Ling 3.0 Flash vs Qwen3.8-27B

Updated September 28, 2026. Rank says Qwen3.8-27B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.36, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Ling 3.0 Flash

InclusionAI

45.36/100

Estimated · Public rank #101

90% interval 33.8–56.9

Model B

Qwen3.8-27B

Alibaba

55.26/100

Estimated · Public rank #58

90% interval 47.7–62.8

Shared results: 10
Ling 3.0 Flash only: 12
Qwen3.8-27B only: 23
Like-for-like categories: 4 / 8

Estimated: Ling 3.0 Flash and Qwen3.8-27BHow the comparison works

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.

Coding work
Code generation, repair, and software-engineering tasks
Qwen3.8-27B
Qwen3.8-27B leads on the public coding lane, 48.7 to 34.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Qwen3.8-27B
Qwen3.8-27B leads on the public agentic lane, 61.2 to 33.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger

Show secondary and unsupported calls

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.

Pick the job you would use them for

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

34.8Ling 3.0 Flash48.7Qwen3.8-27B

Like-for-like · BenchAlign v5.7

Qwen3.8-27B leads the like-for-like coding row, although the 90% intervals overlap.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

HLEKnowledge
Normalized gap 8.1
Ling 3.0 Flash:22.7%
Qwen3.8-27B:30.8%
Ling 3.0 Flash source Qwen3.8-27B source
SWE-bench ProCoding
Normalized gap 5.1
Ling 3.0 Flash:56.6%
Qwen3.8-27B:61.7%
Ling 3.0 Flash source Qwen3.8-27B source
IFBenchInstruction following
Normalized gap 5.0
Ling 3.0 Flash:74.5%
Qwen3.8-27B:79.5%
Ling 3.0 Flash source Qwen3.8-27B source
GPQAKnowledge
Normalized gap 4.2
Ling 3.0 Flash:85.0%
Qwen3.8-27B:89.2%
Ling 3.0 Flash source Qwen3.8-27B source
MMLU-Pro (Vals)Knowledge
Normalized gap 2.3
Ling 3.0 Flash:82.0%
Qwen3.8-27B:84.3%
Ling 3.0 Flash source Qwen3.8-27B source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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.

Agentic

Like-for-like

Ling 3.0 Flash: 33.2; Supported · #73/117
Qwen3.8-27B: 61.2; Supported · #18/117
Basis: BenchAlign v5.7 lane · 7 vs 8 public rows
Reading: Qwen3.8-27B leads

Coding

Like-for-like

Ling 3.0 Flash: 34.8; Supported · #80/142
Qwen3.8-27B: 48.7; Supported · #45/142
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: Qwen3.8-27B leads · intervals overlap

Knowledge

Like-for-like

Ling 3.0 Flash: 43.3; Supported · #83/168
Qwen3.8-27B: 49.2; Supported · #63/168
Basis: BenchAlign v5.7 lane · 5 vs 6 public rows
Reading: Qwen3.8-27B leads · intervals overlap

Instruction following

Like-for-like

Ling 3.0 Flash: 72.1; #63/124
Qwen3.8-27B: 83.2; #45/124
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.8-27B leads

Reasoning

Not comparable

Ling 3.0 Flash: 72.5; Unranked · 2 rankable rows
Qwen3.8-27B: 78.7; #8/27
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Ling 3.0 Flash: Not ranked
Qwen3.8-27B: 80.9; #11/50
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Ling 3.0 Flash: Not ranked
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Ling 3.0 Flash: 72.9; Unranked · 3 rankable rows
Qwen3.8-27B: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Ling 3.0 Flash and Qwen3.8-27B
Category	Ling 3.0 Flash	Qwen3.8-27B	Basis	Reading
Agentic	33.2Supported · #73/117	61.2Supported · #18/117	Like-for-likeBenchAlign v5.7 lane · 7 vs 8 public rows	Qwen3.8-27B leads
Coding	34.8Supported · #80/142	48.7Supported · #45/142	Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows	Qwen3.8-27B leads · intervals overlap
Knowledge	43.3Supported · #83/168	49.2Supported · #63/168	Like-for-likeBenchAlign v5.7 lane · 5 vs 6 public rows	Qwen3.8-27B leads · intervals overlap
Instruction following	72.1#63/124	83.2#45/124	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.8-27B leads
Reasoning	72.5Unranked · 2 rankable rows	78.7#8/27	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	80.9#11/50	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	72.9Unranked · 3 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-27B 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
Qwen3.8-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Ling 3.0 Flash has no comparable published API token rate. Qwen3.8-27B has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationLing 3.0 FlashQwen3.8-27B

