Updated October 10, 2026. Rank says Qwen3.8 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Qwen3.8 Max has the higher public point estimate, 70.66 versus 64.23. Their conditional score ranges overlap. These ranges do not establish rank confidence. 22 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Code generation, repair, and software-engineering tasks
Qwen3.8 Max
Qwen3.8 Max has the higher public coding point estimate, 53.5 to 52.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Qwen3.8 Max
Qwen3.8 Max has the larger documented context window.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Qwen3.8-Flash-Next is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
Like-for-like · BenchAlign v5.8
Qwen3.8 Max has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 7.7HLE w/o toolsKnowledge
Normalized gap 7.7SWE-bench ProCoding
Normalized gap 5.2CharXivMultimodal
Normalized gap 2.9IFBenchInstruction following
Normalized gap 1.5Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Qwen3.8-Flash-Next | Qwen3.8 Max | Basis | Reading |
|---|---|---|---|---|
| Coding | 52.4Supported · #37/146 | 53.5Supported · #34/146 | Like-for-likeBenchAlign v5.8 lane · 5 vs 13 public rows | Qwen3.8 Max leads · intervals overlap |
| Knowledge | 59.4Supported · #47/177 | 66.6Supported · #24/177 | Like-for-likeBenchAlign v5.8 lane · 4 vs 6 public rows | Qwen3.8 Max leads · intervals overlap |
| Instruction following | 91.2#22/127 | 94.7#2/127 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.8 Max leads |
| Agentic | 59.5Estimated · #24/123 | 65.4Supported · #15/123 | Directional onlyBenchAlign v5.8 lane · 6 vs 15 public rows | Directional only |
| Multimodal | 87.7#8/54 | 91.2#4/54 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Reasoning | 80.9Unranked · 2 rankable rows | 87.8Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
Qwen3.8-Flash-Next has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.8-Flash-Next has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.8-Flash-Next has no comparable published API token rate. Qwen3.8 Max has no comparable published API token rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Qwen3.8-Flash-Next
Qwen3.8 Max
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardQwen3.8 Max
qwen3.8-max
Alibaba Cloud Model Studio pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardQwen3.8 Max
No comparable hosted API rate
Alibaba Cloud Model Studio pricingQwen3.8-Flash-Next
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.8-Flash-Next
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.8-Flash-Next
Not sourced
Qwen3.8 Max
Not sourced
Qwen3.8-Flash-Next
Reasoning
Qwen3.8 Max
Reasoning
Qwen3.8-Flash-Next
Open Weight
Qwen3.8 Max
Open Weight
Qwen3.8-Flash-Next
Open Weight
Qwen3.8 Max
Open Weight
Qwen3.8-Flash-Next
2026-08-26
Qwen3.8 Max
2026-08-03
Qwen3.8 Max has the higher public point estimate, 70.66 versus 64.23. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Qwen3.8 Max has the higher public coding point estimate, 53.5 to 52.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Qwen3.8 Max scores higher for agentic tasks on the public lane, 65.4 to 59.5. Qwen3.8-Flash-Next is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Qwen3.8 Max has the larger documented context window: 1M, compared with 262K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
CoWorkBench
Qwen3.8 Max leads this result
JobBench
Qwen3.8-Flash-Next leads this result
Agents' Last Exam
Qwen3.8 Max leads this result
Toolathlon-Verified
Qwen3.8-Flash-Next leads this result
AndroidWorld
Qwen3.8 Max leads this result
OSWorld 2.0
Tie
Terminal-Bench 2.1
Not directly comparable
skillsBench
Not directly comparable
AutomationBench
Not directly comparable
WideResearch
Not directly comparable
HLE w/ tools
Not directly comparable
OSWorld-Verified
Not directly comparable
WebArena-Verified
Not directly comparable
MobileWorld
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Pro
Qwen3.8 Max leads this result
SWE Multilingual
Not directly comparable
NL2Repo
Qwen3.8 Max leads this result
DeepSWE
Qwen3.8-Flash-Next leads this result
LiveCodeBench v6
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
FrontierSWE
Not directly comparable
MLS-Bench Lite
Not directly comparable
PaperBench
Not directly comparable
VulcanBench v3
Not directly comparable
OpenHarmony Bench
Not directly comparable
FrontierSWE v2
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
Bug Hunt Bench
Not directly comparable
Vision2Web
Qwen3.8 Max leads this result
ERQA
Qwen3.8 Max leads this result
LVBench
Qwen3.8 Max leads this result
RealWorldQA
Qwen3.8-Flash-Next leads this result
MathVision
Qwen3.8 Max leads this result
MathVision w/ Python
Qwen3.8 Max leads this result
CharXiv w/o tools
Qwen3.8 Max leads this result
CharXiv
Qwen3.8 Max leads this result
MMMU-Pro
Not directly comparable
BabyVision
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench
Not directly comparable
ZeroBench w/ Python
Not directly comparable
MedXpertQA (MM)
Not directly comparable
ScreenSpot Pro
Not directly comparable
OmniDocBench 1.5
Not directly comparable
OCRBench V2
Not directly comparable
CC-OCR
Not directly comparable
SimpleVQA
Not directly comparable
PerceptionBench
Not directly comparable
Video-MME (with subtitle)
Not directly comparable
VideoMMMU
Not directly comparable
MMVU
Not directly comparable
MLVU (M-Avg)
Not directly comparable
GPQA
Qwen3.8 Max leads this result
GPQA-D
Qwen3.8 Max leads this result
HLE
Qwen3.8 Max leads this result
HLE w/o tools
Qwen3.8 Max leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 10, 2026
