Release date

DeepSeek V4 Flash Base

2026-04-24

LFM2.5-Embedding-350M

2026-06-18

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
DeepSeek V4 Flash Base has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence26 rows

Coding

  • BigCodeBench

    DeepSeek V4 Flash Base56.8%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • HumanEval

    DeepSeek V4 Flash Base69.5%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

Reasoning

  • BBH

    DeepSeek V4 Flash Base86.9%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • DROP

    DeepSeek V4 Flash Base88.6%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • HellaSwag

    DeepSeek V4 Flash Base85.7%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • WinoGrande

    DeepSeek V4 Flash Base79.5%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • CLUEWSC

    DeepSeek V4 Flash Base82.2%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • LongBench v2

    DeepSeek V4 Flash Base44.7%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

Knowledge

  • AGIEval

    DeepSeek V4 Flash Base82.6%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MMLU

    DeepSeek V4 Flash Base88.7%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MMLU-Redux

    DeepSeek V4 Flash Base89.4%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MMLU-Pro

    DeepSeek V4 Flash Base68.3%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MMMLU

    DeepSeek V4 Flash Base88.8%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • C-Eval

    DeepSeek V4 Flash Base92.1%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • CMMLU

    DeepSeek V4 Flash Base90.4%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MultiLoKo

    DeepSeek V4 Flash Base42.2%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Flash Base30.1%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • SuperGPQA

    DeepSeek V4 Flash Base46.5%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • FACTS Parametric

    DeepSeek V4 Flash Base33.9%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • TriviaQA

    DeepSeek V4 Flash Base82.8%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

Math

  • GSM8K

    DeepSeek V4 Flash Base90.8%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • MATH

    DeepSeek V4 Flash Base57.4%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • CMath

    DeepSeek V4 Flash Base93.6%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

Multilingual

  • MGSM

    DeepSeek V4 Flash Base85.7%
    Source
    LFM2.5-Embedding-350M—

    Not directly comparable

  • NanoBEIR Multilingual

    DeepSeek V4 Flash Base—
    LFM2.5-Embedding-350M57.7%
    Source

    Not directly comparable

  • MKQA-11

    DeepSeek V4 Flash Base—
    LFM2.5-Embedding-350M69.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Flash Base or LFM2.5-Embedding-350M?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4 Flash Base or LFM2.5-Embedding-350M?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, DeepSeek V4 Flash Base or LFM2.5-Embedding-350M?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, DeepSeek V4 Flash Base or LFM2.5-Embedding-350M?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V4 Flash Base or LFM2.5-Embedding-350M?

DeepSeek V4 Flash Base has the larger documented context window: 1M, compared with 32K.

Related comparisons

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Last updated August 2, 2026

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