Knowledge
Like-for-like- Ling 3.0 Flash
- 31.1
- Qwen3.8-Flash-Next
- 43.4
- Weighted basis
- 2 vs 2 rows
- Reading
- Qwen3.8-Flash-Next leads
Ling 3.0 Flash vs Qwen3.8-Flash-Next
Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 53.77, 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.
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.
11 Ling 3.0 Flash only
6 Shared
18 Qwen3.8-Flash-Next only
- Shared results
- 6
- Ling 3.0 Flash only
- 11
- Qwen3.8-Flash-Next only
- 18
- Like-for-like categories
- 2 / 8
1 category uses 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- Ling 3.0 Flash
- 74.5
- Qwen3.8-Flash-Next
- 81.3
- Weighted basis
- 1 vs 1 rows
- Reading
- Qwen3.8-Flash-Next leads
Coding
Directional only- Ling 3.0 Flash
- 47.1
- Qwen3.8-Flash-Next
- 62.5
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Agentic
Not comparable- Ling 3.0 Flash
- 72.2
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Ling 3.0 Flash
- Not measured
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash
- 90.1
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash
- Not measured
- Qwen3.8-Flash-Next
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash
- Not measured
- Qwen3.8-Flash-Next
- 90.6
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash | Qwen3.8-Flash-Next | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 31.1 | 43.4 | Like-for-like2 vs 2 rows | Qwen3.8-Flash-Next leads |
| Instruction following | 74.5 | 81.3 | Like-for-like1 vs 1 rows | Qwen3.8-Flash-Next leads |
| Coding | 47.1 | 62.5 | 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 | 90.6 | Not comparable0 vs 1 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.
HLE
Knowledge
Ling 3.0 Flash: 22.7%Qwen3.8-Flash-Next: 35.9%Normalized gap 13.2Ling 3.0 Flash source Qwen3.8-Flash-Next sourceIFBench
Instruction following
Ling 3.0 Flash: 74.5%Qwen3.8-Flash-Next: 81.3%Normalized gap 6.8Ling 3.0 Flash source Qwen3.8-Flash-Next sourceGPQA
Knowledge
Ling 3.0 Flash: 85.0%Qwen3.8-Flash-Next: 91.7%Normalized gap 6.7Ling 3.0 Flash source Qwen3.8-Flash-Next sourceSWE-bench Pro
Coding
Ling 3.0 Flash: 56.6%Qwen3.8-Flash-Next: 62.5%Normalized gap 5.9Ling 3.0 Flash source Qwen3.8-Flash-Next 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
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-Flash-Next 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
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-Flash-Next 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
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-Flash-Next 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 FlashQwen3.8-Flash-Next
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash
Qwen3.8-Flash-Next
API model ID
Ling 3.0 Flash
Not sourced
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardCached-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 cardQwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardDocumented inputs
Ling 3.0 Flash
Not sourced
Qwen3.8-Flash-Next
Not sourced
Documented outputs
Ling 3.0 Flash
Not sourced
Qwen3.8-Flash-Next
Not sourced
Provider availability
Ling 3.0 Flash
Not sourced
Qwen3.8-Flash-Next
Not sourced
Reasoning profile
Ling 3.0 Flash
Reasoning
Qwen3.8-Flash-Next
Reasoning
Weight access
Ling 3.0 Flash
Open Weight
Qwen3.8-Flash-Next
Open Weight
License
Ling 3.0 Flash
Open Weight
Qwen3.8-Flash-Next
Open Weight
Release date
Ling 3.0 Flash
2026-07-23
Qwen3.8-Flash-Next
2026-08-26
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
- Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 53.77, but the 90% score intervals overlap.
