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H Company logo
Model A
Holo3.1-35B-A3B-NVFP4

H Company

Evidence status unavailable

90% interval unavailable

Holo3.1-35B-A3B-NVFP4 vs Ling 3.0 Flash Fin

Updated August 28, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

InclusionAI logo
Model B
Ling 3.0 Flash Fin

InclusionAI

Evidence status unavailable

90% interval unavailable

Decision reading

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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

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

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

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

    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
Holo3.1-35B-A3B-NVFP4 only
0
Ling 3.0 Flash Fin only
3
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
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Holo3.1-35B-A3B-NVFP4
Not measured
Ling 3.0 Flash Fin
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

Holo3.1-35B-A3B-NVFP4
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash Fin
API rate not published
Fits in one request

Holo3.1-35B-A3B-NVFP4 has no comparable published API token rate. Ling 3.0 Flash Fin has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Holo3.1-35B-A3B-NVFP4
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash Fin
API rate not published
Fits in one request

Holo3.1-35B-A3B-NVFP4 has no comparable published API token rate. Ling 3.0 Flash Fin has no comparable published API token rate.

Cache-heavy agent loop

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

Holo3.1-35B-A3B-NVFP4
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Ling 3.0 Flash Fin
API rate not published
Fits in one request
Cached-input rate unavailable

Holo3.1-35B-A3B-NVFP4 has no comparable published API token rate. Ling 3.0 Flash Fin 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.

Holo3.1-35B-A3B-NVFP4

262K

Ling 3.0 Flash Fin

Cached-input rate

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

Holo3.1-35B-A3B-NVFP4

No comparable hosted API rate

Ling 3.0 Flash Fin

No comparable hosted API rate

Vercel AI Gateway availability

Documented inputs

Holo3.1-35B-A3B-NVFP4

Not sourced

Ling 3.0 Flash Fin

Not sourced

Documented outputs

Holo3.1-35B-A3B-NVFP4

Not sourced

Ling 3.0 Flash Fin

Not sourced

Provider availability

Holo3.1-35B-A3B-NVFP4

Not sourced

Ling 3.0 Flash Fin

Not sourced

Reasoning profile

Holo3.1-35B-A3B-NVFP4

Non-Reasoning

Ling 3.0 Flash Fin

Reasoning

Weight access

Holo3.1-35B-A3B-NVFP4

Open Weight

Ling 3.0 Flash Fin

Proprietary

License

Holo3.1-35B-A3B-NVFP4

Open Weight

Ling 3.0 Flash Fin

Proprietary

Release date

Holo3.1-35B-A3B-NVFP4

2026-06-01

Ling 3.0 Flash Fin

2026-08-28

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
Both models list 262K.

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 evidence3 rows

Agentic

  • Finance Agent v2

    Holo3.1-35B-A3B-NVFP4
    Ling 3.0 Flash Fin59.8%
    Source

    Not directly comparable

  • APEX-Agents

    Holo3.1-35B-A3B-NVFP4
    Ling 3.0 Flash Fin29.2%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    Holo3.1-35B-A3B-NVFP4
    Ling 3.0 Flash Fin21.8%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Holo3.1-35B-A3B-NVFP4 or Ling 3.0 Flash Fin?

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, Holo3.1-35B-A3B-NVFP4 or Ling 3.0 Flash Fin?

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, Holo3.1-35B-A3B-NVFP4 or Ling 3.0 Flash Fin?

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, Holo3.1-35B-A3B-NVFP4 or Ling 3.0 Flash Fin?

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, Holo3.1-35B-A3B-NVFP4 or Ling 3.0 Flash Fin?

Both models list the same context window, 262K.

Related comparisons

Last updated August 28, 2026

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