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BenchLM
IBM logo
Model A
Granite 4.2 8B

IBM

46.88/100

Estimated · Public rank #162

90% interval 35.4–58.4

Granite 4.2 8B vs Ling 3.0 Flash

Updated August 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

InclusionAI logo
Model B
Ling 3.0 Flash

InclusionAI

54.33/100

Estimated · Public rank #110

90% interval 42.8–65.8

Decision reading

Ling 3.0 Flash has the higher public score estimate, 54.33 versus 46.88, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 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

    Ling 3.0 Flash

    Ling 3.0 Flash 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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. Granite 4.2 8B does not fit this workload in one request. Granite 4.2 8B has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

    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
6
Granite 4.2 8B only
8
Ling 3.0 Flash only
11
Like-for-like categories
1 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Instruction following

Like-for-like
Granite 4.2 8B
79.3
Ling 3.0 Flash
74.5
Weighted basis
1 vs 1 rows
Reading
Granite 4.2 8B leads

Coding

Directional only
Granite 4.2 8B
36.5
Ling 3.0 Flash
47.1
Weighted basis
3 vs 2 rows
Reading
Directional only

Knowledge

Directional only
Granite 4.2 8B
72.2
Ling 3.0 Flash
31.1
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Granite 4.2 8B
Not measured
Ling 3.0 Flash
72.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Granite 4.2 8B
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Granite 4.2 8B
Not measured
Ling 3.0 Flash
90.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Granite 4.2 8B
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Granite 4.2 8B
Not measured
Ling 3.0 Flash
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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

Granite 4.2 8B
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash
API rate not published
Fits in one request

Granite 4.2 8B has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Granite 4.2 8B
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash
API rate not published
Fits in one request

Granite 4.2 8B has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Granite 4.2 8B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Ling 3.0 Flash
API rate not published
Fits in one request
Cached-input rate unavailable

Granite 4.2 8B does not fit this workload in one request. Granite 4.2 8B has no comparable published API token rate. Ling 3.0 Flash 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.

Documented inputs

Granite 4.2 8B

Not sourced

Ling 3.0 Flash

Not sourced

Documented outputs

Granite 4.2 8B

Not sourced

Ling 3.0 Flash

Not sourced

Provider availability

Granite 4.2 8B

Not sourced

Ling 3.0 Flash

Not sourced

Reasoning profile

Granite 4.2 8B

Reasoning

Ling 3.0 Flash

Reasoning

Weight access

Granite 4.2 8B

Open Weight

Ling 3.0 Flash

Open Weight

License

Granite 4.2 8B

Open Weight

Ling 3.0 Flash

Open Weight

Release date

Granite 4.2 8B

2026-08-25

Ling 3.0 Flash

2026-07-23

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
Ling 3.0 Flash has the higher public score estimate, 54.33 versus 46.88, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Ling 3.0 Flash has the larger documented window (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 evidence25 rows

Agentic

  • Terminal-Bench 2.1

    Granite 4.2 8B20.6%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • τ³-bench results

    Granite 4.2 8B58.1%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • BFCL v4

    Granite 4.2 8B52.4%
    Source
    Ling 3.0 Flash73.0%
    Source

    Ling 3.0 Flash leads this result

  • MCP Atlas

    Granite 4.2 8B—
    Ling 3.0 Flash65.5%
    Source

    Not directly comparable

  • skillsBench

    Granite 4.2 8B—
    Ling 3.0 Flash44.8%
    Source

    Not directly comparable

  • WideResearch

    Granite 4.2 8B—
    Ling 3.0 Flash73.6%
    Source

    Not directly comparable

  • BrowseComp

    Granite 4.2 8B—
    Ling 3.0 Flash72.2%
    Source

    Not directly comparable

  • DRACO

    Granite 4.2 8B—
    Ling 3.0 Flash70.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Granite 4.2 8B47.7%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • SWE-bench Pro

    Granite 4.2 8B19.1%
    Source
    Ling 3.0 Flash56.6%
    Source

    Ling 3.0 Flash leads this result

  • SWE Multilingual

    Granite 4.2 8B30.8%
    Source
    Ling 3.0 Flash72.4%
    Source

    Ling 3.0 Flash leads this result

  • Terminal-Bench 2.1

    Granite 4.2 8B20.6%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • LiveCodeBench v6

    Granite 4.2 8B73.2%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • SciCode

    Granite 4.2 8B36.1%
    Source
    Ling 3.0 Flash41.2%
    Source

    Ling 3.0 Flash leads this result

  • LiveCodeBench v5

    Granite 4.2 8B—
    Ling 3.0 Flash82.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Granite 4.2 8B64.1%
    Source
    Ling 3.0 Flash85.0%
    Source

    Ling 3.0 Flash leads this result

  • MMLU-Pro

    Granite 4.2 8B74.0%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • GPQA-D

    Granite 4.2 8B—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • HLE

    Granite 4.2 8B—
    Ling 3.0 Flash22.7%
    Source

    Not directly comparable

Math

  • AIME 2025

    Granite 4.2 8B86.7%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • HMMT Feb 2025

    Granite 4.2 8B78.3%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • AIME26

    Granite 4.2 8B—
    Ling 3.0 Flash93.2%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Granite 4.2 8B—
    Ling 3.0 Flash87.0%
    Source

    Not directly comparable

  • IMOAnswerBench

    Granite 4.2 8B—
    Ling 3.0 Flash83.7%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Granite 4.2 8B79.3%
    Source
    Ling 3.0 Flash74.5%
    Source

    Granite 4.2 8B leads this result

Frequently asked questions

Which is better, Granite 4.2 8B or Ling 3.0 Flash?

Ling 3.0 Flash has the higher public score estimate, 54.33 versus 46.88, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Granite 4.2 8B or Ling 3.0 Flash?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Granite 4.2 8B or Ling 3.0 Flash?

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, Granite 4.2 8B or Ling 3.0 Flash?

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, Granite 4.2 8B or Ling 3.0 Flash?

Ling 3.0 Flash has the larger documented context window: 262K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 31, 2026

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