- 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 evidence35 rows
Agentic
MCP Atlas
Ling 3.0 Flash65.5%SourceQwen3.8-Flash-Next—Not directly comparable
skillsBench
Ling 3.0 Flash44.8%SourceQwen3.8-Flash-Next—Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%SourceQwen3.8-Flash-Next—Not directly comparable
WideResearch
Ling 3.0 Flash73.6%SourceQwen3.8-Flash-Next—Not directly comparable
BrowseComp
Ling 3.0 Flash72.2%SourceQwen3.8-Flash-Next—Not directly comparable
DRACO
Ling 3.0 Flash70.4%SourceQwen3.8-Flash-Next—Not directly comparable
CoWorkBench
Ling 3.0 Flash—Qwen3.8-Flash-Next73.9%SourceNot directly comparable
JobBench
Ling 3.0 Flash—Qwen3.8-Flash-Next55.7%SourceNot directly comparable
Agents' Last Exam
Ling 3.0 Flash—Qwen3.8-Flash-Next51.2%SourceNot directly comparable
Toolathlon-Verified
Ling 3.0 Flash—Qwen3.8-Flash-Next73.5%SourceNot directly comparable
AndroidWorld
Ling 3.0 Flash—Qwen3.8-Flash-Next84.5%SourceNot directly comparable
OSWorld 2.0
Ling 3.0 Flash—Qwen3.8-Flash-Next19.4%SourceNot directly comparable
Coding
SWE-bench Pro
Ling 3.0 Flash56.6%SourceQwen3.8-Flash-Next62.5%SourceQwen3.8-Flash-Next leads this result
SWE Multilingual
Ling 3.0 Flash72.4%SourceQwen3.8-Flash-Next81%SourceQwen3.8-Flash-Next leads this result
LiveCodeBench v5
Ling 3.0 Flash82.8%SourceQwen3.8-Flash-Next—Not directly comparable
SciCode
Ling 3.0 Flash41.2%SourceQwen3.8-Flash-Next—Not directly comparable
NL2Repo
Ling 3.0 Flash—Qwen3.8-Flash-Next48.1%SourceNot directly comparable
deepSwe
Ling 3.0 Flash—Qwen3.8-Flash-Next58.7%SourceNot directly comparable
LiveCodeBench v6
Ling 3.0 Flash—Qwen3.8-Flash-Next91.9%SourceNot directly comparable
Knowledge
GPQA
Ling 3.0 Flash85.0%SourceQwen3.8-Flash-Next91.7%SourceQwen3.8-Flash-Next leads this result
GPQA-D
Ling 3.0 Flash85.0%SourceQwen3.8-Flash-Next91.7%SourceQwen3.8-Flash-Next leads this result
HLE
Ling 3.0 Flash22.7%SourceQwen3.8-Flash-Next35.9%SourceQwen3.8-Flash-Next leads this result
HLE w/o tools
Ling 3.0 Flash—Qwen3.8-Flash-Next35.9%SourceNot directly comparable
Math
Multimodal
Vision2Web
Ling 3.0 Flash—Qwen3.8-Flash-Next64.0%SourceNot directly comparable
ERQA
Ling 3.0 Flash—Qwen3.8-Flash-Next72.3%SourceNot directly comparable
LVBench
Ling 3.0 Flash—Qwen3.8-Flash-Next76.6%SourceNot directly comparable
RealWorldQA
Ling 3.0 Flash—Qwen3.8-Flash-Next88.5%SourceNot directly comparable
MathVision
Ling 3.0 Flash—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
MathVision w/ Python
Ling 3.0 Flash—Qwen3.8-Flash-Next95.7%SourceNot directly comparable
CharXiv w/o tools
Ling 3.0 Flash—Qwen3.8-Flash-Next84.6%SourceNot directly comparable
CharXiv
Ling 3.0 Flash—Qwen3.8-Flash-Next90.6%SourceNot directly comparable
Instruction following
Frequently asked questions
Which is better, Ling 3.0 Flash or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next has the higher public score estimate, 67.54 versus 53.77, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Ling 3.0 Flash or Qwen3.8-Flash-Next?
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 Qwen3.8-Flash-Next?
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 Qwen3.8-Flash-Next?
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 Qwen3.8-Flash-Next?
Both models list the same context window, 262K.
Related comparisons
Last updated August 26, 2026
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