Agentic
Like-for-like- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Qwen3.6 Plus
- 36.4
- Supported · #128/151
- Basis
- BenchAlign lane · 7 vs 13 public rows
- Reading
- Ling 3.0 Flash leads · intervals overlap
Ling 3.0 Flash vs Qwen3.6 Plus
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Agentic work
Tool use, computer use, and multi-step task completion
Ling 3.0 Flash
Ling 3.0 Flash leads on the public agentic lane, 40 to 36.4, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.6 Plus
Qwen3.6 Plus 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
Ling 3.0 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
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
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.
8 Ling 3.0 Flash only
14 Shared
34 Qwen3.6 Plus only
- Shared results
- 14
- Ling 3.0 Flash only
- 8
- Qwen3.6 Plus only
- 34
- Like-for-like categories
- 3 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Like-for-like- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Qwen3.6 Plus
- 54.7
- Supported · #63/181
- Basis
- BenchAlign lane · 5 vs 8 public rows
- Reading
- Qwen3.6 Plus leads · intervals overlap
Instruction following
Like-for-like- Ling 3.0 Flash
- 75.6
- #58/120
- Qwen3.6 Plus
- 85.5
- #41/120
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Qwen3.6 Plus leads
Coding
Directional only- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Qwen3.6 Plus
- 47.0
- Supported · #92/183
- Basis
- BenchAlign lane · 6 vs 7 public rows
- Reading
- Directional only
Reasoning
Not comparable- Ling 3.0 Flash
- 69.2
- Unranked · 2 rankable rows
- Qwen3.6 Plus
- 59.3
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Qwen3.6 Plus
- 62.4
- #4/7
- Basis
- Provisional lane · 2 vs 4 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.0 Flash
- Not ranked
- Qwen3.6 Plus
- 69.7
- #4/12
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.0 Flash
- Not ranked
- Qwen3.6 Plus
- 66.3
- #22/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Ling 3.0 Flash | Qwen3.6 Plus | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.0Supported · #121/151 | 36.4Supported · #128/151 | Like-for-likeBenchAlign lane · 7 vs 13 public rows | Ling 3.0 Flash leads · intervals overlap |
| Knowledge | 45.9Supported · #112/181 | 54.7Supported · #63/181 | Like-for-likeBenchAlign lane · 5 vs 8 public rows | Qwen3.6 Plus leads · intervals overlap |
| Instruction following | 75.6#58/120 | 85.5#41/120 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.6 Plus leads |
| Coding | 42.8Estimated · #126/183 | 47.0Supported · #92/183 | Directional onlyBenchAlign lane · 6 vs 7 public rows | Directional only |
| Reasoning | 69.2Unranked · 2 rankable rows | 59.3Unranked · 4 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 73.7Unranked · 3 rankable rows | 62.4#4/7 | Not comparableProvisional lane · 2 vs 4 weighted rows | Not comparable |
| Multilingual | Not ranked | 69.7#4/12 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 66.3#22/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | 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.
HLE
Knowledge
MMLU-Pro (Vals)
Knowledge
GPQA
Knowledge
AIME26
Math
LiveCodeBench (Vals)
Coding
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
- Qwen3.6 Plus
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate. Qwen3.6 Plus 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
- Qwen3.6 Plus
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate. Qwen3.6 Plus 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
- Qwen3.6 Plus
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Ling 3.0 Flash has no comparable published API token rate. Qwen3.6 Plus 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 FlashQwen3.6 Plus
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.0 Flash
Qwen3.6 Plus
1M
API model ID
Ling 3.0 Flash
Not sourced
Qwen3.6 Plus
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 cardQwen3.6 Plus
No comparable hosted API rate
Documented inputs
Ling 3.0 Flash
Not sourced
Qwen3.6 Plus
Not sourced
Documented outputs
Ling 3.0 Flash
Not sourced
Qwen3.6 Plus
Not sourced
Provider availability
Ling 3.0 Flash
Not sourced
Qwen3.6 Plus
Not sourced
Reasoning profile
Ling 3.0 Flash
Reasoning
Qwen3.6 Plus
Reasoning
Weight access
Ling 3.0 Flash
Open Weight
Qwen3.6 Plus
Proprietary
License
Ling 3.0 Flash
Open Weight
Qwen3.6 Plus
Proprietary
Release date
Ling 3.0 Flash
2026-07-23
Qwen3.6 Plus
2026-04-02
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
- Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.2, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Qwen3.6 Plus has the larger documented window (1M).
