Updated October 10, 2026. Rank says Qwen3.8-Flash-Next 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-Flash-Next has the higher public point estimate, 64.23 versus 58.27. Their conditional score ranges overlap. These ranges do not establish rank confidence. 20 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-Flash-Next
Qwen3.8-Flash-Next has the higher public coding point estimate, 52.4 to 47.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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-Flash-Next 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.
1 category rests 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 5.1HLE w/o toolsKnowledge
Normalized gap 5.1GPQAKnowledge
Normalized gap 2.5IFBenchInstruction following
Normalized gap 1.8SWE-bench ProCoding
Normalized gap 0.8Each 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 | Qwen3.8-27B | Qwen3.8-Flash-Next | Basis | Reading |
|---|---|---|---|---|
| Coding | 47.3Supported · #47/146 | 52.4Supported · #37/146 | Like-for-likeBenchAlign v5.8 lane · 9 vs 5 public rows | Qwen3.8-Flash-Next leads · intervals overlap |
| Multimodal | 84.4#13/54 | 87.7#8/54 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.8-Flash-Next leads |
| Knowledge | 51.0Supported · #72/177 | 59.4Supported · #47/177 | Like-for-likeBenchAlign v5.8 lane · 6 vs 4 public rows | Qwen3.8-Flash-Next leads · intervals overlap |
| Instruction following | 86.9#40/127 | 91.2#22/127 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Qwen3.8-Flash-Next leads |
| Agentic | 61.7Supported · #19/123 | 59.5Estimated · #24/123 | Directional onlyBenchAlign v5.8 lane · 8 vs 6 public rows | Directional only |
| Reasoning | 82.5#8/28 | 80.9Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 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
Qwen3.8-27B has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.
50K fresh input + 3K output tokens
Qwen3.8-27B has no comparable published API token rate. Qwen3.8-Flash-Next has no comparable published API token rate.
200K cached + 20K fresh input + 10K output tokens
Qwen3.8-27B has no comparable published API token rate. Qwen3.8-Flash-Next 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.
Qwen3.8-27B
Qwen3.8-Flash-Next
Qwen3.8-27B
Not sourced
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.8-27B
No comparable hosted API rate
Qwen3.8-27B model cardQwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardQwen3.8-27B
Not sourced
Qwen3.8-Flash-Next
Not sourced
Qwen3.8-27B
Not sourced
Qwen3.8-Flash-Next
Not sourced
Qwen3.8-27B
Not sourced
Qwen3.8-Flash-Next
Not sourced
Qwen3.8-27B
Reasoning
Qwen3.8-Flash-Next
Reasoning
Qwen3.8-27B
Open Weight
Qwen3.8-Flash-Next
Open Weight
Qwen3.8-27B
Open Weight
Qwen3.8-Flash-Next
Open Weight
Qwen3.8-27B
2026-08-05
Qwen3.8-Flash-Next
2026-08-26
Qwen3.8-Flash-Next has the higher public point estimate, 64.23 versus 58.27. 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-Flash-Next has the higher public coding point estimate, 52.4 to 47.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Qwen3.8-27B scores higher for agentic tasks on the public lane, 61.7 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.
Both models list the same context window, 262K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.1
Not directly comparable
CoWorkBench
Qwen3.8-Flash-Next leads this result
JobBench
Qwen3.8-Flash-Next leads this result
Agents' Last Exam
Qwen3.8-Flash-Next leads this result
OSWorld-Verified
Not directly comparable
WebArena-Verified
Not directly comparable
AndroidWorld
Qwen3.8-Flash-Next leads this result
Terminal-Bench 2.1 (Vals)
Not directly comparable
Toolathlon-Verified
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
Qwen3.8-Flash-Next leads this result
NL2Repo
Qwen3.8-Flash-Next leads this result
DeepSWE
Qwen3.8-Flash-Next leads this result
LiveCodeBench v6
Qwen3.8-Flash-Next leads this result
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE Multilingual
Not directly comparable
MathVision
Qwen3.8-Flash-Next leads this result
MathVision w/ Python
Qwen3.8-Flash-Next leads this result
BabyVision
Not directly comparable
BabyVision w/ Python
Not directly comparable
Vision2Web
Qwen3.8-Flash-Next leads this result
CharXiv w/o tools
Qwen3.8-Flash-Next leads this result
CharXiv
Qwen3.8-Flash-Next leads this result
OmniDocBench 1.5
Not directly comparable
RealWorldQA
Qwen3.8-Flash-Next leads this result
ERQA
Qwen3.8-Flash-Next leads this result
LVBench
Not directly comparable
GPQA
Qwen3.8-Flash-Next leads this result
GPQA-D
Qwen3.8-Flash-Next leads this result
HLE
Qwen3.8-Flash-Next leads this result
HLE w/o tools
Qwen3.8-Flash-Next leads this result
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated October 10, 2026
