Inference
EN
Alibaba

Qwen3.8 Max (0902)

Qwen3.8 Max (0902) is a model from Alibaba. 10 benchmarks count toward its index, in 5 categories.

  • Input: text · image · video
  • Released 2026-09
RankedEnough results for an index.
IndexAll categories, out of 100.63.4±7.1Median 51.0
CoverageIndex weight that has results.80%
SpeedOutput tokens per second.33/sMedian 67/s
First tokenSeconds to the first output token.3.2 sMedian 1.3 s
Input / 1MUS dollars per 1M input tokens.$2.00batch $1.15Median $0.60
Output / 1MUS dollars per 1M output tokens.$6.00batch $3.45 Batch price per 1M output tokens.Median $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-max-0902
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.
$0.25
WeightsLink to the published weights.
—
RetiresDate OpenRouter removes the model.
—

Where to run

APIs that serve this model.
1 provider
ProviderTypeMaker, cloud, host or router.InputOutputContext
OpenRouterRouter$2.00$6.001M

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 counted66.439 / 218
CodingCode writing and repair.1 counted70.5—
ReasoningLogic problems and puzzles.2 counted59.071 / 227
MultimodalTasks with images and text.None counted——
KnowledgeFacts and expert knowledge.2 counted62.652 / 268
MultilingualTasks in many languages.None counted——
InstructionTasks with strict prompt rules.None counted——
MathMath problems.3 counted64.831 / 168

Results

10 counted
BenchmarkThe benchmark name.ResultThe score from the source.PlacePlace among results on this benchmark.
OTIS Mock AIME 2024-2025Math100.0%1 / 200
GPQA diamond · Epoch AIKnowledge92.3% ±1.728 / 217
SimpleQA VerifiedKnowledge47.3% ±1.636 / 91
FrontierMath-Tiers-1-3-v2-PrivateMath65.6% ±2.837 / 90
FrontierMath-Tier-4-v2-PrivateMath34.1% ±7.530 / 72
Chess PuzzlesReasoning37.0% ±4.939 / 90
MirrorCodeCoding47.8% ±10.319 / 39
Terminal-Bench 4.0.0Agentic27.0% ±1.913 / 23
Terminal-Bench 4.1.0Agentic27.0% ±2.013 / 23
Furniture AssemblyReasoning20.0% ±5.237 / 37

Sources

BenchLMCC BY-NC 4.0OpenRouterTerms restrict reuseEpoch AICC BYTerminal-BenchNo licence statedmodels.devMIT

History

5 releases
Index62.7 → 63.4
23 Oct 202612 Nov 2026
Output / 1M$6.00 → $6.00
23 Oct 202612 Nov 2026

Questions

How much does Qwen3.8 Max (0902) cost?
$2.00 per 1M input tokens, $6.00 per 1M output tokens.
How good is Qwen3.8 Max (0902)?
Its index is 63.4 out of 100. That is place 57 of 283 ranked models.
What is Qwen3.8 Max (0902) best at?
Agentic: 66.4 out of 100, place 39 of 218.
How fast is Qwen3.8 Max (0902)?
33 output tokens per second, 3.2 s to the first token.
What is the context window of Qwen3.8 Max (0902)?
1M tokens.
When was Qwen3.8 Max (0902) released?
September 2026.

Badge

Qwen3.8 Max (0902) on Inference360: place 57 of 283, index 63.4
HTML
<a href="https://inference360.ai/models/alibaba-qwen3-8-max-0902"><img src="https://inference360.ai/badges/alibaba-qwen3-8-max-0902.svg" alt="Qwen3.8 Max (0902) on Inference360: place 57 of 283, index 63.4" height="20"></a>
Markdown
[![Qwen3.8 Max (0902) on Inference360: place 57 of 283, index 63.4](https://inference360.ai/badges/alibaba-qwen3-8-max-0902.svg)](https://inference360.ai/models/alibaba-qwen3-8-max-0902)

Compare with

GLM-5.3-Flash63.1Gemini 3.6 Flash63.7

Lower price

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

More from Alibaba

All models
Qwen3.8 FlashUnrankedQwen3.8-Flash-Next64.3Qwen3.8-Omni-Flash62.7Qwen3.8 Max PrimeUnrankedQwen3.8-27B (Chutes TEE)Unranked