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 evidence56 rows
Agentic
MCP Atlas
Ling 3.0 Flash65.5%SourceQwen3.6 Plus48.2%SourceLing 3.0 Flash leads this result
skillsBench
Ling 3.0 Flash44.8%SourceQwen3.6 Plus—Not directly comparable
BFCL v4
Ling 3.0 Flash73.0%SourceQwen3.6 Plus—Not directly comparable
WideResearch
Ling 3.0 Flash73.6%SourceQwen3.6 Plus74.3%SourceQwen3.6 Plus leads this result
BrowseComp
Ling 3.0 Flash72.2%SourceQwen3.6 Plus—Not directly comparable
DRACO
Ling 3.0 Flash70.4%SourceQwen3.6 Plus—Not directly comparable
Terminal-Bench 2.1 (Vals)
Ling 3.0 Flash50.2%SourceQwen3.6 Plus53.2%SourceQwen3.6 Plus leads this result
Terminal-Bench 2.0
Ling 3.0 Flash—Qwen3.6 Plus61.6%SourceNot directly comparable
Claw-Eval
Ling 3.0 Flash—Qwen3.6 Plus58.8%SourceNot directly comparable
QwenClawBench
Ling 3.0 Flash—Qwen3.6 Plus57.2%SourceNot directly comparable
τ³-bench results
Ling 3.0 Flash—Qwen3.6 Plus70.7%SourceNot directly comparable
VITA-Bench
Ling 3.0 Flash—Qwen3.6 Plus44.3%SourceNot directly comparable
DeepPlanning
Ling 3.0 Flash—Qwen3.6 Plus41.5%SourceNot directly comparable
Toolathlon
Ling 3.0 Flash—Qwen3.6 Plus39.8%SourceNot directly comparable
MCP-Tasks
Ling 3.0 Flash—Qwen3.6 Plus74.1%SourceNot directly comparable
Gert Labs
Ling 3.0 Flash—Qwen3.6 Plus50.60%SourceNot directly comparable
ResearchClawBench
Ling 3.0 Flash—Qwen3.6 Plus18.0%SourceNot directly comparable
Coding
SWE-bench Pro
Ling 3.0 Flash56.6%SourceQwen3.6 Plus56.6%SourceTie
SWE Multilingual
Ling 3.0 Flash72.4%SourceQwen3.6 Plus73.8%SourceQwen3.6 Plus leads this result
LiveCodeBench v5
Ling 3.0 Flash82.8%SourceQwen3.6 Plus—Not directly comparable
SciCode
Ling 3.0 Flash41.2%SourceQwen3.6 Plus—Not directly comparable
LiveCodeBench (Vals)
Ling 3.0 Flash84.0%SourceQwen3.6 Plus86.0%SourceQwen3.6 Plus leads this result
SWE-bench (Vals)
Ling 3.0 Flash65.2%SourceQwen3.6 Plus73.4%SourceQwen3.6 Plus leads this result
SWE-bench Verified
Ling 3.0 Flash—Qwen3.6 Plus78.8%SourceNot directly comparable
LiveCodeBench v6
Ling 3.0 Flash—Qwen3.6 Plus87.1%SourceNot directly comparable
Vibe Code Bench
Ling 3.0 Flash—Qwen3.6 Plus25.56%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Ling 3.0 Flash85.0%SourceQwen3.6 Plus90.4%SourceQwen3.6 Plus leads this result
GPQA-D
Ling 3.0 Flash85.0%SourceQwen3.6 Plus—Not directly comparable
HLE
Ling 3.0 Flash22.7%SourceQwen3.6 Plus28.8%SourceQwen3.6 Plus leads this result
GPQA Diamond (Vals)
Ling 3.0 Flash84.8%SourceQwen3.6 Plus87.4%SourceQwen3.6 Plus leads this result
MMLU-Pro (Vals)
Ling 3.0 Flash82.0%SourceQwen3.6 Plus87.7%SourceQwen3.6 Plus leads this result
SuperGPQA
Ling 3.0 Flash—Qwen3.6 Plus71.6%SourceNot directly comparable
MMLU-Pro
Ling 3.0 Flash—Qwen3.6 Plus88.5%SourceNot directly comparable
MMLU-Redux
Ling 3.0 Flash—Qwen3.6 Plus94.5%SourceNot directly comparable
C-Eval
Ling 3.0 Flash—Qwen3.6 Plus93.3%SourceNot directly comparable
Math
AIME26
Ling 3.0 Flash93.2%SourceQwen3.6 Plus95.3%SourceQwen3.6 Plus leads this result
HMMT Feb 2026
Ling 3.0 Flash87.0%SourceQwen3.6 Plus87.8%SourceQwen3.6 Plus leads this result
IMOAnswerBench
Ling 3.0 Flash83.7%SourceQwen3.6 Plus—Not directly comparable
HMMT Feb 2025
Ling 3.0 Flash—Qwen3.6 Plus96.7%SourceNot directly comparable
HMMT Nov 2025
Ling 3.0 Flash—Qwen3.6 Plus94.6%SourceNot directly comparable
MMAnswerBench
Ling 3.0 Flash—Qwen3.6 Plus83.8%SourceNot directly comparable
FrontierMath v2 (Tiers 1-3)
Ling 3.0 Flash—Qwen3.6 Plus26.207%SourceNot directly comparable
FrontierMath v2 (Tier 4)
Ling 3.0 Flash—Qwen3.6 Plus8.333%SourceNot directly comparable
Multilingual
Multimodal
MMMU
Ling 3.0 Flash—Qwen3.6 Plus86.0%SourceNot directly comparable
MMMU-Pro
Ling 3.0 Flash—Qwen3.6 Plus78.8%SourceNot directly comparable
MathVision
Ling 3.0 Flash—Qwen3.6 Plus88.0%SourceNot directly comparable
VideoMMMU
Ling 3.0 Flash—Qwen3.6 Plus84.0%SourceNot directly comparable
ScreenSpot Pro
Ling 3.0 Flash—Qwen3.6 Plus68.2%SourceNot directly comparable
CharXiv
Ling 3.0 Flash—Qwen3.6 Plus81.5%SourceNot directly comparable
V*
Ling 3.0 Flash—Qwen3.6 Plus96.9%SourceNot directly comparable
Instruction following
Frequently asked questions
Which is better, Ling 3.0 Flash or Qwen3.6 Plus?
Qwen3.6 Plus has the higher public score estimate, 61.49 versus 52.2, 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 Qwen3.6 Plus?
Qwen3.6 Plus scores higher for coding on the public lane, 47 to 42.8. Ling 3.0 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; 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 Qwen3.6 Plus?
Ling 3.0 Flash leads the public agentic tasks lane, 40 to 36.4, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Ling 3.0 Flash or Qwen3.6 Plus?
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 Qwen3.6 Plus?
Qwen3.6 Plus has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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