Context window

Maximum documented context; output-token limits may be lower.

Ling 3.0 Flash

262K

InclusionAI Ling 3.0 Flash model card

Qwen3.8-27B

262K

Qwen3.8-27B model card

API model ID

Ling 3.0 Flash

Not sourced

Qwen3.8-27B

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 card

Qwen3.8-27B

No comparable hosted API rate

Qwen3.8-27B model card

Documented inputs

Ling 3.0 Flash

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Ling 3.0 Flash

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Ling 3.0 Flash

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Ling 3.0 Flash

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Ling 3.0 Flash

Open Weight

Qwen3.8-27B

Open Weight

License

Ling 3.0 Flash

Open Weight

Qwen3.8-27B

Open Weight

Release date

Ling 3.0 Flash

2026-07-23

Qwen3.8-27B

2026-08-05

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: Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.36, 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.

Questions

Which is better, Ling 3.0 Flash or Qwen3.8-27B?

Qwen3.8-27B has the higher public score estimate, 55.26 versus 45.36, 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 Qwen3.8-27B?

Qwen3.8-27B leads the public coding lane, 48.7 to 34.8, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Ling 3.0 Flash or Qwen3.8-27B?

Qwen3.8-27B leads the public agentic tasks lane, 61.2 to 33.2, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Ling 3.0 Flash or Qwen3.8-27B?

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 Qwen3.8-27B?

Both models list the same context window, 262K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence45 rows

Agentic

MCP Atlas
Ling 3.0 Flash65.5%
Source
Qwen3.8-27B—
Not directly comparable
skillsBench
Ling 3.0 Flash44.8%
Source
Qwen3.8-27B—
Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%
Source
Qwen3.8-27B—
Not directly comparable
WideResearch
Ling 3.0 Flash73.6%
Source
Qwen3.8-27B—
Not directly comparable
BrowseComp
Ling 3.0 Flash72.2%
Source
Qwen3.8-27B—
Not directly comparable
DRACO
Ling 3.0 Flash70.4%
Source
Qwen3.8-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.0 Flash50.2%
Source
Qwen3.8-27B58.4%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.1
Ling 3.0 Flash—
Qwen3.8-27B73.0%
Source
Not directly comparable
CoWorkBench
Ling 3.0 Flash—
Qwen3.8-27B70.7%
Source
Not directly comparable
JobBench
Ling 3.0 Flash—
Qwen3.8-27B33.4%
Source
Not directly comparable
Agents' Last Exam
Ling 3.0 Flash—
Qwen3.8-27B42.9%
Source
Not directly comparable
OSWorld-Verified
Ling 3.0 Flash—
Qwen3.8-27B84.3%
Source
Not directly comparable
WebArena-Verified
Ling 3.0 Flash—
Qwen3.8-27B64.8%
Source
Not directly comparable
AndroidWorld
Ling 3.0 Flash—
Qwen3.8-27B81.9%
Source
Not directly comparable

