Inference
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Alibaba

Qwen3.8-Omni-Flash

Qwen3.8-Omni-Flash is a reasoning model from Alibaba. 13 benchmarks count toward its index, in 5 categories.

  • Closed weights
  • Reasoning
  • Input: text · image · audio · video
  • Released 2026-09
RankedEnough results for an index.
IndexAll categories, out of 100.62.7±7.5Median 51.0
CoverageIndex weight that has results.70%
SpeedOutput tokens per second.41/sMedian 67/s
First tokenSeconds to the first output token.1.1 sMedian 1.3 s
Input / 1MUS dollars per 1M input tokens.$0.15Median $0.60
Output / 1MUS dollars per 1M output tokens.$0.47Median $2.55
ContextMaximum tokens in one request.1MMedian 262K
EloLMArena rating and rank.—Median 1422

Details

From OpenRouter
API IDThe model ID on OpenRouter.
qwen/qwen3.8-omni-flash
Max outputMaximum output tokens per request.
131K
Knowledge cutoffLast date of training data.
—
ToolsTool calls on OpenRouter.
Yes
JSON outputOutput follows a given JSON schema.
Yes
Cache read / 1MUS dollars per 1M cached tokens.
$0.016
WeightsLink to the published weights.
—
RetiresDate OpenRouter removes the model.
—

Where to run

APIs that serve this model.
2 providers
ProviderTypeMaker, cloud, host or router.InputOutputContext
Alibaba CloudMaker$0.15$0.471M
OpenRouterRouter$0.15$0.471M

US dollars per 1M tokens, list price. From models.dev (opens in a new tab) (MIT).

Capabilities

Index per category, out of 100.
Out of 100
AgenticMulti-step tasks with tools.2 counted64.549 / 218
CodingCode writing and repair.3 counted61.562 / 209
ReasoningLogic problems and puzzles.None counted——
MultimodalTasks with images and text.5 counted64.529 / 84
KnowledgeFacts and expert knowledge.2 counted60.860 / 268
MultilingualTasks in many languages.None counted——
InstructionTasks with strict prompt rules.1 counted65.2—
MathMath problems.None counted——

Results

13 counted
BenchmarkThe benchmark name.ResultThe score from the source.PlacePlace among results on this benchmark.
LiveCodeBench v6Coding92.6%3 / 59
CharXiv ReasoningMultimodal91.4%4 / 49
AndroidWorldAgentic87.1%1 / 10
Instruction Following BenchmarkInstruction81.5%7 / 57
RealWorldQAMultimodal87.7%3 / 18
Graduate-Level Google-Proof Q&AKnowledge91.0%24 / 141
MathVisionMultimodal91.8%5 / 24
SWE-bench ProCoding63.3%21 / 83
GPQA Diamond · BenchLMKnowledge91.0%23 / 84
ERQAMultimodal71.0%5 / 18
NL2RepoCoding48.9%13 / 36
Humanity's Last ExamKnowledge36.5%38 / 77
CharXiv Reasoning without toolsMultimodal83.5%10 / 17
DeepSWEAgentic57.8%45 / 59
MathVision with PythonMultimodal96.2%—
LVBenchMultimodal76.9%—
CoWorkBenchAgentic75.3%—
Vision2WebMultimodal62.9%—

Sources

BenchLMCC BY-NC 4.0OpenRouterTerms restrict reusemodels.devMIT

History

5 releases
Index63.8 → 62.7
23 Oct 202612 Nov 2026
Output / 1M$0.47 → $0.47
23 Oct 202612 Nov 2026

Questions

How much does Qwen3.8-Omni-Flash cost?
$0.15 per 1M input tokens, $0.47 per 1M output tokens.
How good is Qwen3.8-Omni-Flash?
Its index is 62.7 out of 100. That is place 61 of 283 ranked models.
What is Qwen3.8-Omni-Flash best at?
Knowledge: 60.8 out of 100, place 60 of 268.
How fast is Qwen3.8-Omni-Flash?
41 output tokens per second, 1.1 s to the first token.
What is the context window of Qwen3.8-Omni-Flash?
1M tokens.
Is Qwen3.8-Omni-Flash open source?
No. Its weights are not published.
When was Qwen3.8-Omni-Flash released?
September 2026.

Badge

Qwen3.8-Omni-Flash on Inference360: place 61 of 283, index 62.7
HTML
<a href="https://inference360.ai/models/qwen3-8-omni-flash"><img src="https://inference360.ai/badges/qwen3-8-omni-flash.svg" alt="Qwen3.8-Omni-Flash on Inference360: place 61 of 283, index 62.7" height="20"></a>
Markdown
[![Qwen3.8-Omni-Flash on Inference360: place 61 of 283, index 62.7](https://inference360.ai/badges/qwen3-8-omni-flash.svg)](https://inference360.ai/models/qwen3-8-omni-flash)

Compare with

Gemini 3.1 Pro62.3GLM-5.3-Flash63.1Claude Opus 4.7 (Adaptive)63.2dots3-note Preview63.4

Lower price

dots3-note Preview63.4 · FreeGLM-5.3-Flash63.1 · $0.078/1MLing 3.1 Flash67.2 · $0.135/1M

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