Instruction following
Like-for-like- Ling 3.0 Flash
- 74.5
- Ling 3.0 Flash FP8
- 73.4
- Weighted basis
- 1 vs 1 rows
- Reading
- Ling 3.0 Flash leads
Model comparison
Ling 3.0 Flash vs Ling 3.0 Flash FP8
Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
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 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
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
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.
13 Ling 3.0 Flash only
4 Shared
- Shared results
- 4
- Ling 3.0 Flash only
- 13
- Ling 3.0 Flash FP8 only
- 0
- 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.
Coding
Directional only- Ling 3.0 Flash
- 47.1
- Ling 3.0 Flash FP8
- 40.4
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Knowledge
Directional only- Ling 3.0 Flash
- 31.1
- Ling 3.0 Flash FP8
- 84.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Agentic
Not comparable- Ling 3.0 Flash
- 72.2
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash
- Not measured
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash
- 90.1
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash
- Not measured
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash
- Not measured
- Ling 3.0 Flash FP8
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash | Ling 3.0 Flash FP8 | Weighted basis | Reading |
|---|---|---|---|---|
| Instruction following | 74.5 | 73.4 | Like-for-like1 vs 1 rows | Ling 3.0 Flash leads |
| Coding | 47.1 | 40.4 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 31.1 | 84.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | 72.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 90.1 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | 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.
IFBench
Instruction following
Ling 3.0 Flash: 74.5%Ling 3.0 Flash FP8: 73.4%Normalized gap 1.1Ling 3.0 Flash source Ling 3.0 Flash FP8 sourceGPQA
Knowledge
Ling 3.0 Flash: 85.0%Ling 3.0 Flash FP8: 84%Normalized gap 1.0Ling 3.0 Flash source Ling 3.0 Flash FP8 sourceSciCode
Coding
Ling 3.0 Flash: 41.2%Ling 3.0 Flash FP8: 40.4%Normalized gap 0.9Ling 3.0 Flash source Ling 3.0 Flash FP8 source
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.
SpecificationLing 3.0 FlashLing 3.0 Flash FP8
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash
Ling 3.0 Flash FP8
API model ID
Ling 3.0 Flash
Not sourced
Ling 3.0 Flash FP8
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ling 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardLing 3.0 Flash FP8
No comparable hosted API rate
InclusionAI Ling 3.0 Flash FP8 model cardDocumented 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 evidence17 rows
Agentic
MCP Atlas
Ling 3.0 Flash65.5%SourceLing 3.0 Flash FP8—Not directly comparable
skillsBench
Ling 3.0 Flash44.8%SourceLing 3.0 Flash FP8—Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%SourceLing 3.0 Flash FP8—Not directly comparable
WideResearch
Ling 3.0 Flash73.6%SourceLing 3.0 Flash FP8—Not directly comparable
BrowseComp
Ling 3.0 Flash72.2%SourceLing 3.0 Flash FP8—Not directly comparable
DRACO
Ling 3.0 Flash70.4%SourceLing 3.0 Flash FP8—Not directly comparable
Coding
SWE-bench Pro
Ling 3.0 Flash56.6%SourceLing 3.0 Flash FP8—Not directly comparable
SWE Multilingual
Ling 3.0 Flash72.4%SourceLing 3.0 Flash FP8—Not directly comparable
LiveCodeBench v5
Ling 3.0 Flash82.8%SourceLing 3.0 Flash FP8—Not directly comparable
SciCode
Ling 3.0 Flash41.2%SourceLing 3.0 Flash FP840.4%SourceLing 3.0 Flash leads this result
Knowledge
Math
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Frequently asked 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?
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, Ling 3.0 Flash or Ling 3.0 Flash FP8?
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, 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
Last updated August 4, 2026
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