Coding

SWE-bench Pro
Ling 3.0 Flash56.6%
Source
Qwen3.8-27B61.7%
Source
Qwen3.8-27B leads this result
SWE Multilingual
Ling 3.0 Flash72.4%
Source
Qwen3.8-27B—
Not directly comparable
LiveCodeBench v5
Ling 3.0 Flash82.8%
Source
Qwen3.8-27B—
Not directly comparable
SciCode
Ling 3.0 Flash41.2%
Source
Qwen3.8-27B—
Not directly comparable
LiveCodeBench (Vals)
Ling 3.0 Flash84.0%
Source
Qwen3.8-27B84.0%
Source
Tie
SWE-bench (Vals)
Ling 3.0 Flash65.2%
Source
Qwen3.8-27B86.0%
Source
Qwen3.8-27B leads this result
Terminal-Bench 2.1
Ling 3.0 Flash—
Qwen3.8-27B73.0%
Source
Not directly comparable
NL2Repo
Ling 3.0 Flash—
Qwen3.8-27B42.3%
Source
Not directly comparable
DeepSWE
Ling 3.0 Flash—
Qwen3.8-27B42.2%
Source
Not directly comparable
LiveCodeBench v6
Ling 3.0 Flash—
Qwen3.8-27B90.3%
Source
Not directly comparable
VulcanBench v3
Ling 3.0 Flash—
Qwen3.8-27B82.6%
Source
Not directly comparable

Multimodal

MathVision
Ling 3.0 Flash—
Qwen3.8-27B90.0%
Source
Not directly comparable
MathVision w/ Python
Ling 3.0 Flash—
Qwen3.8-27B94.6%
Source
Not directly comparable
BabyVision
Ling 3.0 Flash—
Qwen3.8-27B65.7%
Source
Not directly comparable
BabyVision w/ Python
Ling 3.0 Flash—
Qwen3.8-27B85.6%
Source
Not directly comparable
Vision2Web
Ling 3.0 Flash—
Qwen3.8-27B62.9%
Source
Not directly comparable
CharXiv w/o tools
Ling 3.0 Flash—
Qwen3.8-27B83.7%
Source
Not directly comparable
CharXiv
Ling 3.0 Flash—
Qwen3.8-27B90.2%
Source
Not directly comparable
OmniDocBench 1.5
Ling 3.0 Flash—
Qwen3.8-27B91.1%
Source
Not directly comparable
RealWorldQA
Ling 3.0 Flash—
Qwen3.8-27B85.9%
Source
Not directly comparable
ERQA
Ling 3.0 Flash—
Qwen3.8-27B65.5%
Source
Not directly comparable

Knowledge

GPQA
Ling 3.0 Flash85.0%
Source
Qwen3.8-27B89.2%
Source
Qwen3.8-27B leads this result
GPQA-D
Ling 3.0 Flash85.0%
Source
Qwen3.8-27B89.2%
Source
Qwen3.8-27B leads this result
HLE
Ling 3.0 Flash22.7%
Source
Qwen3.8-27B30.8%
Source
Qwen3.8-27B leads this result
GPQA Diamond (Vals)
Ling 3.0 Flash84.8%
Source
Qwen3.8-27B88.9%
Source
Qwen3.8-27B leads this result
MMLU-Pro (Vals)
Ling 3.0 Flash82.0%
Source
Qwen3.8-27B84.3%
Source
Qwen3.8-27B leads this result
HLE w/o tools
Ling 3.0 Flash—
Qwen3.8-27B30.8%
Source
Not directly comparable

Instruction following

IFBench
Ling 3.0 Flash74.5%
Source
Qwen3.8-27B79.5%
Source
Qwen3.8-27B leads this result

Math

AIME26
Ling 3.0 Flash93.2%
Source
Qwen3.8-27B—
Not directly comparable
HMMT Feb 2026
Ling 3.0 Flash87.0%
Source
Qwen3.8-27B—
Not directly comparable
IMOAnswerBench
Ling 3.0 Flash83.7%
Source
Qwen3.8-27B—
Not directly comparable

45 public results · 10 shared

Related comparisons

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Last updated September 28, 2026

Ling 3.0 Flash vs Qwen3.8-27B

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Ling 3.0 Flash or Qwen3.8-27B?

Which is better for coding, Ling 3.0 Flash or Qwen3.8-27B?

Which is better for agentic tasks, Ling 3.0 Flash or Qwen3.8-27B?

Which costs less, Ling 3.0 Flash or Qwen3.8-27B?

Which has the larger context window, Ling 3.0 Flash or Qwen3.8-27B?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch Ling 3.0 Flash vs Qwen3.8-27B