Skip to main content
Radar

Every change to the models you run, with its source and its date. Releases, price changes, retirements, API changes, and incidents.Every change to the models you run, with its source.

Follow model changes
BenchLM

Ling 3.0 Flash vs Ling 3.0 Flash FP8

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

InclusionAI logo
Model A
Ling 3.0 Flash

InclusionAI

46.83/100

Estimated · Public rank #152

90% interval 35.3–58.3

InclusionAI logo
Model B
Ling 3.0 Flash FP8

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.

Share or export

Share on XLinkedInSocial cardCSVJSON

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

    Ling 3.0 Flash FP8 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Ling 3.0 Flash FP8 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

40.8Ling 3.0 Flash—Ling 3.0 Flash FP8

Not comparable · BenchAlign

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job

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
4
Ling 3.0 Flash only
18
Ling 3.0 Flash FP8 only
0
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Instruction following

Like-for-like
Ling 3.0 Flash
72.1
#63/124
Ling 3.0 Flash FP8
69.7
#65/124
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Ling 3.0 Flash leads

Agentic

Not comparable
Ling 3.0 Flash
42.3
Estimated · #110/154
Ling 3.0 Flash FP8
Not ranked
Basis
BenchAlign lane · 7 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Ling 3.0 Flash
40.8
Supported · #116/154
Ling 3.0 Flash FP8
Not ranked
Basis
BenchAlign lane · 6 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash
71.2
Unranked · 2 rankable rows
Ling 3.0 Flash FP8
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Ling 3.0 Flash
44.6
Supported · #116/184
Ling 3.0 Flash FP8
Not ranked
Basis
BenchAlign lane · 5 vs 2 public rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash
72.9
Unranked · 3 rankable rows
Ling 3.0 Flash FP8
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash
Not ranked
Ling 3.0 Flash FP8
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash
Not ranked
Ling 3.0 Flash FP8
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Ling 3.0 Flash
API rate not published
Fits in one request
Ling 3.0 Flash FP8
API rate not published
Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Ling 3.0 Flash FP8 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.0 Flash
API rate not published
Fits in one request
Ling 3.0 Flash FP8
API rate not published
Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Ling 3.0 Flash FP8 has no comparable published API token rate.

Cache-heavy agent loop

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

Ling 3.0 Flash
API rate not published
Fits in one request
Cached-input rate unavailable
Ling 3.0 Flash FP8
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash has no comparable published API token rate. Ling 3.0 Flash FP8 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.

API model ID

Ling 3.0 Flash

Not sourced

Ling 3.0 Flash FP8

Not sourced

Documented inputs

Ling 3.0 Flash

Not sourced

Ling 3.0 Flash FP8

Not sourced

Documented outputs

Ling 3.0 Flash

Not sourced

Ling 3.0 Flash FP8

Not sourced

Provider availability

Ling 3.0 Flash

Not sourced

Ling 3.0 Flash FP8

Not sourced

Reasoning profile

Ling 3.0 Flash

Reasoning

Ling 3.0 Flash FP8

Reasoning

Weight access

Ling 3.0 Flash

Open Weight

Ling 3.0 Flash FP8

Open Weight

License

Ling 3.0 Flash

Open Weight

Ling 3.0 Flash FP8

Open Weight

Release date

Ling 3.0 Flash

2026-07-23

Ling 3.0 Flash FP8

2026-08-04

If you are choosing between sibling variants
Deployment change
Both entries list InclusionAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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 evidence22 rows

Agentic

  • MCP Atlas

    Ling 3.0 Flash65.5%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • skillsBench

    Ling 3.0 Flash44.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • BFCL v4

    Ling 3.0 Flash73.0%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • WideResearch

    Ling 3.0 Flash73.6%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • BrowseComp

    Ling 3.0 Flash72.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • DRACO

    Ling 3.0 Flash70.4%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Ling 3.0 Flash50.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Coding

  • SWE-bench Pro

    Ling 3.0 Flash56.6%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • SWE Multilingual

    Ling 3.0 Flash72.4%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • LiveCodeBench v5

    Ling 3.0 Flash82.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • SciCode

    Ling 3.0 Flash41.2%
    Source
    Ling 3.0 Flash FP840.4%
    Source

    Ling 3.0 Flash leads this result

  • LiveCodeBench (Vals)

    Ling 3.0 Flash84.0%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • SWE-bench (Vals)

    Ling 3.0 Flash65.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Knowledge

  • GPQA

    Ling 3.0 Flash85.0%
    Source
    Ling 3.0 Flash FP884%
    Source

    Ling 3.0 Flash leads this result

  • GPQA-D

    Ling 3.0 Flash85.0%
    Source
    Ling 3.0 Flash FP884.0%
    Source

    Ling 3.0 Flash leads this result

  • HLE

    Ling 3.0 Flash22.7%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • GPQA Diamond (Vals)

    Ling 3.0 Flash84.8%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • MMLU-Pro (Vals)

    Ling 3.0 Flash82.0%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Math

  • AIME26

    Ling 3.0 Flash93.2%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • HMMT Feb 2026

    Ling 3.0 Flash87.0%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

  • IMOAnswerBench

    Ling 3.0 Flash83.7%
    Source
    Ling 3.0 Flash FP8—

    Not directly comparable

Instruction following

  • IFBench

    Ling 3.0 Flash74.5%
    Source
    Ling 3.0 Flash FP873.4%
    Source

    Ling 3.0 Flash leads this result

Questions

Which is better, Ling 3.0 Flash or Ling 3.0 Flash FP8?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ling 3.0 Flash or Ling 3.0 Flash FP8?

Ling 3.0 Flash FP8 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ling 3.0 Flash or Ling 3.0 Flash FP8?

Ling 3.0 Flash FP8 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Ling 3.0 Flash or Ling 3.0 Flash FP8?

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, Ling 3.0 Flash or Ling 3.0 Flash FP8?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

Watch Ling 3.0 Flash vs Ling 3.0 Flash FP8

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.